Pregnancy and epilepsy—when you’re managing both

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Pregnancy and epilepsy—when you’re managing both

PRACTICE RECOMMENDATIONS

Use the dose of antiepileptic drug (AED) at which the patient is seizure-free prior to conception as a target level to adjust dosing during pregnancy. C

Avoid switching a pregnant patient to an AED that she has not taken before. C

Start all women who have epilepsy and are of childbearing age on ≥0.4 mg folic acid daily prior to conception. C

Strength of recommendation (SOR)

A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series

About 500,000 women in the United States suffer from epilepsy and are of childbearing age.1 For these patients and their physicians, family planning and pregnancy are complex and fraught with risk.

The dilemma: Infants born to women with epilepsy have a 2- to 3-fold risk of congenital malformations compared with those whose mothers do not have epilepsy, mainly related to exposure to antiepileptic drugs (AEDs).2 Recent studies also suggest that children exposed to AEDs such as valproate, phenobarbital, and phenytoin in utero may have neuro-cognitive deficits, even when there are no major congenital malformations.1,3,4

Yet discontinuing the drugs prior to conception or in early pregnancy is rarely a viable option. In 1 recent prospective study, convulsive seizures during the first trimester (the type and timing of seizure thought to have the most harmful effect on the developing fetus) were associated with malformations in 7.4% of pregnancies.2 Seizures also increase the risk of both fetal and maternal death, although the extent of that risk is not known.5

Ideally, pregnant women with epilepsy should be under the care of both an obstetrician experienced in high-risk pregnancies and a neurologist or an epileptologist. In reality, those who live in areas with limited access to such specialized care or have limited health coverage may be cared for throughout their pregnancy by a family physician. This evidence-based review was developed with this family physician in mind.

Safeguarding mom and fetus starts before pregnancy

Mechanisms by which AEDs cause fetal and embryonic harm remain unclear. Possible causes include drug toxicity, drug-drug interactions, folic acid deficiency, the effect of suboptimally controlled convulsions, genetic predisposition, enhanced apoptotic neurodegeneration, and alterations in thyroid hormone status, among others.6-9 Major congenital malformations may occur in a dose-dependent manner, and physicians should aim to use the most effective AED at the lowest effective dose for women of childbearing age.2

In reviewing antiseizure therapy for such patients, here are some considerations to keep in mind:

Avoid polytherapy, if possible. Taking multiple AEDs may increase the risk of major congenital malformations, especially when valproate is one of the drugs.1 Hence, an attempt should be made to switch women with epilepsy who are of childbearing age to monotherapy. Ideally, this should be done a year before planned conception so that good seizure control can be achieved and documented prior to pregnancy.

Avoid high-risk AEDs. Overall, an increased risk of major congenital malformations has been most convincingly found with valproate and phenobarbital.1 Specific types of malformations have also been linked to certain drugs (TABLE). Cardiac malformations are associated with carbamazepine, phenobarbital, and valproate; spina bifida, hypospadias, porencephaly, and other brain anomalies, as well as limb reduction defects, are associated with valproate, particularly at doses >1100 mg/d.10

Choose newer agents, whenever possible. The risk of malformations with newer AEDs—including gabapentin, lamotrigine, levetiracetam, oxcarbazepine, and topiramate—remains unclear, but preliminary data for monotherapy with these agents suggest a lower teratogenic risk compared with traditional AEDs, such as phenobarbital and valproate.10

Initiate folic acid supplementation. Drug-induced folate deficiency has been proposed as a contributing factor in the teratogenicity of AEDs, so diligence is essential in ensuring that patients who have epilepsy and are of childbearing age take folic acid.11 Studies have demonstrated a significant reduction in spontaneous abortion in women who are receiving AED therapy and taking folic acid supplements, and the benefits of folic acid have been found to be especially notable for women taking valproate.12

Folic acid supplementation, of course, is important for all women of childbearing age. At a dose of 0.8 mg/d, folate has been shown to reduce the risk of neural tube and ventricular septal defects in the general population. The American Academy of Neurology/American Epilepsy Society (AAN/AES) Practice Parameters recommend that all women of child-bearing age taking AEDs also take folate supplements (0.4-4 mg/d).13 An optimal dose has not been determined for this patient population, but we routinely recommend 1 mg/d for women with epilepsy of childbearing age and increase the dose to 4 mg/d after conception.

TABLE
Commonly used antiepileptic drugs: Major teratogenic risks1,10,19

 

 

AED

FDA pregnancy category*

Associated risks

Recommendations for use during pregnancy

Carbamazepine

C

Cardiac malformations

Lower teratogenic potential compared with phenobarbital and valproate

Gabapentin

C

No MCMs associated with monotherapy

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Lamotrigine

C

No distinctive pattern of MCMs

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Levetiracetam

C

Pyloric stenosis (in polytherapy with lamotrigine); spina bifida (in polytherapy with carbamazepine and valproate)

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Oxcarbazepine

C

Urogenital malformations

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Phenobarbital

D

Cardiac malformations
Increases risk of MCMs to at least double that of general population

Best avoided in women of childbearing age

Phenytoin

D

Bradycardia and hypotension; fetal hydantoin syndrome

Best avoided in women of childbearing age

Topiramate

C

Hypospadias; oral clefts

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Valproate

D

Cardiac malformations; hypospadias; limb reduction defects; neural tube defects; porencephaly; spina bifida
Increases risk of MCMs to at least double that of general population

Best avoided in women of childbearing age

AED, antiepileptic drug; FDA, US Food and Drug Administration; MCMs, major congenital malformations.

*Category C indicates that human data are lacking and animal studies were positive OR not done; Category D indicates that human data have shown a teratogenic risk but the benefits may outweigh the risk.

†Traditional AEDs include carbamazepine, phenobarbital, phenytoin, and valproate.

Switching (or stopping) AEDs before conception
Changes in AEDs are rarely made after conception. Any switches that patients may desire—from a potentially unsafe drug to a “safer” AED, for example—should be considered at least a year prior to planned pregnancy so good seizure control can be achieved before then.

In attempting a change in medication, start by checking the serum drug level of the patient’s effective, yet potentially unsafe, antiseizure drug. That allows you to determine the baseline therapeutic drug level and dose at which the patient is seizure-free. Then add the second, safer AED and taper it up to its therapeutic dose, guided by serum drug levels and the manufacturer’s recommended titration schedule. Once the new medication has reached the therapeutic serum level, begin titrating the older AED down. If the patient suffers a breakthrough seizure during the cross-taper, we recommend aborting the process and rapidly titrating the first drug back to the predetermined therapeutic level.

What about stopping AED therapy entirely if your patient wants to get pregnant? Stopping AEDs is a clinical decision made by the treating physician in accordance with the patient’s wishes on a case-by-case basis, and should be considered only when it is highly likely that seizures will not recur as a result. If the patient has a history of poorly controlled epilepsy despite adequate AED trials, a structural brain lesion, persistently abnormal electroencephalograms, or any other finding that suggests she may have recurrent seizures, explain that the risk of discontinuing the medication is greater than the risk of fetal exposure to AEDs. It is also important to point out that more than 90% of women with epilepsy have normal, healthy children14—and that there are other steps to take to mitigate risk.13

What to consider in the first trimester

Registries that aim to gather data on the outcomes of a large number of AED-exposed pregnancies are a source of reliable information regarding the risks associated with various antiseizure agents. The primary US-based registry is the AED Pregnancy Registry, available at http://aedpregnancyregistry.org. We recommend that physicians caring for pregnant women with epilepsy encourage them to enroll early on, before any prenatal tests are performed. Explain to your patient that by joining the registry, she will be helping others like her make informed decisions about prenatal care.

Prenatal testing. We also recommend that pregnant women taking AEDs—particularly those on higher-risk drugs such as valproate—undergo a detailed first trimester ultrasound study between 16 and 20 weeks’ gestation. Amniocentesis should be avoided, if possible; if needed, however, amniotic alpha-fetoprotein levels may be determined for additional risk assessment.15

 

 

Medication changes. Once a woman is pregnant, stopping or switching AEDs requires a higher level of caution and is usually ill advised. We generally avoid medication switches after conception. But if a patient explicitly requests a change to a “safer” agent, we may attempt a cross-taper, as we would before pregnancy. Evidence suggests, however, that it may be too late to avoid the risk for major congenital malformations, which typically develop very early in pregnancy.1,3

Avoid untried AEDs. We advise against changing a pregnant woman’s seizure medication to an agent she has not tried before, because of the risks of both common adverse effects, such as allergies, and rare idiosyncratic reactions leading to aplastic anemia and Stevens-Johnson syndrome.

AED dosing throughout pregnancy

When seizures are well controlled prior to conception, they usually remain controlled during pregnancy, although both increases and decreases in seizure frequency have been reported.16 Seizure exacerbations are usually due to decreased AED levels; this may be the result of decreased plasma protein binding, decreased albumin concentration, or increased drug clearance,16 although stress, sleep deprivation, and noncompliance may be contributing factors, as well. The changes in pharmacokinetics make it imperative that seizure frequency as well as AED levels be carefully monitored throughout pregnancy.

Although detailed information about changes in serum levels of the newer AEDs during pregnancy is not available, it can be assumed that they will decline somewhat even if the dose remains the same. Carbamazepine has the least alteration in metabolism during pregnancy,17 while a widely disparate effect on lamotrigine metabolism during pregnancy has been noted. In some women, serum levels of lamotrigine have been shown to decrease by as much as 60% to 90% due to induction of UDP-glucuronosyltransferase (UGT) enzymes,18 the drug’s main metabolic enzymes. Increased clearance of lamotrigine typically occurs within the first several weeks of pregnancy and returns to baseline within 2 weeks after birth.

As a result, incremental dosing of lamotrigine is usually required early in the pregnancy. In some cases, dramatic increases—several multiples of the preconception dose—may be needed, followed by a rapid decrease after delivery.18

Monitoring drug levels
Our approach to monitoring AED levels in a pregnant woman with epilepsy includes the following:

  • Check levels at baseline—prior to conception, whenever possible—and monthly throughout the pregnancy, with more frequent checks for women with recurrent seizures and those taking lamotrigine.
  • Use the dose at which the patient was seizure-free prior to conception as a target level during pregnancy.
  • Adjust the dose as needed to maintain the preconception serum drug level.

Drug-specific considerations. As phenytoin and valproate are highly protein-bound, we follow free levels during pregnancy rather than total levels alone. (If your facility is not equipped to track free drug levels, it is important to realize that total levels of these AEDs may not accurately reflect the drug level.) If your patient is taking phenytoin and you’re unable to obtain this information, you can use the patient’s albumin level and the total phenytoin level to estimate the free level of the drug with the following formula:

Free phenytoin = measured level/ [(0.2 × albumin level) + 0.1].

Provide vitamin K augmentation late in pregnancy. In addition to routinely prescribing 4 mg/d folic acid for pregnant women with epilepsy, we recommend oral augmentation of vitamin K as another protective measure.

AEDs that induce hepatic CYP enzymes also induce vitamin K metabolism, thereby reducing the effectiveness of vitamin K-dependent clotting factors and predisposing newborns to hemorrhagic disease.13 It remains unclear whether only women who are taking CYP enzyme-inducing AEDs or all women taking AEDs should receive oral vitamin K supplementation in the last few weeks of pregnancy. We recommend oral vitamin K supplementation for all pregnant women with epilepsy (phytonadione 10 mg/d) starting at 36 weeks’ gestation and continuing until delivery despite the lack of a proven benefit because it is safe and carries little, if any, risk.

An intramuscular injection of 1 mg vitamin K is generally given to all newborns—regardless of whether the mother has epilepsy and takes AEDs—to prevent hemorrhagic disease.13

Should women taking AEDs breastfeed?

The advantages of breastfeeding are largely undisputed, but women being treated with AEDs are generally concerned about the possibility of contaminated breast milk. While antiepileptic agents such as gabapentin, lamotrigine, levetiracetam, and topiramate are excreted in breast milk in potentially clinically important amounts, no short-term adverse effects have been observed in nursing infants of women being treated with AEDs.13 Little information is available regarding long-term effects, and the AAN and AES state that further study is needed. Nonetheless, breastfeeding is generally believed to be a relatively safe option for patients with epilepsy who are being treated with AEDs, and is not contra-indicated by the AAN/AES guidelines.13

 

 

Indeed, pregnancy itself is relatively safe for women with epilepsy. When you’re involved in their care, your awareness of the teratogenicity of various AEDs, the factors to consider in managing epilepsy and pregnancy, and the steps to take to mitigate risk will help you maximize the chance of a positive outcome.

CORRESPONDENCE Nitin K. Sethi, MD, Comprehensive Epilepsy Center, New York-Presbyterian Hospital, Weill Cornell Medical Center, 525 East 68th Street, Room K-615, New York, NY 10021; [email protected]

References

1. Harden CL, Meador KJ, Pennell PB, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): teratogenesis and perinatal outcomes: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:133-141.

2. Sachdeo R. The evidence-based rationale for monotherapy in appropriate patients with epilepsy. Neurology. 2007;69 (suppl 3):S1-S2.

3. Holmes GL, Harden C, Liporace J, et al. Postnatal concerns in children born to women with epilepsy. Epilepsy Behav. 2007;11:270-276.

4. Bromley RL, Baker GA, Meador KJ. Cognitive abilities and behaviour of children exposed to antiepileptic drugs in utero. Curr Opin Neurol. 2009;22:162-166.

5. Yerby MS, Kaplan P, Tran T. Risks and management of pregnancy in women with epilepsy. Cleve Clin J Med. 2004;71 (suppl 2):S25-S37.

6. Samren EB, van Duijn CM, Koch S, et al. Maternal use of anti-epileptic drugs and the risk of major congenital malformations: a joint European prospective study of human teratogenesis associated with maternal epilepsy. Epilepsia. 1997;38:981-990.

7. Bittigau P, Sifringer M, Genz K, et al. Antiepileptic drugs and apoptotic neurodegeneration in the developing brain. Proc Natl Acad Sci. 2002;99:15089-15094.

8. Katz I, Kim J, Gale K, et al. Effects of lamotrigine alone and in combination with MK-801, phenobarbital, or phenytoin on cell death in the neonatal rat brain. J Pharmacol Exp Ther. 2007;322:494-500.

9. Meador KJ, Baker GA, Finnell RH, et al. In utero antiepileptic drug exposure: fetal death and malformations. Neurology. 2006;67:407-412.

10. Kaaja R, Hiilesmaa V. Major malformations in off spring of women with epilepsy. Neurology. 2003;60:575-579.

11. Rasmussen MM, Clemmensen D. Folic acid supplementation in pregnant women. Dan Med Bull. 2010;57:A4134.-

12. Pittschieler S, Brezinka C, Jahn B, et al. Spontaneous abortion and the prophylactic effect of folic acid supplementation in epileptic women undergoing antiepileptic therapy. J Neurol. 2008;255:1926-1931.

13. Epilepsy Foundation. Pregnancy & parenting. Available at: www.epilepsyfoundation.org/living/women/pregnancy/weipregnancy.cfm. Accessed November 4, 2010.

14. Harden CL, Pennell PB, Koppel BS, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): vitamin K, folic acid, blood levels, and breastfeeding: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:142-149.

15. Tettenborn B. Management of epilepsy in women of child-bearing age: practice recommendations. CNS Drugs. 2006;20:373-387.

16. Harden CL, Hopp J, Ting TY, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): obstetrical complications and change in seizure frequency: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:126-132.

17. Kennedy F, Morrow J, Hunt S, et al. PATH39 malformation risks of antiepileptic drugs in pregnancy: an update from the UK Epilepsy and Pregnancy Registry. J Neurol Neurosurg Psychiatry. 2010;81:e18.-

18. Ohman I, Beck O, Vitols S, et al. Plasma concentrations of lamotrigine and its 2-N-glucuronide metabolite during pregnancy in women with epilepsy. Epilepsia. 2008;49:1075-1080.

19. Artama M, Auvinen A, Raudaskoski T, et al. Antiepileptic drug use of women with epilepsy and congenital malformations in off spring. Neurology. 2005;64:1874-1878.

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Nitin K. Sethi, MD
Amy Wasterlain, MD candidate
Cynthia L. Harden, MD
Department of Neurology, New York-Presbyterian Hospital, Weill Cornell Medical Center, New York, NY
[email protected]

Dr. Sethi and Ms. Wasterlain reported no potential conflict of interest relevant to this article. Dr. Harden reported that she receives research support from Forest Pharmaceuticals; serves as a consultant to H. Lundbeck A/S, Novartis, Sepracor Inc., and UCB; and is on the speakers’ bureau of GlaxoSmithKline, H. Lundbeck A/S, and UCB.

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Amy Wasterlain, MD candidate
Cynthia L. Harden, MD
Department of Neurology, New York-Presbyterian Hospital, Weill Cornell Medical Center, New York, NY
[email protected]

Dr. Sethi and Ms. Wasterlain reported no potential conflict of interest relevant to this article. Dr. Harden reported that she receives research support from Forest Pharmaceuticals; serves as a consultant to H. Lundbeck A/S, Novartis, Sepracor Inc., and UCB; and is on the speakers’ bureau of GlaxoSmithKline, H. Lundbeck A/S, and UCB.

Author and Disclosure Information

Nitin K. Sethi, MD
Amy Wasterlain, MD candidate
Cynthia L. Harden, MD
Department of Neurology, New York-Presbyterian Hospital, Weill Cornell Medical Center, New York, NY
[email protected]

Dr. Sethi and Ms. Wasterlain reported no potential conflict of interest relevant to this article. Dr. Harden reported that she receives research support from Forest Pharmaceuticals; serves as a consultant to H. Lundbeck A/S, Novartis, Sepracor Inc., and UCB; and is on the speakers’ bureau of GlaxoSmithKline, H. Lundbeck A/S, and UCB.

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PRACTICE RECOMMENDATIONS

Use the dose of antiepileptic drug (AED) at which the patient is seizure-free prior to conception as a target level to adjust dosing during pregnancy. C

Avoid switching a pregnant patient to an AED that she has not taken before. C

Start all women who have epilepsy and are of childbearing age on ≥0.4 mg folic acid daily prior to conception. C

Strength of recommendation (SOR)

A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series

About 500,000 women in the United States suffer from epilepsy and are of childbearing age.1 For these patients and their physicians, family planning and pregnancy are complex and fraught with risk.

The dilemma: Infants born to women with epilepsy have a 2- to 3-fold risk of congenital malformations compared with those whose mothers do not have epilepsy, mainly related to exposure to antiepileptic drugs (AEDs).2 Recent studies also suggest that children exposed to AEDs such as valproate, phenobarbital, and phenytoin in utero may have neuro-cognitive deficits, even when there are no major congenital malformations.1,3,4

Yet discontinuing the drugs prior to conception or in early pregnancy is rarely a viable option. In 1 recent prospective study, convulsive seizures during the first trimester (the type and timing of seizure thought to have the most harmful effect on the developing fetus) were associated with malformations in 7.4% of pregnancies.2 Seizures also increase the risk of both fetal and maternal death, although the extent of that risk is not known.5

Ideally, pregnant women with epilepsy should be under the care of both an obstetrician experienced in high-risk pregnancies and a neurologist or an epileptologist. In reality, those who live in areas with limited access to such specialized care or have limited health coverage may be cared for throughout their pregnancy by a family physician. This evidence-based review was developed with this family physician in mind.

Safeguarding mom and fetus starts before pregnancy

Mechanisms by which AEDs cause fetal and embryonic harm remain unclear. Possible causes include drug toxicity, drug-drug interactions, folic acid deficiency, the effect of suboptimally controlled convulsions, genetic predisposition, enhanced apoptotic neurodegeneration, and alterations in thyroid hormone status, among others.6-9 Major congenital malformations may occur in a dose-dependent manner, and physicians should aim to use the most effective AED at the lowest effective dose for women of childbearing age.2

In reviewing antiseizure therapy for such patients, here are some considerations to keep in mind:

Avoid polytherapy, if possible. Taking multiple AEDs may increase the risk of major congenital malformations, especially when valproate is one of the drugs.1 Hence, an attempt should be made to switch women with epilepsy who are of childbearing age to monotherapy. Ideally, this should be done a year before planned conception so that good seizure control can be achieved and documented prior to pregnancy.

Avoid high-risk AEDs. Overall, an increased risk of major congenital malformations has been most convincingly found with valproate and phenobarbital.1 Specific types of malformations have also been linked to certain drugs (TABLE). Cardiac malformations are associated with carbamazepine, phenobarbital, and valproate; spina bifida, hypospadias, porencephaly, and other brain anomalies, as well as limb reduction defects, are associated with valproate, particularly at doses >1100 mg/d.10

Choose newer agents, whenever possible. The risk of malformations with newer AEDs—including gabapentin, lamotrigine, levetiracetam, oxcarbazepine, and topiramate—remains unclear, but preliminary data for monotherapy with these agents suggest a lower teratogenic risk compared with traditional AEDs, such as phenobarbital and valproate.10

Initiate folic acid supplementation. Drug-induced folate deficiency has been proposed as a contributing factor in the teratogenicity of AEDs, so diligence is essential in ensuring that patients who have epilepsy and are of childbearing age take folic acid.11 Studies have demonstrated a significant reduction in spontaneous abortion in women who are receiving AED therapy and taking folic acid supplements, and the benefits of folic acid have been found to be especially notable for women taking valproate.12

Folic acid supplementation, of course, is important for all women of childbearing age. At a dose of 0.8 mg/d, folate has been shown to reduce the risk of neural tube and ventricular septal defects in the general population. The American Academy of Neurology/American Epilepsy Society (AAN/AES) Practice Parameters recommend that all women of child-bearing age taking AEDs also take folate supplements (0.4-4 mg/d).13 An optimal dose has not been determined for this patient population, but we routinely recommend 1 mg/d for women with epilepsy of childbearing age and increase the dose to 4 mg/d after conception.

TABLE
Commonly used antiepileptic drugs: Major teratogenic risks1,10,19

 

 

AED

FDA pregnancy category*

Associated risks

Recommendations for use during pregnancy

Carbamazepine

C

Cardiac malformations

Lower teratogenic potential compared with phenobarbital and valproate

Gabapentin

C

No MCMs associated with monotherapy

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Lamotrigine

C

No distinctive pattern of MCMs

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Levetiracetam

C

Pyloric stenosis (in polytherapy with lamotrigine); spina bifida (in polytherapy with carbamazepine and valproate)

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Oxcarbazepine

C

Urogenital malformations

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Phenobarbital

D

Cardiac malformations
Increases risk of MCMs to at least double that of general population

Best avoided in women of childbearing age

Phenytoin

D

Bradycardia and hypotension; fetal hydantoin syndrome

Best avoided in women of childbearing age

Topiramate

C

Hypospadias; oral clefts

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Valproate

D

Cardiac malformations; hypospadias; limb reduction defects; neural tube defects; porencephaly; spina bifida
Increases risk of MCMs to at least double that of general population

Best avoided in women of childbearing age

AED, antiepileptic drug; FDA, US Food and Drug Administration; MCMs, major congenital malformations.

*Category C indicates that human data are lacking and animal studies were positive OR not done; Category D indicates that human data have shown a teratogenic risk but the benefits may outweigh the risk.

†Traditional AEDs include carbamazepine, phenobarbital, phenytoin, and valproate.

Switching (or stopping) AEDs before conception
Changes in AEDs are rarely made after conception. Any switches that patients may desire—from a potentially unsafe drug to a “safer” AED, for example—should be considered at least a year prior to planned pregnancy so good seizure control can be achieved before then.

In attempting a change in medication, start by checking the serum drug level of the patient’s effective, yet potentially unsafe, antiseizure drug. That allows you to determine the baseline therapeutic drug level and dose at which the patient is seizure-free. Then add the second, safer AED and taper it up to its therapeutic dose, guided by serum drug levels and the manufacturer’s recommended titration schedule. Once the new medication has reached the therapeutic serum level, begin titrating the older AED down. If the patient suffers a breakthrough seizure during the cross-taper, we recommend aborting the process and rapidly titrating the first drug back to the predetermined therapeutic level.

What about stopping AED therapy entirely if your patient wants to get pregnant? Stopping AEDs is a clinical decision made by the treating physician in accordance with the patient’s wishes on a case-by-case basis, and should be considered only when it is highly likely that seizures will not recur as a result. If the patient has a history of poorly controlled epilepsy despite adequate AED trials, a structural brain lesion, persistently abnormal electroencephalograms, or any other finding that suggests she may have recurrent seizures, explain that the risk of discontinuing the medication is greater than the risk of fetal exposure to AEDs. It is also important to point out that more than 90% of women with epilepsy have normal, healthy children14—and that there are other steps to take to mitigate risk.13

What to consider in the first trimester

Registries that aim to gather data on the outcomes of a large number of AED-exposed pregnancies are a source of reliable information regarding the risks associated with various antiseizure agents. The primary US-based registry is the AED Pregnancy Registry, available at http://aedpregnancyregistry.org. We recommend that physicians caring for pregnant women with epilepsy encourage them to enroll early on, before any prenatal tests are performed. Explain to your patient that by joining the registry, she will be helping others like her make informed decisions about prenatal care.

Prenatal testing. We also recommend that pregnant women taking AEDs—particularly those on higher-risk drugs such as valproate—undergo a detailed first trimester ultrasound study between 16 and 20 weeks’ gestation. Amniocentesis should be avoided, if possible; if needed, however, amniotic alpha-fetoprotein levels may be determined for additional risk assessment.15

 

 

Medication changes. Once a woman is pregnant, stopping or switching AEDs requires a higher level of caution and is usually ill advised. We generally avoid medication switches after conception. But if a patient explicitly requests a change to a “safer” agent, we may attempt a cross-taper, as we would before pregnancy. Evidence suggests, however, that it may be too late to avoid the risk for major congenital malformations, which typically develop very early in pregnancy.1,3

Avoid untried AEDs. We advise against changing a pregnant woman’s seizure medication to an agent she has not tried before, because of the risks of both common adverse effects, such as allergies, and rare idiosyncratic reactions leading to aplastic anemia and Stevens-Johnson syndrome.

AED dosing throughout pregnancy

When seizures are well controlled prior to conception, they usually remain controlled during pregnancy, although both increases and decreases in seizure frequency have been reported.16 Seizure exacerbations are usually due to decreased AED levels; this may be the result of decreased plasma protein binding, decreased albumin concentration, or increased drug clearance,16 although stress, sleep deprivation, and noncompliance may be contributing factors, as well. The changes in pharmacokinetics make it imperative that seizure frequency as well as AED levels be carefully monitored throughout pregnancy.

Although detailed information about changes in serum levels of the newer AEDs during pregnancy is not available, it can be assumed that they will decline somewhat even if the dose remains the same. Carbamazepine has the least alteration in metabolism during pregnancy,17 while a widely disparate effect on lamotrigine metabolism during pregnancy has been noted. In some women, serum levels of lamotrigine have been shown to decrease by as much as 60% to 90% due to induction of UDP-glucuronosyltransferase (UGT) enzymes,18 the drug’s main metabolic enzymes. Increased clearance of lamotrigine typically occurs within the first several weeks of pregnancy and returns to baseline within 2 weeks after birth.

As a result, incremental dosing of lamotrigine is usually required early in the pregnancy. In some cases, dramatic increases—several multiples of the preconception dose—may be needed, followed by a rapid decrease after delivery.18

Monitoring drug levels
Our approach to monitoring AED levels in a pregnant woman with epilepsy includes the following:

  • Check levels at baseline—prior to conception, whenever possible—and monthly throughout the pregnancy, with more frequent checks for women with recurrent seizures and those taking lamotrigine.
  • Use the dose at which the patient was seizure-free prior to conception as a target level during pregnancy.
  • Adjust the dose as needed to maintain the preconception serum drug level.

Drug-specific considerations. As phenytoin and valproate are highly protein-bound, we follow free levels during pregnancy rather than total levels alone. (If your facility is not equipped to track free drug levels, it is important to realize that total levels of these AEDs may not accurately reflect the drug level.) If your patient is taking phenytoin and you’re unable to obtain this information, you can use the patient’s albumin level and the total phenytoin level to estimate the free level of the drug with the following formula:

Free phenytoin = measured level/ [(0.2 × albumin level) + 0.1].

Provide vitamin K augmentation late in pregnancy. In addition to routinely prescribing 4 mg/d folic acid for pregnant women with epilepsy, we recommend oral augmentation of vitamin K as another protective measure.

AEDs that induce hepatic CYP enzymes also induce vitamin K metabolism, thereby reducing the effectiveness of vitamin K-dependent clotting factors and predisposing newborns to hemorrhagic disease.13 It remains unclear whether only women who are taking CYP enzyme-inducing AEDs or all women taking AEDs should receive oral vitamin K supplementation in the last few weeks of pregnancy. We recommend oral vitamin K supplementation for all pregnant women with epilepsy (phytonadione 10 mg/d) starting at 36 weeks’ gestation and continuing until delivery despite the lack of a proven benefit because it is safe and carries little, if any, risk.

An intramuscular injection of 1 mg vitamin K is generally given to all newborns—regardless of whether the mother has epilepsy and takes AEDs—to prevent hemorrhagic disease.13

Should women taking AEDs breastfeed?

The advantages of breastfeeding are largely undisputed, but women being treated with AEDs are generally concerned about the possibility of contaminated breast milk. While antiepileptic agents such as gabapentin, lamotrigine, levetiracetam, and topiramate are excreted in breast milk in potentially clinically important amounts, no short-term adverse effects have been observed in nursing infants of women being treated with AEDs.13 Little information is available regarding long-term effects, and the AAN and AES state that further study is needed. Nonetheless, breastfeeding is generally believed to be a relatively safe option for patients with epilepsy who are being treated with AEDs, and is not contra-indicated by the AAN/AES guidelines.13

 

 

Indeed, pregnancy itself is relatively safe for women with epilepsy. When you’re involved in their care, your awareness of the teratogenicity of various AEDs, the factors to consider in managing epilepsy and pregnancy, and the steps to take to mitigate risk will help you maximize the chance of a positive outcome.

CORRESPONDENCE Nitin K. Sethi, MD, Comprehensive Epilepsy Center, New York-Presbyterian Hospital, Weill Cornell Medical Center, 525 East 68th Street, Room K-615, New York, NY 10021; [email protected]

PRACTICE RECOMMENDATIONS

Use the dose of antiepileptic drug (AED) at which the patient is seizure-free prior to conception as a target level to adjust dosing during pregnancy. C

Avoid switching a pregnant patient to an AED that she has not taken before. C

Start all women who have epilepsy and are of childbearing age on ≥0.4 mg folic acid daily prior to conception. C

Strength of recommendation (SOR)

A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series

About 500,000 women in the United States suffer from epilepsy and are of childbearing age.1 For these patients and their physicians, family planning and pregnancy are complex and fraught with risk.

The dilemma: Infants born to women with epilepsy have a 2- to 3-fold risk of congenital malformations compared with those whose mothers do not have epilepsy, mainly related to exposure to antiepileptic drugs (AEDs).2 Recent studies also suggest that children exposed to AEDs such as valproate, phenobarbital, and phenytoin in utero may have neuro-cognitive deficits, even when there are no major congenital malformations.1,3,4

Yet discontinuing the drugs prior to conception or in early pregnancy is rarely a viable option. In 1 recent prospective study, convulsive seizures during the first trimester (the type and timing of seizure thought to have the most harmful effect on the developing fetus) were associated with malformations in 7.4% of pregnancies.2 Seizures also increase the risk of both fetal and maternal death, although the extent of that risk is not known.5

Ideally, pregnant women with epilepsy should be under the care of both an obstetrician experienced in high-risk pregnancies and a neurologist or an epileptologist. In reality, those who live in areas with limited access to such specialized care or have limited health coverage may be cared for throughout their pregnancy by a family physician. This evidence-based review was developed with this family physician in mind.

Safeguarding mom and fetus starts before pregnancy

Mechanisms by which AEDs cause fetal and embryonic harm remain unclear. Possible causes include drug toxicity, drug-drug interactions, folic acid deficiency, the effect of suboptimally controlled convulsions, genetic predisposition, enhanced apoptotic neurodegeneration, and alterations in thyroid hormone status, among others.6-9 Major congenital malformations may occur in a dose-dependent manner, and physicians should aim to use the most effective AED at the lowest effective dose for women of childbearing age.2

In reviewing antiseizure therapy for such patients, here are some considerations to keep in mind:

Avoid polytherapy, if possible. Taking multiple AEDs may increase the risk of major congenital malformations, especially when valproate is one of the drugs.1 Hence, an attempt should be made to switch women with epilepsy who are of childbearing age to monotherapy. Ideally, this should be done a year before planned conception so that good seizure control can be achieved and documented prior to pregnancy.

Avoid high-risk AEDs. Overall, an increased risk of major congenital malformations has been most convincingly found with valproate and phenobarbital.1 Specific types of malformations have also been linked to certain drugs (TABLE). Cardiac malformations are associated with carbamazepine, phenobarbital, and valproate; spina bifida, hypospadias, porencephaly, and other brain anomalies, as well as limb reduction defects, are associated with valproate, particularly at doses >1100 mg/d.10

Choose newer agents, whenever possible. The risk of malformations with newer AEDs—including gabapentin, lamotrigine, levetiracetam, oxcarbazepine, and topiramate—remains unclear, but preliminary data for monotherapy with these agents suggest a lower teratogenic risk compared with traditional AEDs, such as phenobarbital and valproate.10

Initiate folic acid supplementation. Drug-induced folate deficiency has been proposed as a contributing factor in the teratogenicity of AEDs, so diligence is essential in ensuring that patients who have epilepsy and are of childbearing age take folic acid.11 Studies have demonstrated a significant reduction in spontaneous abortion in women who are receiving AED therapy and taking folic acid supplements, and the benefits of folic acid have been found to be especially notable for women taking valproate.12

Folic acid supplementation, of course, is important for all women of childbearing age. At a dose of 0.8 mg/d, folate has been shown to reduce the risk of neural tube and ventricular septal defects in the general population. The American Academy of Neurology/American Epilepsy Society (AAN/AES) Practice Parameters recommend that all women of child-bearing age taking AEDs also take folate supplements (0.4-4 mg/d).13 An optimal dose has not been determined for this patient population, but we routinely recommend 1 mg/d for women with epilepsy of childbearing age and increase the dose to 4 mg/d after conception.

TABLE
Commonly used antiepileptic drugs: Major teratogenic risks1,10,19

 

 

AED

FDA pregnancy category*

Associated risks

Recommendations for use during pregnancy

Carbamazepine

C

Cardiac malformations

Lower teratogenic potential compared with phenobarbital and valproate

Gabapentin

C

No MCMs associated with monotherapy

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Lamotrigine

C

No distinctive pattern of MCMs

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Levetiracetam

C

Pyloric stenosis (in polytherapy with lamotrigine); spina bifida (in polytherapy with carbamazepine and valproate)

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Oxcarbazepine

C

Urogenital malformations

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Phenobarbital

D

Cardiac malformations
Increases risk of MCMs to at least double that of general population

Best avoided in women of childbearing age

Phenytoin

D

Bradycardia and hypotension; fetal hydantoin syndrome

Best avoided in women of childbearing age

Topiramate

C

Hypospadias; oral clefts

Limited data suggest lower teratogenic risk compared with traditional AEDs†

Valproate

D

Cardiac malformations; hypospadias; limb reduction defects; neural tube defects; porencephaly; spina bifida
Increases risk of MCMs to at least double that of general population

Best avoided in women of childbearing age

AED, antiepileptic drug; FDA, US Food and Drug Administration; MCMs, major congenital malformations.

*Category C indicates that human data are lacking and animal studies were positive OR not done; Category D indicates that human data have shown a teratogenic risk but the benefits may outweigh the risk.

†Traditional AEDs include carbamazepine, phenobarbital, phenytoin, and valproate.

Switching (or stopping) AEDs before conception
Changes in AEDs are rarely made after conception. Any switches that patients may desire—from a potentially unsafe drug to a “safer” AED, for example—should be considered at least a year prior to planned pregnancy so good seizure control can be achieved before then.

In attempting a change in medication, start by checking the serum drug level of the patient’s effective, yet potentially unsafe, antiseizure drug. That allows you to determine the baseline therapeutic drug level and dose at which the patient is seizure-free. Then add the second, safer AED and taper it up to its therapeutic dose, guided by serum drug levels and the manufacturer’s recommended titration schedule. Once the new medication has reached the therapeutic serum level, begin titrating the older AED down. If the patient suffers a breakthrough seizure during the cross-taper, we recommend aborting the process and rapidly titrating the first drug back to the predetermined therapeutic level.

What about stopping AED therapy entirely if your patient wants to get pregnant? Stopping AEDs is a clinical decision made by the treating physician in accordance with the patient’s wishes on a case-by-case basis, and should be considered only when it is highly likely that seizures will not recur as a result. If the patient has a history of poorly controlled epilepsy despite adequate AED trials, a structural brain lesion, persistently abnormal electroencephalograms, or any other finding that suggests she may have recurrent seizures, explain that the risk of discontinuing the medication is greater than the risk of fetal exposure to AEDs. It is also important to point out that more than 90% of women with epilepsy have normal, healthy children14—and that there are other steps to take to mitigate risk.13

What to consider in the first trimester

Registries that aim to gather data on the outcomes of a large number of AED-exposed pregnancies are a source of reliable information regarding the risks associated with various antiseizure agents. The primary US-based registry is the AED Pregnancy Registry, available at http://aedpregnancyregistry.org. We recommend that physicians caring for pregnant women with epilepsy encourage them to enroll early on, before any prenatal tests are performed. Explain to your patient that by joining the registry, she will be helping others like her make informed decisions about prenatal care.

Prenatal testing. We also recommend that pregnant women taking AEDs—particularly those on higher-risk drugs such as valproate—undergo a detailed first trimester ultrasound study between 16 and 20 weeks’ gestation. Amniocentesis should be avoided, if possible; if needed, however, amniotic alpha-fetoprotein levels may be determined for additional risk assessment.15

 

 

Medication changes. Once a woman is pregnant, stopping or switching AEDs requires a higher level of caution and is usually ill advised. We generally avoid medication switches after conception. But if a patient explicitly requests a change to a “safer” agent, we may attempt a cross-taper, as we would before pregnancy. Evidence suggests, however, that it may be too late to avoid the risk for major congenital malformations, which typically develop very early in pregnancy.1,3

Avoid untried AEDs. We advise against changing a pregnant woman’s seizure medication to an agent she has not tried before, because of the risks of both common adverse effects, such as allergies, and rare idiosyncratic reactions leading to aplastic anemia and Stevens-Johnson syndrome.

AED dosing throughout pregnancy

When seizures are well controlled prior to conception, they usually remain controlled during pregnancy, although both increases and decreases in seizure frequency have been reported.16 Seizure exacerbations are usually due to decreased AED levels; this may be the result of decreased plasma protein binding, decreased albumin concentration, or increased drug clearance,16 although stress, sleep deprivation, and noncompliance may be contributing factors, as well. The changes in pharmacokinetics make it imperative that seizure frequency as well as AED levels be carefully monitored throughout pregnancy.

Although detailed information about changes in serum levels of the newer AEDs during pregnancy is not available, it can be assumed that they will decline somewhat even if the dose remains the same. Carbamazepine has the least alteration in metabolism during pregnancy,17 while a widely disparate effect on lamotrigine metabolism during pregnancy has been noted. In some women, serum levels of lamotrigine have been shown to decrease by as much as 60% to 90% due to induction of UDP-glucuronosyltransferase (UGT) enzymes,18 the drug’s main metabolic enzymes. Increased clearance of lamotrigine typically occurs within the first several weeks of pregnancy and returns to baseline within 2 weeks after birth.

As a result, incremental dosing of lamotrigine is usually required early in the pregnancy. In some cases, dramatic increases—several multiples of the preconception dose—may be needed, followed by a rapid decrease after delivery.18

Monitoring drug levels
Our approach to monitoring AED levels in a pregnant woman with epilepsy includes the following:

  • Check levels at baseline—prior to conception, whenever possible—and monthly throughout the pregnancy, with more frequent checks for women with recurrent seizures and those taking lamotrigine.
  • Use the dose at which the patient was seizure-free prior to conception as a target level during pregnancy.
  • Adjust the dose as needed to maintain the preconception serum drug level.

Drug-specific considerations. As phenytoin and valproate are highly protein-bound, we follow free levels during pregnancy rather than total levels alone. (If your facility is not equipped to track free drug levels, it is important to realize that total levels of these AEDs may not accurately reflect the drug level.) If your patient is taking phenytoin and you’re unable to obtain this information, you can use the patient’s albumin level and the total phenytoin level to estimate the free level of the drug with the following formula:

Free phenytoin = measured level/ [(0.2 × albumin level) + 0.1].

Provide vitamin K augmentation late in pregnancy. In addition to routinely prescribing 4 mg/d folic acid for pregnant women with epilepsy, we recommend oral augmentation of vitamin K as another protective measure.

AEDs that induce hepatic CYP enzymes also induce vitamin K metabolism, thereby reducing the effectiveness of vitamin K-dependent clotting factors and predisposing newborns to hemorrhagic disease.13 It remains unclear whether only women who are taking CYP enzyme-inducing AEDs or all women taking AEDs should receive oral vitamin K supplementation in the last few weeks of pregnancy. We recommend oral vitamin K supplementation for all pregnant women with epilepsy (phytonadione 10 mg/d) starting at 36 weeks’ gestation and continuing until delivery despite the lack of a proven benefit because it is safe and carries little, if any, risk.

An intramuscular injection of 1 mg vitamin K is generally given to all newborns—regardless of whether the mother has epilepsy and takes AEDs—to prevent hemorrhagic disease.13

Should women taking AEDs breastfeed?

The advantages of breastfeeding are largely undisputed, but women being treated with AEDs are generally concerned about the possibility of contaminated breast milk. While antiepileptic agents such as gabapentin, lamotrigine, levetiracetam, and topiramate are excreted in breast milk in potentially clinically important amounts, no short-term adverse effects have been observed in nursing infants of women being treated with AEDs.13 Little information is available regarding long-term effects, and the AAN and AES state that further study is needed. Nonetheless, breastfeeding is generally believed to be a relatively safe option for patients with epilepsy who are being treated with AEDs, and is not contra-indicated by the AAN/AES guidelines.13

 

 

Indeed, pregnancy itself is relatively safe for women with epilepsy. When you’re involved in their care, your awareness of the teratogenicity of various AEDs, the factors to consider in managing epilepsy and pregnancy, and the steps to take to mitigate risk will help you maximize the chance of a positive outcome.

CORRESPONDENCE Nitin K. Sethi, MD, Comprehensive Epilepsy Center, New York-Presbyterian Hospital, Weill Cornell Medical Center, 525 East 68th Street, Room K-615, New York, NY 10021; [email protected]

References

1. Harden CL, Meador KJ, Pennell PB, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): teratogenesis and perinatal outcomes: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:133-141.

2. Sachdeo R. The evidence-based rationale for monotherapy in appropriate patients with epilepsy. Neurology. 2007;69 (suppl 3):S1-S2.

3. Holmes GL, Harden C, Liporace J, et al. Postnatal concerns in children born to women with epilepsy. Epilepsy Behav. 2007;11:270-276.

4. Bromley RL, Baker GA, Meador KJ. Cognitive abilities and behaviour of children exposed to antiepileptic drugs in utero. Curr Opin Neurol. 2009;22:162-166.

5. Yerby MS, Kaplan P, Tran T. Risks and management of pregnancy in women with epilepsy. Cleve Clin J Med. 2004;71 (suppl 2):S25-S37.

6. Samren EB, van Duijn CM, Koch S, et al. Maternal use of anti-epileptic drugs and the risk of major congenital malformations: a joint European prospective study of human teratogenesis associated with maternal epilepsy. Epilepsia. 1997;38:981-990.

7. Bittigau P, Sifringer M, Genz K, et al. Antiepileptic drugs and apoptotic neurodegeneration in the developing brain. Proc Natl Acad Sci. 2002;99:15089-15094.

8. Katz I, Kim J, Gale K, et al. Effects of lamotrigine alone and in combination with MK-801, phenobarbital, or phenytoin on cell death in the neonatal rat brain. J Pharmacol Exp Ther. 2007;322:494-500.

9. Meador KJ, Baker GA, Finnell RH, et al. In utero antiepileptic drug exposure: fetal death and malformations. Neurology. 2006;67:407-412.

10. Kaaja R, Hiilesmaa V. Major malformations in off spring of women with epilepsy. Neurology. 2003;60:575-579.

11. Rasmussen MM, Clemmensen D. Folic acid supplementation in pregnant women. Dan Med Bull. 2010;57:A4134.-

12. Pittschieler S, Brezinka C, Jahn B, et al. Spontaneous abortion and the prophylactic effect of folic acid supplementation in epileptic women undergoing antiepileptic therapy. J Neurol. 2008;255:1926-1931.

13. Epilepsy Foundation. Pregnancy & parenting. Available at: www.epilepsyfoundation.org/living/women/pregnancy/weipregnancy.cfm. Accessed November 4, 2010.

14. Harden CL, Pennell PB, Koppel BS, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): vitamin K, folic acid, blood levels, and breastfeeding: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:142-149.

15. Tettenborn B. Management of epilepsy in women of child-bearing age: practice recommendations. CNS Drugs. 2006;20:373-387.

16. Harden CL, Hopp J, Ting TY, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): obstetrical complications and change in seizure frequency: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:126-132.

17. Kennedy F, Morrow J, Hunt S, et al. PATH39 malformation risks of antiepileptic drugs in pregnancy: an update from the UK Epilepsy and Pregnancy Registry. J Neurol Neurosurg Psychiatry. 2010;81:e18.-

18. Ohman I, Beck O, Vitols S, et al. Plasma concentrations of lamotrigine and its 2-N-glucuronide metabolite during pregnancy in women with epilepsy. Epilepsia. 2008;49:1075-1080.

19. Artama M, Auvinen A, Raudaskoski T, et al. Antiepileptic drug use of women with epilepsy and congenital malformations in off spring. Neurology. 2005;64:1874-1878.

References

1. Harden CL, Meador KJ, Pennell PB, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): teratogenesis and perinatal outcomes: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:133-141.

2. Sachdeo R. The evidence-based rationale for monotherapy in appropriate patients with epilepsy. Neurology. 2007;69 (suppl 3):S1-S2.

3. Holmes GL, Harden C, Liporace J, et al. Postnatal concerns in children born to women with epilepsy. Epilepsy Behav. 2007;11:270-276.

4. Bromley RL, Baker GA, Meador KJ. Cognitive abilities and behaviour of children exposed to antiepileptic drugs in utero. Curr Opin Neurol. 2009;22:162-166.

5. Yerby MS, Kaplan P, Tran T. Risks and management of pregnancy in women with epilepsy. Cleve Clin J Med. 2004;71 (suppl 2):S25-S37.

6. Samren EB, van Duijn CM, Koch S, et al. Maternal use of anti-epileptic drugs and the risk of major congenital malformations: a joint European prospective study of human teratogenesis associated with maternal epilepsy. Epilepsia. 1997;38:981-990.

7. Bittigau P, Sifringer M, Genz K, et al. Antiepileptic drugs and apoptotic neurodegeneration in the developing brain. Proc Natl Acad Sci. 2002;99:15089-15094.

8. Katz I, Kim J, Gale K, et al. Effects of lamotrigine alone and in combination with MK-801, phenobarbital, or phenytoin on cell death in the neonatal rat brain. J Pharmacol Exp Ther. 2007;322:494-500.

9. Meador KJ, Baker GA, Finnell RH, et al. In utero antiepileptic drug exposure: fetal death and malformations. Neurology. 2006;67:407-412.

10. Kaaja R, Hiilesmaa V. Major malformations in off spring of women with epilepsy. Neurology. 2003;60:575-579.

11. Rasmussen MM, Clemmensen D. Folic acid supplementation in pregnant women. Dan Med Bull. 2010;57:A4134.-

12. Pittschieler S, Brezinka C, Jahn B, et al. Spontaneous abortion and the prophylactic effect of folic acid supplementation in epileptic women undergoing antiepileptic therapy. J Neurol. 2008;255:1926-1931.

13. Epilepsy Foundation. Pregnancy & parenting. Available at: www.epilepsyfoundation.org/living/women/pregnancy/weipregnancy.cfm. Accessed November 4, 2010.

14. Harden CL, Pennell PB, Koppel BS, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): vitamin K, folic acid, blood levels, and breastfeeding: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:142-149.

15. Tettenborn B. Management of epilepsy in women of child-bearing age: practice recommendations. CNS Drugs. 2006;20:373-387.

16. Harden CL, Hopp J, Ting TY, et al. American Academy of Neurology; American Epilepsy Society. Practice parameter update: management issues for women with epilepsy—focus on pregnancy (an evidence-based review): obstetrical complications and change in seizure frequency: report of the Quality Standards Subcommittee and Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and American Epilepsy Society. Neurology. 2009;73:126-132.

17. Kennedy F, Morrow J, Hunt S, et al. PATH39 malformation risks of antiepileptic drugs in pregnancy: an update from the UK Epilepsy and Pregnancy Registry. J Neurol Neurosurg Psychiatry. 2010;81:e18.-

18. Ohman I, Beck O, Vitols S, et al. Plasma concentrations of lamotrigine and its 2-N-glucuronide metabolite during pregnancy in women with epilepsy. Epilepsia. 2008;49:1075-1080.

19. Artama M, Auvinen A, Raudaskoski T, et al. Antiepileptic drug use of women with epilepsy and congenital malformations in off spring. Neurology. 2005;64:1874-1878.

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End-of-life dementia care: A palliative perspective

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Dr. Casey reports no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.

Dr. Casey: How to discuss the palliative care approach with families

Psychiatrists need to be aware of aspects of psychiatric and pain management that are unique to end-of-life care for patients with advanced dementia. The palliative care philosophy presents an opportunity to honestly acknowledge the terminal nature of advanced dementia, limit intrusive and unnecessary care, and thoughtfully address pain and suffering.

Studies suggest that nursing home patients with severe dementia have an average life expectancy of 2 years.1 These patients often have substantial medical comorbidity, frequently with multiple illnesses, each of which has an accepted management that may involve several medications and interventions. Treatment guidelines for individual conditions don’t necessarily take into account multiple interacting illnesses in advanced dementia. Applying recommended treatments for multiple conditions simultaneously may entail prescribing many medications and interventions. Often these techniques are designed to prevent or modify disease over long term, which a patient with advanced dementia is not likely to achieve.2

Such polypharmacy and intensive intervention are not likely to extend life or improve its quality. In fact, the opposite may occur. Dementia patients react poorly to polypharmacy and may require restraint or sedation to accommodate invasive interventions. Feeding tubes are particularly challenging. Studies have revealed that feeding tubes do not extend life in advanced dementia patients.3

Goals for palliative care. The palliative care approach emphasizes relieving suffering in the near term. Applying this philosophy to advanced dementia depends on acknowledging that the patient will not recover from this condition, has a limited life expectancy, and is not likely to benefit from—and in fact may be harmed by—an aggressive approach to comorbid conditions. Instead, these conditions are best managed by controlling pain and suffering in the near term. Hospitalization and invasive interventions are minimized.

Psychiatric management fits well within this approach. Near the end of life, dementia patients often suffer agitation, psychosis, depression, and delirium that may require the expert, judicious use of psychopharmacology. Patients often experience pain, but might not be able to communicate this, except through behavioral changes. Physicians may be overly concerned with possible adverse effects of pain medications, but when appropriately prescribed, these drugs may help relieve suffering. Psychiatrists also have a role in assisting staff and families during an emotionally difficult time.4

References

1. Mitchell SL, Kiely DK, Hamel DK, et al. Estimating prognosis for nursing home residents with advanced dementia. JAMA. 2004;291:2734-2740.

2. Boyd CM, Darer J, Boult C, et al. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases. JAMA. 2005;294:741-743.

3. Li I. Feeding tubes in patients with severe dementia. Am Fam Physician. 2002;65(8):1605-1610,1515.

4. Lyness JM. End of life care: issues relevant to the geriatric psychiatrist. Am J Geriatr Psych. 2004;12(5):457-482.

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Dr. Casey reports no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.

Dr. Casey: How to discuss the palliative care approach with families

Psychiatrists need to be aware of aspects of psychiatric and pain management that are unique to end-of-life care for patients with advanced dementia. The palliative care philosophy presents an opportunity to honestly acknowledge the terminal nature of advanced dementia, limit intrusive and unnecessary care, and thoughtfully address pain and suffering.

Studies suggest that nursing home patients with severe dementia have an average life expectancy of 2 years.1 These patients often have substantial medical comorbidity, frequently with multiple illnesses, each of which has an accepted management that may involve several medications and interventions. Treatment guidelines for individual conditions don’t necessarily take into account multiple interacting illnesses in advanced dementia. Applying recommended treatments for multiple conditions simultaneously may entail prescribing many medications and interventions. Often these techniques are designed to prevent or modify disease over long term, which a patient with advanced dementia is not likely to achieve.2

Such polypharmacy and intensive intervention are not likely to extend life or improve its quality. In fact, the opposite may occur. Dementia patients react poorly to polypharmacy and may require restraint or sedation to accommodate invasive interventions. Feeding tubes are particularly challenging. Studies have revealed that feeding tubes do not extend life in advanced dementia patients.3

Goals for palliative care. The palliative care approach emphasizes relieving suffering in the near term. Applying this philosophy to advanced dementia depends on acknowledging that the patient will not recover from this condition, has a limited life expectancy, and is not likely to benefit from—and in fact may be harmed by—an aggressive approach to comorbid conditions. Instead, these conditions are best managed by controlling pain and suffering in the near term. Hospitalization and invasive interventions are minimized.

Psychiatric management fits well within this approach. Near the end of life, dementia patients often suffer agitation, psychosis, depression, and delirium that may require the expert, judicious use of psychopharmacology. Patients often experience pain, but might not be able to communicate this, except through behavioral changes. Physicians may be overly concerned with possible adverse effects of pain medications, but when appropriately prescribed, these drugs may help relieve suffering. Psychiatrists also have a role in assisting staff and families during an emotionally difficult time.4

Dr. Casey reports no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.

Dr. Casey: How to discuss the palliative care approach with families

Psychiatrists need to be aware of aspects of psychiatric and pain management that are unique to end-of-life care for patients with advanced dementia. The palliative care philosophy presents an opportunity to honestly acknowledge the terminal nature of advanced dementia, limit intrusive and unnecessary care, and thoughtfully address pain and suffering.

Studies suggest that nursing home patients with severe dementia have an average life expectancy of 2 years.1 These patients often have substantial medical comorbidity, frequently with multiple illnesses, each of which has an accepted management that may involve several medications and interventions. Treatment guidelines for individual conditions don’t necessarily take into account multiple interacting illnesses in advanced dementia. Applying recommended treatments for multiple conditions simultaneously may entail prescribing many medications and interventions. Often these techniques are designed to prevent or modify disease over long term, which a patient with advanced dementia is not likely to achieve.2

Such polypharmacy and intensive intervention are not likely to extend life or improve its quality. In fact, the opposite may occur. Dementia patients react poorly to polypharmacy and may require restraint or sedation to accommodate invasive interventions. Feeding tubes are particularly challenging. Studies have revealed that feeding tubes do not extend life in advanced dementia patients.3

Goals for palliative care. The palliative care approach emphasizes relieving suffering in the near term. Applying this philosophy to advanced dementia depends on acknowledging that the patient will not recover from this condition, has a limited life expectancy, and is not likely to benefit from—and in fact may be harmed by—an aggressive approach to comorbid conditions. Instead, these conditions are best managed by controlling pain and suffering in the near term. Hospitalization and invasive interventions are minimized.

Psychiatric management fits well within this approach. Near the end of life, dementia patients often suffer agitation, psychosis, depression, and delirium that may require the expert, judicious use of psychopharmacology. Patients often experience pain, but might not be able to communicate this, except through behavioral changes. Physicians may be overly concerned with possible adverse effects of pain medications, but when appropriately prescribed, these drugs may help relieve suffering. Psychiatrists also have a role in assisting staff and families during an emotionally difficult time.4

References

1. Mitchell SL, Kiely DK, Hamel DK, et al. Estimating prognosis for nursing home residents with advanced dementia. JAMA. 2004;291:2734-2740.

2. Boyd CM, Darer J, Boult C, et al. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases. JAMA. 2005;294:741-743.

3. Li I. Feeding tubes in patients with severe dementia. Am Fam Physician. 2002;65(8):1605-1610,1515.

4. Lyness JM. End of life care: issues relevant to the geriatric psychiatrist. Am J Geriatr Psych. 2004;12(5):457-482.

References

1. Mitchell SL, Kiely DK, Hamel DK, et al. Estimating prognosis for nursing home residents with advanced dementia. JAMA. 2004;291:2734-2740.

2. Boyd CM, Darer J, Boult C, et al. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases. JAMA. 2005;294:741-743.

3. Li I. Feeding tubes in patients with severe dementia. Am Fam Physician. 2002;65(8):1605-1610,1515.

4. Lyness JM. End of life care: issues relevant to the geriatric psychiatrist. Am J Geriatr Psych. 2004;12(5):457-482.

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‘Firing’ a patient: May a psychiatrist unilaterally terminate care?

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Dear Dr. Mossman:
One of my patients, Ms. A, keeps calling in to refill her prescription, but will not come in for an appointment; she needs the medication, but I really shouldn’t keep prescribing it without seeing her. Another patient, Mr. B, has an open chart, but he stopped seeing me last year after I treated him for an acute depressive episode. May I “fire” these patients? If so, what should I do?—Submitted by “Dr. C”

All physicians occasionally encounter patients whom we’d like to stop treating, but because we feel devoted to those we treat, the idea of “firing” a patient makes us uncomfortable. Sometimes, however, ending a treatment relationship is the right choice for the doctor and patient.1

To know why, how, and when you may terminate your professional relationship with a patient, you need to:

 

  • understand the legal and ethical status of a doctor-patient relationship
  • know the proper way to end treatment relationships
  • decide whether ending your care of the patient is the right medical and ethical choice.

After exploring these points, we’ll return to the cases of Ms. A and Mr. B and consider what Dr. C might do.

 

DO YOU HAVE A QUESTION ABOUT POSSIBLE LIABILITY?

 

  • Submit your malpractice-related questions to Dr. Mossman at [email protected].
  • Include your name, address, and practice location. If your question is chosen for publication, your name can be withheld by request.

Doctor-patient relationships

Legal and medical authorities characterize the treatment relationship as an implicit contract that imposes certain obligations on the doctor and the patient.2,3 Doctors are compelled to conduct themselves in accordance with the prevailing “standard of care.” Patients’ obligations include being honest and cooperating with care once they have agreed to a treatment plan (Table 1).3

Patients may stop seeing their doctors at any time, but a physician usually must continue to provide all necessary medical attention until either the treatment episode has concluded or both parties agree to end the doctor-patient relationship.2 If a physician wishes to withdraw from a case before the need for services has ended, the physician must either make arrangements for another competent physician to assume care or give the patient ample notice and opportunity to obtain treatment elsewhere.2 If a doctor fails to do this and harm to the patient results, the doctor is guilty of “abandonment,” legally defined as termination of the physician-patient relationship “at an unreasonable time or without affording the patient the opportunity to procure an equally qualified replacement.”2 Physician abandonment can lead to malpractice liability,4 complaints to state licensing authorities,5 and ethical condemnation.6

Table 1

A patient’s responsibilities

 

Being truthful
Providing a complete medical history
Cooperating with agreed-upon treatment and keeping appointments
Meeting financial obligations for medical care
Health-enhancing behavior
Not participating in fraudulent health care
Source: Reference 3

Terminating without abandoning

Doctors commonly terminate care of their patients when they decide to move or close their practices. Accusations of abandonment may arise if such career decisions are executed improperly, but these matters are not as emotionally troubling for physicians as a decision to “fire” a patient because of the patient’s behavior. Common, legitimate reasons a doctor may consider unilateral termination appear in Table 2.7,8

Certain circumstances are not valid grounds for terminating a doctor-patient relationship. You cannot ethically decline to treat a patient whose problem lies within your areas of clinical competence solely because the patient is seropositive for human immunodeficiency virus,9 nor because of a patient’s race, religion, or other reasons that would constitute illegal discrimination.3 Doctors who practice in rural areas must be especially cautious about terminating care because their patients may have limited access to alternate care sources.10

 

Meeting with or verbally informing a patient of a termination may be reasonable in some cases, but appropriate unilateral termination of a patient usually requires providing written notification to the patient or person responsible for the patient’s care. Attorneys who specialize in risk management advise doctors to seek legal consultation when preparing a termination-of-care letter and to send it by certified mail. The letter should conform to any applicable rules or regulations where you practice. Typically, required content includes:

 

  • notification that the physician-patient relationship is terminated
  • a statement of willingness to provide emergency treatment and access to services for up to 30 days from the mailing date to allow the patient to arrange care from another provider
  • an offer to transfer records to the new provider upon receiving the patient’s signed authorization to do so.11
 

 

More discussion of the possible contents of termination letters appears in Table 3.7,12-14

Table 2

Common reasons to consider terminating a patient’s care

 

Failing to pay bills
Repeatedly cancelling or missing appointments
Repeatedly failing to follow the agreed-upon treatment plan
Overly demanding, rude, disruptive, threatening, or violent behavior toward staff or other patients
Patient is very dissatisfied with care
Needing specialized services that the physician cannot provide
Filing a complaint or legal action against the physician
Dishonesty that compromises safety or legality of treatment
Physician feels treatment is ineffective
Conflict of interest (eg, physician’s religious beliefs preclude providing certain treatments that might be indicated)
Developing and acting upon an inappropriate personal interest in the physician
Inappropriate response by physician to feelings about the patient (eg, physician feels tempted to act upon an attraction)
Source: References 7,8


Table 3

Potential elements of termination letters

 

ElementComment
Reason for terminationGiving a reason is not required. If an explanation seems necessary, offer a general statement (eg, ‘I have determined it would be best…’)
Adequate time to seek care elsewhereTypically, at least 30 days. Courts have described appropriate time frames in general terms, such as ‘ample,’ ‘sufficient,’ or ‘reasonable’
Interim care provisionsOffer interim care for urgent problems until the time limit stated above
Continued care provisions
  1. If the patient will need further care, state this clearly
  2. For patients who have been noncompliant, state clearly the possible consequences of not obtaining treatment
  3. Offer suggestions concerning places to seek evaluation for continued care
Medical record copiesOffer to provide a summary of treatment or copy of the record to a new provider. Consider enclosing a ‘release of information’ authorization to be returned to the office with the patient’s signature
Sending the letterRegular and certified mail (return receipt requested). Place a copy of this letter in the patient’s medical record, along with the original certified mail receipt and, if received, the original return receipt
Source: References 7,12-14

Deciding to ‘fire‘ a patient

 

Physicians in all specialties encounter patients whose actions generate intensely negative feelings—resentment, anger, even hate.15 But “firing” a patient should be a rare circumstance that’s not undertaken lightly. Many different circumstances can make it reasonable for a physician to consider terminating a patient’s care, so it’s difficult to provide general advice about when firing a patient really is the right thing to do. But 1 “prescription” seems clear: consult a respected colleague first. According to psychiatrist Robert Michels, “Any physician who is thinking of firing a patient should first speak to a colleague… This is an enormous decision and, while it might even be right at times, the physician is probably having a countertransference reaction to his patient and should really understand that before taking action.”1

 

Having an anonymous consultation with a colleague offers several potential benefits, such as:

 

  • If you’re thinking about firing a patient, you’re probably very upset. A colleague who isn’t emotionally involved can help you assess the matter more dispassionately.
  • You may be feeling guilty about disliking the patient. A colleague’s empathy (“Of course you’re angry!”) can help you avoid disowning your feelings, which may make it easier to figure out how to use those feelings to help the patient.15,16
  • A colleague may think of solutions that you haven’t considered, which might help you feel less frustrated about how treatment is going.
  • A colleague may help you see ways that you’re actually helping the patient, despite feeling that your work is futile.
  • If a thoughtful colleague confirms your view that terminating care is appropriate, you’ll feel better about the decision. If you document the anonymous consultation in the patient’s chart, you’ll create a record of your reasonableness and prudence—which will be helpful if you have to defend your action in court.12

Revisiting the case patients

With these thoughts in mind, we return to Dr. C’s clinical dilemmas.

Ms. A. In retrospect, Dr. C might wish he had been clearer with Ms. A about how often she would need to see him for medication monitoring. At this point, however, Dr. C still has options besides firing Ms. A:

 

  • Dr. C can call Ms. A to ask how she’s doing and to explain his medical responsibility to see and reassess her if he is to continue prescribing her medication. He can then follow up with a letter summarizing the conversation.
  • Dr. C might ask whether some problem is preventing Ms. A from making an appointment. If, for example, Ms. A has lost her job and health insurance coverage for office visits, Dr. C might suggest options (such as seeing Ms. A once at no charge) or help Ms. A find other ways to obtain follow-up care.
 

 

Mr. B. Concerning Mr. B, we wonder, “Why not just leave the chart open?” As is the case with care provided by other specialists—including internists, obstetricians, or dermatologists—psychiatric treatment may occur in discrete episodes over many years. Patients regard a previous care provider as “their doctor” for decades after a treatment episode, and it’s comforting and valuable for former patients to know they can see their “shrink” again if they need to.

Related Resource

 

Disclosure

The authors report no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.

References

 

1. Friedman RA. Should a doctor fire a patient? Sometimes it is good medicine. New York Times. September 27, 2005;sect F:1.

2. Dietz LH, Jacobs A, Leming TL, et al. Physicians, surgeons, and other healers, §§130, 216-218. In: American jurisprudence. Vol 61. 2nd ed. New York, NY: Thomson Reuters; 2010.

3. American Medical Association. Code of ethics. Opinions 9.12, 10.02, and 10.015. Available at: http://www.ama-assn.org/ama/pub/physician-resources/medical-ethics/code-medical-ethics.shtml. Accessed October 18, 2010.

4. Lowery v Miller, 157 Wis 2d 503, 460 NW2d 446 (Wis App 1990).

5. Crausman RS. Board of medical licensure and discipline. Available at: http://www.health.ri.gov/hsr/bmld. Accessed October 27, 2010.

6. Pellegrino ED. Nonabandonment: an old obligation revisited. Ann Intern Med. 1995;122:377-378.

7. Harris SM. Take care when firing a patient. American Medical News. Available at: http://www.ama-assn.org/amednews/2008/02/04/bica0204.htm. Accessed October 18, 2010.

8. Gabbard GO. Long-term psychodynamic psychotherapy: a basic text. Arlington, VA: American Psychiatric Publishing, Inc.; 2004.

9. Bragdon v Abbott, 524 U.S. 624 (1998).

10. Henderson SM. Advice on abandonment. Oklahoma Board of Medical Licensure and Supervision. Available at: http://www.okmedicalboard.org/download/19980401MD.htm. Accessed October 18, 2010.

11. Ohio Admin Code Ch, 4731-27(A)(1).

12. Appelbaum PS, Gutheil T. Clinical handbook of psychiatry and the law. 4th ed. Philadelphia, PA: Lippincott Williams and Wilkins; 2006.

13. Appelbaum PS. Law & psychiatry: can a psychiatrist be held responsible when a patient commits murder? Psychiatr Serv. 2002;53:27-29.

14. Tan MW, McDonough WJ. Risk management in psychiatry. Psychiatr Clin North Am. 1990;13:135-147.

15. Groves JE. Taking care of the hateful patient. N Engl J Med. 1978;298:883-887.

16. Strous RD, Ulman AM, Kotler M. The hateful patient revisited: relevance for 21st century medicine. Eur J Intern Med. 2006;17:387-393.

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Douglas Mossman, MD
Dr. Mossman is director, Glenn M. Weaver Institute of Law and Psychiatry, University of Cincinnati College of Law, and Adjunct Professor of Clinical Psychiatry and Training Director for the Forensic Psychiatry Fellowship.
Helen M. Farrell, MD
Dr. Farrell, Fellow in Forensic Psychiatry, University of Cincinnati College of Medicine.
Elizabeth Gilday, MD
Dr.Gilday, Fellow in Forensic Psychiatry, University of Cincinnati College of Medicine.

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Helen M. Farrell, MD
Dr. Farrell, Fellow in Forensic Psychiatry, University of Cincinnati College of Medicine.
Elizabeth Gilday, MD
Dr.Gilday, Fellow in Forensic Psychiatry, University of Cincinnati College of Medicine.

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Douglas Mossman, MD
Dr. Mossman is director, Glenn M. Weaver Institute of Law and Psychiatry, University of Cincinnati College of Law, and Adjunct Professor of Clinical Psychiatry and Training Director for the Forensic Psychiatry Fellowship.
Helen M. Farrell, MD
Dr. Farrell, Fellow in Forensic Psychiatry, University of Cincinnati College of Medicine.
Elizabeth Gilday, MD
Dr.Gilday, Fellow in Forensic Psychiatry, University of Cincinnati College of Medicine.

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Dear Dr. Mossman:
One of my patients, Ms. A, keeps calling in to refill her prescription, but will not come in for an appointment; she needs the medication, but I really shouldn’t keep prescribing it without seeing her. Another patient, Mr. B, has an open chart, but he stopped seeing me last year after I treated him for an acute depressive episode. May I “fire” these patients? If so, what should I do?—Submitted by “Dr. C”

All physicians occasionally encounter patients whom we’d like to stop treating, but because we feel devoted to those we treat, the idea of “firing” a patient makes us uncomfortable. Sometimes, however, ending a treatment relationship is the right choice for the doctor and patient.1

To know why, how, and when you may terminate your professional relationship with a patient, you need to:

 

  • understand the legal and ethical status of a doctor-patient relationship
  • know the proper way to end treatment relationships
  • decide whether ending your care of the patient is the right medical and ethical choice.

After exploring these points, we’ll return to the cases of Ms. A and Mr. B and consider what Dr. C might do.

 

DO YOU HAVE A QUESTION ABOUT POSSIBLE LIABILITY?

 

  • Submit your malpractice-related questions to Dr. Mossman at [email protected].
  • Include your name, address, and practice location. If your question is chosen for publication, your name can be withheld by request.

Doctor-patient relationships

Legal and medical authorities characterize the treatment relationship as an implicit contract that imposes certain obligations on the doctor and the patient.2,3 Doctors are compelled to conduct themselves in accordance with the prevailing “standard of care.” Patients’ obligations include being honest and cooperating with care once they have agreed to a treatment plan (Table 1).3

Patients may stop seeing their doctors at any time, but a physician usually must continue to provide all necessary medical attention until either the treatment episode has concluded or both parties agree to end the doctor-patient relationship.2 If a physician wishes to withdraw from a case before the need for services has ended, the physician must either make arrangements for another competent physician to assume care or give the patient ample notice and opportunity to obtain treatment elsewhere.2 If a doctor fails to do this and harm to the patient results, the doctor is guilty of “abandonment,” legally defined as termination of the physician-patient relationship “at an unreasonable time or without affording the patient the opportunity to procure an equally qualified replacement.”2 Physician abandonment can lead to malpractice liability,4 complaints to state licensing authorities,5 and ethical condemnation.6

Table 1

A patient’s responsibilities

 

Being truthful
Providing a complete medical history
Cooperating with agreed-upon treatment and keeping appointments
Meeting financial obligations for medical care
Health-enhancing behavior
Not participating in fraudulent health care
Source: Reference 3

Terminating without abandoning

Doctors commonly terminate care of their patients when they decide to move or close their practices. Accusations of abandonment may arise if such career decisions are executed improperly, but these matters are not as emotionally troubling for physicians as a decision to “fire” a patient because of the patient’s behavior. Common, legitimate reasons a doctor may consider unilateral termination appear in Table 2.7,8

Certain circumstances are not valid grounds for terminating a doctor-patient relationship. You cannot ethically decline to treat a patient whose problem lies within your areas of clinical competence solely because the patient is seropositive for human immunodeficiency virus,9 nor because of a patient’s race, religion, or other reasons that would constitute illegal discrimination.3 Doctors who practice in rural areas must be especially cautious about terminating care because their patients may have limited access to alternate care sources.10

 

Meeting with or verbally informing a patient of a termination may be reasonable in some cases, but appropriate unilateral termination of a patient usually requires providing written notification to the patient or person responsible for the patient’s care. Attorneys who specialize in risk management advise doctors to seek legal consultation when preparing a termination-of-care letter and to send it by certified mail. The letter should conform to any applicable rules or regulations where you practice. Typically, required content includes:

 

  • notification that the physician-patient relationship is terminated
  • a statement of willingness to provide emergency treatment and access to services for up to 30 days from the mailing date to allow the patient to arrange care from another provider
  • an offer to transfer records to the new provider upon receiving the patient’s signed authorization to do so.11
 

 

More discussion of the possible contents of termination letters appears in Table 3.7,12-14

Table 2

Common reasons to consider terminating a patient’s care

 

Failing to pay bills
Repeatedly cancelling or missing appointments
Repeatedly failing to follow the agreed-upon treatment plan
Overly demanding, rude, disruptive, threatening, or violent behavior toward staff or other patients
Patient is very dissatisfied with care
Needing specialized services that the physician cannot provide
Filing a complaint or legal action against the physician
Dishonesty that compromises safety or legality of treatment
Physician feels treatment is ineffective
Conflict of interest (eg, physician’s religious beliefs preclude providing certain treatments that might be indicated)
Developing and acting upon an inappropriate personal interest in the physician
Inappropriate response by physician to feelings about the patient (eg, physician feels tempted to act upon an attraction)
Source: References 7,8


Table 3

Potential elements of termination letters

 

ElementComment
Reason for terminationGiving a reason is not required. If an explanation seems necessary, offer a general statement (eg, ‘I have determined it would be best…’)
Adequate time to seek care elsewhereTypically, at least 30 days. Courts have described appropriate time frames in general terms, such as ‘ample,’ ‘sufficient,’ or ‘reasonable’
Interim care provisionsOffer interim care for urgent problems until the time limit stated above
Continued care provisions
  1. If the patient will need further care, state this clearly
  2. For patients who have been noncompliant, state clearly the possible consequences of not obtaining treatment
  3. Offer suggestions concerning places to seek evaluation for continued care
Medical record copiesOffer to provide a summary of treatment or copy of the record to a new provider. Consider enclosing a ‘release of information’ authorization to be returned to the office with the patient’s signature
Sending the letterRegular and certified mail (return receipt requested). Place a copy of this letter in the patient’s medical record, along with the original certified mail receipt and, if received, the original return receipt
Source: References 7,12-14

Deciding to ‘fire‘ a patient

 

Physicians in all specialties encounter patients whose actions generate intensely negative feelings—resentment, anger, even hate.15 But “firing” a patient should be a rare circumstance that’s not undertaken lightly. Many different circumstances can make it reasonable for a physician to consider terminating a patient’s care, so it’s difficult to provide general advice about when firing a patient really is the right thing to do. But 1 “prescription” seems clear: consult a respected colleague first. According to psychiatrist Robert Michels, “Any physician who is thinking of firing a patient should first speak to a colleague… This is an enormous decision and, while it might even be right at times, the physician is probably having a countertransference reaction to his patient and should really understand that before taking action.”1

 

Having an anonymous consultation with a colleague offers several potential benefits, such as:

 

  • If you’re thinking about firing a patient, you’re probably very upset. A colleague who isn’t emotionally involved can help you assess the matter more dispassionately.
  • You may be feeling guilty about disliking the patient. A colleague’s empathy (“Of course you’re angry!”) can help you avoid disowning your feelings, which may make it easier to figure out how to use those feelings to help the patient.15,16
  • A colleague may think of solutions that you haven’t considered, which might help you feel less frustrated about how treatment is going.
  • A colleague may help you see ways that you’re actually helping the patient, despite feeling that your work is futile.
  • If a thoughtful colleague confirms your view that terminating care is appropriate, you’ll feel better about the decision. If you document the anonymous consultation in the patient’s chart, you’ll create a record of your reasonableness and prudence—which will be helpful if you have to defend your action in court.12

Revisiting the case patients

With these thoughts in mind, we return to Dr. C’s clinical dilemmas.

Ms. A. In retrospect, Dr. C might wish he had been clearer with Ms. A about how often she would need to see him for medication monitoring. At this point, however, Dr. C still has options besides firing Ms. A:

 

  • Dr. C can call Ms. A to ask how she’s doing and to explain his medical responsibility to see and reassess her if he is to continue prescribing her medication. He can then follow up with a letter summarizing the conversation.
  • Dr. C might ask whether some problem is preventing Ms. A from making an appointment. If, for example, Ms. A has lost her job and health insurance coverage for office visits, Dr. C might suggest options (such as seeing Ms. A once at no charge) or help Ms. A find other ways to obtain follow-up care.
 

 

Mr. B. Concerning Mr. B, we wonder, “Why not just leave the chart open?” As is the case with care provided by other specialists—including internists, obstetricians, or dermatologists—psychiatric treatment may occur in discrete episodes over many years. Patients regard a previous care provider as “their doctor” for decades after a treatment episode, and it’s comforting and valuable for former patients to know they can see their “shrink” again if they need to.

Related Resource

 

Disclosure

The authors report no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.

Dear Dr. Mossman:
One of my patients, Ms. A, keeps calling in to refill her prescription, but will not come in for an appointment; she needs the medication, but I really shouldn’t keep prescribing it without seeing her. Another patient, Mr. B, has an open chart, but he stopped seeing me last year after I treated him for an acute depressive episode. May I “fire” these patients? If so, what should I do?—Submitted by “Dr. C”

All physicians occasionally encounter patients whom we’d like to stop treating, but because we feel devoted to those we treat, the idea of “firing” a patient makes us uncomfortable. Sometimes, however, ending a treatment relationship is the right choice for the doctor and patient.1

To know why, how, and when you may terminate your professional relationship with a patient, you need to:

 

  • understand the legal and ethical status of a doctor-patient relationship
  • know the proper way to end treatment relationships
  • decide whether ending your care of the patient is the right medical and ethical choice.

After exploring these points, we’ll return to the cases of Ms. A and Mr. B and consider what Dr. C might do.

 

DO YOU HAVE A QUESTION ABOUT POSSIBLE LIABILITY?

 

  • Submit your malpractice-related questions to Dr. Mossman at [email protected].
  • Include your name, address, and practice location. If your question is chosen for publication, your name can be withheld by request.

Doctor-patient relationships

Legal and medical authorities characterize the treatment relationship as an implicit contract that imposes certain obligations on the doctor and the patient.2,3 Doctors are compelled to conduct themselves in accordance with the prevailing “standard of care.” Patients’ obligations include being honest and cooperating with care once they have agreed to a treatment plan (Table 1).3

Patients may stop seeing their doctors at any time, but a physician usually must continue to provide all necessary medical attention until either the treatment episode has concluded or both parties agree to end the doctor-patient relationship.2 If a physician wishes to withdraw from a case before the need for services has ended, the physician must either make arrangements for another competent physician to assume care or give the patient ample notice and opportunity to obtain treatment elsewhere.2 If a doctor fails to do this and harm to the patient results, the doctor is guilty of “abandonment,” legally defined as termination of the physician-patient relationship “at an unreasonable time or without affording the patient the opportunity to procure an equally qualified replacement.”2 Physician abandonment can lead to malpractice liability,4 complaints to state licensing authorities,5 and ethical condemnation.6

Table 1

A patient’s responsibilities

 

Being truthful
Providing a complete medical history
Cooperating with agreed-upon treatment and keeping appointments
Meeting financial obligations for medical care
Health-enhancing behavior
Not participating in fraudulent health care
Source: Reference 3

Terminating without abandoning

Doctors commonly terminate care of their patients when they decide to move or close their practices. Accusations of abandonment may arise if such career decisions are executed improperly, but these matters are not as emotionally troubling for physicians as a decision to “fire” a patient because of the patient’s behavior. Common, legitimate reasons a doctor may consider unilateral termination appear in Table 2.7,8

Certain circumstances are not valid grounds for terminating a doctor-patient relationship. You cannot ethically decline to treat a patient whose problem lies within your areas of clinical competence solely because the patient is seropositive for human immunodeficiency virus,9 nor because of a patient’s race, religion, or other reasons that would constitute illegal discrimination.3 Doctors who practice in rural areas must be especially cautious about terminating care because their patients may have limited access to alternate care sources.10

 

Meeting with or verbally informing a patient of a termination may be reasonable in some cases, but appropriate unilateral termination of a patient usually requires providing written notification to the patient or person responsible for the patient’s care. Attorneys who specialize in risk management advise doctors to seek legal consultation when preparing a termination-of-care letter and to send it by certified mail. The letter should conform to any applicable rules or regulations where you practice. Typically, required content includes:

 

  • notification that the physician-patient relationship is terminated
  • a statement of willingness to provide emergency treatment and access to services for up to 30 days from the mailing date to allow the patient to arrange care from another provider
  • an offer to transfer records to the new provider upon receiving the patient’s signed authorization to do so.11
 

 

More discussion of the possible contents of termination letters appears in Table 3.7,12-14

Table 2

Common reasons to consider terminating a patient’s care

 

Failing to pay bills
Repeatedly cancelling or missing appointments
Repeatedly failing to follow the agreed-upon treatment plan
Overly demanding, rude, disruptive, threatening, or violent behavior toward staff or other patients
Patient is very dissatisfied with care
Needing specialized services that the physician cannot provide
Filing a complaint or legal action against the physician
Dishonesty that compromises safety or legality of treatment
Physician feels treatment is ineffective
Conflict of interest (eg, physician’s religious beliefs preclude providing certain treatments that might be indicated)
Developing and acting upon an inappropriate personal interest in the physician
Inappropriate response by physician to feelings about the patient (eg, physician feels tempted to act upon an attraction)
Source: References 7,8


Table 3

Potential elements of termination letters

 

ElementComment
Reason for terminationGiving a reason is not required. If an explanation seems necessary, offer a general statement (eg, ‘I have determined it would be best…’)
Adequate time to seek care elsewhereTypically, at least 30 days. Courts have described appropriate time frames in general terms, such as ‘ample,’ ‘sufficient,’ or ‘reasonable’
Interim care provisionsOffer interim care for urgent problems until the time limit stated above
Continued care provisions
  1. If the patient will need further care, state this clearly
  2. For patients who have been noncompliant, state clearly the possible consequences of not obtaining treatment
  3. Offer suggestions concerning places to seek evaluation for continued care
Medical record copiesOffer to provide a summary of treatment or copy of the record to a new provider. Consider enclosing a ‘release of information’ authorization to be returned to the office with the patient’s signature
Sending the letterRegular and certified mail (return receipt requested). Place a copy of this letter in the patient’s medical record, along with the original certified mail receipt and, if received, the original return receipt
Source: References 7,12-14

Deciding to ‘fire‘ a patient

 

Physicians in all specialties encounter patients whose actions generate intensely negative feelings—resentment, anger, even hate.15 But “firing” a patient should be a rare circumstance that’s not undertaken lightly. Many different circumstances can make it reasonable for a physician to consider terminating a patient’s care, so it’s difficult to provide general advice about when firing a patient really is the right thing to do. But 1 “prescription” seems clear: consult a respected colleague first. According to psychiatrist Robert Michels, “Any physician who is thinking of firing a patient should first speak to a colleague… This is an enormous decision and, while it might even be right at times, the physician is probably having a countertransference reaction to his patient and should really understand that before taking action.”1

 

Having an anonymous consultation with a colleague offers several potential benefits, such as:

 

  • If you’re thinking about firing a patient, you’re probably very upset. A colleague who isn’t emotionally involved can help you assess the matter more dispassionately.
  • You may be feeling guilty about disliking the patient. A colleague’s empathy (“Of course you’re angry!”) can help you avoid disowning your feelings, which may make it easier to figure out how to use those feelings to help the patient.15,16
  • A colleague may think of solutions that you haven’t considered, which might help you feel less frustrated about how treatment is going.
  • A colleague may help you see ways that you’re actually helping the patient, despite feeling that your work is futile.
  • If a thoughtful colleague confirms your view that terminating care is appropriate, you’ll feel better about the decision. If you document the anonymous consultation in the patient’s chart, you’ll create a record of your reasonableness and prudence—which will be helpful if you have to defend your action in court.12

Revisiting the case patients

With these thoughts in mind, we return to Dr. C’s clinical dilemmas.

Ms. A. In retrospect, Dr. C might wish he had been clearer with Ms. A about how often she would need to see him for medication monitoring. At this point, however, Dr. C still has options besides firing Ms. A:

 

  • Dr. C can call Ms. A to ask how she’s doing and to explain his medical responsibility to see and reassess her if he is to continue prescribing her medication. He can then follow up with a letter summarizing the conversation.
  • Dr. C might ask whether some problem is preventing Ms. A from making an appointment. If, for example, Ms. A has lost her job and health insurance coverage for office visits, Dr. C might suggest options (such as seeing Ms. A once at no charge) or help Ms. A find other ways to obtain follow-up care.
 

 

Mr. B. Concerning Mr. B, we wonder, “Why not just leave the chart open?” As is the case with care provided by other specialists—including internists, obstetricians, or dermatologists—psychiatric treatment may occur in discrete episodes over many years. Patients regard a previous care provider as “their doctor” for decades after a treatment episode, and it’s comforting and valuable for former patients to know they can see their “shrink” again if they need to.

Related Resource

 

Disclosure

The authors report no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.

References

 

1. Friedman RA. Should a doctor fire a patient? Sometimes it is good medicine. New York Times. September 27, 2005;sect F:1.

2. Dietz LH, Jacobs A, Leming TL, et al. Physicians, surgeons, and other healers, §§130, 216-218. In: American jurisprudence. Vol 61. 2nd ed. New York, NY: Thomson Reuters; 2010.

3. American Medical Association. Code of ethics. Opinions 9.12, 10.02, and 10.015. Available at: http://www.ama-assn.org/ama/pub/physician-resources/medical-ethics/code-medical-ethics.shtml. Accessed October 18, 2010.

4. Lowery v Miller, 157 Wis 2d 503, 460 NW2d 446 (Wis App 1990).

5. Crausman RS. Board of medical licensure and discipline. Available at: http://www.health.ri.gov/hsr/bmld. Accessed October 27, 2010.

6. Pellegrino ED. Nonabandonment: an old obligation revisited. Ann Intern Med. 1995;122:377-378.

7. Harris SM. Take care when firing a patient. American Medical News. Available at: http://www.ama-assn.org/amednews/2008/02/04/bica0204.htm. Accessed October 18, 2010.

8. Gabbard GO. Long-term psychodynamic psychotherapy: a basic text. Arlington, VA: American Psychiatric Publishing, Inc.; 2004.

9. Bragdon v Abbott, 524 U.S. 624 (1998).

10. Henderson SM. Advice on abandonment. Oklahoma Board of Medical Licensure and Supervision. Available at: http://www.okmedicalboard.org/download/19980401MD.htm. Accessed October 18, 2010.

11. Ohio Admin Code Ch, 4731-27(A)(1).

12. Appelbaum PS, Gutheil T. Clinical handbook of psychiatry and the law. 4th ed. Philadelphia, PA: Lippincott Williams and Wilkins; 2006.

13. Appelbaum PS. Law & psychiatry: can a psychiatrist be held responsible when a patient commits murder? Psychiatr Serv. 2002;53:27-29.

14. Tan MW, McDonough WJ. Risk management in psychiatry. Psychiatr Clin North Am. 1990;13:135-147.

15. Groves JE. Taking care of the hateful patient. N Engl J Med. 1978;298:883-887.

16. Strous RD, Ulman AM, Kotler M. The hateful patient revisited: relevance for 21st century medicine. Eur J Intern Med. 2006;17:387-393.

References

 

1. Friedman RA. Should a doctor fire a patient? Sometimes it is good medicine. New York Times. September 27, 2005;sect F:1.

2. Dietz LH, Jacobs A, Leming TL, et al. Physicians, surgeons, and other healers, §§130, 216-218. In: American jurisprudence. Vol 61. 2nd ed. New York, NY: Thomson Reuters; 2010.

3. American Medical Association. Code of ethics. Opinions 9.12, 10.02, and 10.015. Available at: http://www.ama-assn.org/ama/pub/physician-resources/medical-ethics/code-medical-ethics.shtml. Accessed October 18, 2010.

4. Lowery v Miller, 157 Wis 2d 503, 460 NW2d 446 (Wis App 1990).

5. Crausman RS. Board of medical licensure and discipline. Available at: http://www.health.ri.gov/hsr/bmld. Accessed October 27, 2010.

6. Pellegrino ED. Nonabandonment: an old obligation revisited. Ann Intern Med. 1995;122:377-378.

7. Harris SM. Take care when firing a patient. American Medical News. Available at: http://www.ama-assn.org/amednews/2008/02/04/bica0204.htm. Accessed October 18, 2010.

8. Gabbard GO. Long-term psychodynamic psychotherapy: a basic text. Arlington, VA: American Psychiatric Publishing, Inc.; 2004.

9. Bragdon v Abbott, 524 U.S. 624 (1998).

10. Henderson SM. Advice on abandonment. Oklahoma Board of Medical Licensure and Supervision. Available at: http://www.okmedicalboard.org/download/19980401MD.htm. Accessed October 18, 2010.

11. Ohio Admin Code Ch, 4731-27(A)(1).

12. Appelbaum PS, Gutheil T. Clinical handbook of psychiatry and the law. 4th ed. Philadelphia, PA: Lippincott Williams and Wilkins; 2006.

13. Appelbaum PS. Law & psychiatry: can a psychiatrist be held responsible when a patient commits murder? Psychiatr Serv. 2002;53:27-29.

14. Tan MW, McDonough WJ. Risk management in psychiatry. Psychiatr Clin North Am. 1990;13:135-147.

15. Groves JE. Taking care of the hateful patient. N Engl J Med. 1978;298:883-887.

16. Strous RD, Ulman AM, Kotler M. The hateful patient revisited: relevance for 21st century medicine. Eur J Intern Med. 2006;17:387-393.

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Identify and Treat Vitamin D Insufficiency

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Many children and teenagers are not getting enough vitamin D, and this is a common problem.

I started seeing medical consequences years ago. We did a study of breastfed African American infants who developed rickets because they were so deficient in vitamin D (J. Pediatrics 2000;137:153-7). Based on these data, North Carolina Women, Infants, and Children Program clinics started distributing free multivitamin supplements to breastfeeding infants, and now we see fewer cases in our area.

What is new is that we are seeing the adverse effects of vitamin D deficiency in older kids. We've had vitamin D–deficient teenagers present with seizures, and we see osteoporosis in teenagers. We had one adolescent who broke his hip from just twisting around who was found to have a severe deficiency of vitamin D.

This is a preventable problem. But about 20% of children and teenagers get the recommended amounts of calcium and vitamin D.

Who do you screen for deficiency? We screen in our clinic with one useful question to ask parents and our kids: “How many glasses of milk do you drink a day?” For those who respond one glass or less, we check their serum vitamin D level.

When taking a history, determine if patients take medications that might interfere with vitamin D metabolism. For example, some antiseizure medicines cause vitamin D to break down more quickly. Obviously, if a child has a severe seizure disorder, that individual needs seizure medicine. But you may need to check the vitamin D level and increase supplementation accordingly.

We found 75% of 48 kids with a history of inadequate milk intake had a vitamin D level below 30 ng/mL; 30% of the children had deficient levels (below 20 ng/mL).

The key is prevention. I recommend that infants start drinking vitamin D–fortified milk after they wean off breast milk or formula. We are finding that a tremendous number of kids do not drink milk, and they go directly from the breast or formula to juice and soda. I tell them to drink a glass of milk with their breakfast, lunch, and supper, hoping they will get at least two glasses per day. I accept chocolate milk. It's not ideal, but it's better than no milk at all.

Make sure your patients get adequate calcium in their diet – about 700 mg/day for a young child anda1,200-1,300 mg for an adolescent. Recommend a multivitamin –, which typically has a minimum of 400 U of vitamin D – as well as a calcium supplement. This combination is important because vitamin D facilitates absorption of calcium from the gut.

I recommend milk with 1% or less fat. A lot of people believe that whole milk provides more vitamin D, but that is not the case. If you have lactose-intolerant patients, suggest that they drink a soy milk product fortified with vitamin D.

A good way to explain the importance of vitamin D to kids and parents is that we build up our bones until we're about 20 After that, if we live long enough, we're going to slide down the hill – we will all get osteoporosis. But if you've had your milk and followed recommendations, you'll start your slide downward from the top of the mountain.

In addition to those with poor diets, patients with chronic disease, with malabsorption, or who are confined to the indoors are at higher risk for vitamin D deficiency. If they are severely deficient, you will need to use higher doses of vitamin D or refer them to a specialist.

Specialists cannot see all these patients because vitamin D insufficiency is so common. It's becoming like obesity. We need the general pediatrician's help to screen and treat most of these patients. We can help via phone consultation or referral for severely deficient patients, particularly those who experience a seizure or multiple fractures because of their deficiency.

To diagnose a suspected deficiency of vitamin D, order a serum 25-hydroxyvitamin D level. With that you might want to get a calcium and phosphorus level and an alkaline phosphatase assay (a measure of bone formation).

Do not order a 1,25-dihydroxyvitamin D test. It is easily ordered by mistake with electronic test ordering. But the 1,25 form does not reflect true vitamin D sufficiency or insufficiency, and can confuse clinical diagnosis.

I do not recommend bone density measurements (such as dual-energy x-ray absorptiometry, or DXA) because many of these scans can be misread. You want to take a history, get a vitamin D level, and treat. Otherwise, you are just going to run up medical expenses.

 

 

No column on vitamin D would be complete without addressing sunlight. Ultraviolet light from sun exposuer week of sunshine if they are wearing only diapers, or 2 hours per week if fully clothed. That evidently provides an adequawe recommend sunscreen for people who are outdoors for more than just a short period. If children and teenagers are really out in the summer, when most get exposure, they're using sunscreens that block 95% of the rays. So we have become dependent on diet for our vitamin D, and we're not getting it.

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Many children and teenagers are not getting enough vitamin D, and this is a common problem.

I started seeing medical consequences years ago. We did a study of breastfed African American infants who developed rickets because they were so deficient in vitamin D (J. Pediatrics 2000;137:153-7). Based on these data, North Carolina Women, Infants, and Children Program clinics started distributing free multivitamin supplements to breastfeeding infants, and now we see fewer cases in our area.

What is new is that we are seeing the adverse effects of vitamin D deficiency in older kids. We've had vitamin D–deficient teenagers present with seizures, and we see osteoporosis in teenagers. We had one adolescent who broke his hip from just twisting around who was found to have a severe deficiency of vitamin D.

This is a preventable problem. But about 20% of children and teenagers get the recommended amounts of calcium and vitamin D.

Who do you screen for deficiency? We screen in our clinic with one useful question to ask parents and our kids: “How many glasses of milk do you drink a day?” For those who respond one glass or less, we check their serum vitamin D level.

When taking a history, determine if patients take medications that might interfere with vitamin D metabolism. For example, some antiseizure medicines cause vitamin D to break down more quickly. Obviously, if a child has a severe seizure disorder, that individual needs seizure medicine. But you may need to check the vitamin D level and increase supplementation accordingly.

We found 75% of 48 kids with a history of inadequate milk intake had a vitamin D level below 30 ng/mL; 30% of the children had deficient levels (below 20 ng/mL).

The key is prevention. I recommend that infants start drinking vitamin D–fortified milk after they wean off breast milk or formula. We are finding that a tremendous number of kids do not drink milk, and they go directly from the breast or formula to juice and soda. I tell them to drink a glass of milk with their breakfast, lunch, and supper, hoping they will get at least two glasses per day. I accept chocolate milk. It's not ideal, but it's better than no milk at all.

Make sure your patients get adequate calcium in their diet – about 700 mg/day for a young child anda1,200-1,300 mg for an adolescent. Recommend a multivitamin –, which typically has a minimum of 400 U of vitamin D – as well as a calcium supplement. This combination is important because vitamin D facilitates absorption of calcium from the gut.

I recommend milk with 1% or less fat. A lot of people believe that whole milk provides more vitamin D, but that is not the case. If you have lactose-intolerant patients, suggest that they drink a soy milk product fortified with vitamin D.

A good way to explain the importance of vitamin D to kids and parents is that we build up our bones until we're about 20 After that, if we live long enough, we're going to slide down the hill – we will all get osteoporosis. But if you've had your milk and followed recommendations, you'll start your slide downward from the top of the mountain.

In addition to those with poor diets, patients with chronic disease, with malabsorption, or who are confined to the indoors are at higher risk for vitamin D deficiency. If they are severely deficient, you will need to use higher doses of vitamin D or refer them to a specialist.

Specialists cannot see all these patients because vitamin D insufficiency is so common. It's becoming like obesity. We need the general pediatrician's help to screen and treat most of these patients. We can help via phone consultation or referral for severely deficient patients, particularly those who experience a seizure or multiple fractures because of their deficiency.

To diagnose a suspected deficiency of vitamin D, order a serum 25-hydroxyvitamin D level. With that you might want to get a calcium and phosphorus level and an alkaline phosphatase assay (a measure of bone formation).

Do not order a 1,25-dihydroxyvitamin D test. It is easily ordered by mistake with electronic test ordering. But the 1,25 form does not reflect true vitamin D sufficiency or insufficiency, and can confuse clinical diagnosis.

I do not recommend bone density measurements (such as dual-energy x-ray absorptiometry, or DXA) because many of these scans can be misread. You want to take a history, get a vitamin D level, and treat. Otherwise, you are just going to run up medical expenses.

 

 

No column on vitamin D would be complete without addressing sunlight. Ultraviolet light from sun exposuer week of sunshine if they are wearing only diapers, or 2 hours per week if fully clothed. That evidently provides an adequawe recommend sunscreen for people who are outdoors for more than just a short period. If children and teenagers are really out in the summer, when most get exposure, they're using sunscreens that block 95% of the rays. So we have become dependent on diet for our vitamin D, and we're not getting it.

Many children and teenagers are not getting enough vitamin D, and this is a common problem.

I started seeing medical consequences years ago. We did a study of breastfed African American infants who developed rickets because they were so deficient in vitamin D (J. Pediatrics 2000;137:153-7). Based on these data, North Carolina Women, Infants, and Children Program clinics started distributing free multivitamin supplements to breastfeeding infants, and now we see fewer cases in our area.

What is new is that we are seeing the adverse effects of vitamin D deficiency in older kids. We've had vitamin D–deficient teenagers present with seizures, and we see osteoporosis in teenagers. We had one adolescent who broke his hip from just twisting around who was found to have a severe deficiency of vitamin D.

This is a preventable problem. But about 20% of children and teenagers get the recommended amounts of calcium and vitamin D.

Who do you screen for deficiency? We screen in our clinic with one useful question to ask parents and our kids: “How many glasses of milk do you drink a day?” For those who respond one glass or less, we check their serum vitamin D level.

When taking a history, determine if patients take medications that might interfere with vitamin D metabolism. For example, some antiseizure medicines cause vitamin D to break down more quickly. Obviously, if a child has a severe seizure disorder, that individual needs seizure medicine. But you may need to check the vitamin D level and increase supplementation accordingly.

We found 75% of 48 kids with a history of inadequate milk intake had a vitamin D level below 30 ng/mL; 30% of the children had deficient levels (below 20 ng/mL).

The key is prevention. I recommend that infants start drinking vitamin D–fortified milk after they wean off breast milk or formula. We are finding that a tremendous number of kids do not drink milk, and they go directly from the breast or formula to juice and soda. I tell them to drink a glass of milk with their breakfast, lunch, and supper, hoping they will get at least two glasses per day. I accept chocolate milk. It's not ideal, but it's better than no milk at all.

Make sure your patients get adequate calcium in their diet – about 700 mg/day for a young child anda1,200-1,300 mg for an adolescent. Recommend a multivitamin –, which typically has a minimum of 400 U of vitamin D – as well as a calcium supplement. This combination is important because vitamin D facilitates absorption of calcium from the gut.

I recommend milk with 1% or less fat. A lot of people believe that whole milk provides more vitamin D, but that is not the case. If you have lactose-intolerant patients, suggest that they drink a soy milk product fortified with vitamin D.

A good way to explain the importance of vitamin D to kids and parents is that we build up our bones until we're about 20 After that, if we live long enough, we're going to slide down the hill – we will all get osteoporosis. But if you've had your milk and followed recommendations, you'll start your slide downward from the top of the mountain.

In addition to those with poor diets, patients with chronic disease, with malabsorption, or who are confined to the indoors are at higher risk for vitamin D deficiency. If they are severely deficient, you will need to use higher doses of vitamin D or refer them to a specialist.

Specialists cannot see all these patients because vitamin D insufficiency is so common. It's becoming like obesity. We need the general pediatrician's help to screen and treat most of these patients. We can help via phone consultation or referral for severely deficient patients, particularly those who experience a seizure or multiple fractures because of their deficiency.

To diagnose a suspected deficiency of vitamin D, order a serum 25-hydroxyvitamin D level. With that you might want to get a calcium and phosphorus level and an alkaline phosphatase assay (a measure of bone formation).

Do not order a 1,25-dihydroxyvitamin D test. It is easily ordered by mistake with electronic test ordering. But the 1,25 form does not reflect true vitamin D sufficiency or insufficiency, and can confuse clinical diagnosis.

I do not recommend bone density measurements (such as dual-energy x-ray absorptiometry, or DXA) because many of these scans can be misread. You want to take a history, get a vitamin D level, and treat. Otherwise, you are just going to run up medical expenses.

 

 

No column on vitamin D would be complete without addressing sunlight. Ultraviolet light from sun exposuer week of sunshine if they are wearing only diapers, or 2 hours per week if fully clothed. That evidently provides an adequawe recommend sunscreen for people who are outdoors for more than just a short period. If children and teenagers are really out in the summer, when most get exposure, they're using sunscreens that block 95% of the rays. So we have become dependent on diet for our vitamin D, and we're not getting it.

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Adolescent substance use is a big, difficult issue. Alcohol, cigarettes, and marijuana – and to a lesser extent, cocaine – are endemic to most high schools.

It's not at all unusual for high school juniors and seniors to drink or even to binge drink. In addition, about 15%-20% of them smoke cigarettes, depending on the community, and probably more than that try or intermittently smoke marijuana.

One approach is to educate parents about which children may be at higher risk for substance use.

For example, most teenagers are at some risk and need to be watched for any high-risk activities, especially drinking and driving or riding in a car with a friend who has been drinking. If parents begin to see a serious problem – such as binge drinking every weekend, obvious use of marijuana on a regular basis, or obvious use of cocaine – take it very seriously. Talk with patients and parents, and provide a referral for services as indicated.

Other kids might be at slightly higher risk in high school, based on their achievements. A good athlete who joins the varsity team in 9th or 10th grade, or the talented 9th-grader who lands the lead in the high school play, tend to spend more time with juniors and seniors. They get invited to parties and events outside their peer groups. Without the judgment of an older child and while trying to “keep up,” they might be more vulnerable to problematic substance use. The difference between a 14-year-old and a 17-year-old is enormous, and the peer pressure of being with seniors is considerable.

Recognize that some kids start high school already predisposed and at quite high risk for substance use problems: A patient with biologic or genetic risk factors; a patient with untreated depression or anxiety; and/or an adolescent with attention-deficit/hyperactivity disorder (ADHD) are examples.

Left unaddressed, these kids are predisposed to earlier and more serious substance use. Some children with genetic and/or biological risk factors begin drinking heavily before their 14th birthday. In contrast, the typical age of onset for alcohol use includes some experimentation at 15 or 16 years that becomes binge drinking for some a year or two later.

Biology predisposes some adolescents to nicotine addiction or heavy use of marijuana or alcohol. While adolescent brains are in development and experience the expected stress of puberty and building an identity, some teenagers' brains may be more susceptible to addiction than others. In addition, genetics and environment can play a role, evidenced by the higher risks for children whose parents have a personal or family history of substance use problems. If there is a strong history of alcoholism in the family or if a parent is a recovering alcoholic, discuss with parents how their past might influence how they treat their teenager. Advise them what information should be shared to alert the teenager to the potential risks. It might help a child at age 12 or 13 to know that he or she may be especially vulnerable to the dangers of substance use, and this may well open up an avenue of communication and trust that could be helpful later.

Anxiety and depression also have genetic roots in some patients. Asking parents about their family history of substance abuse, depression, or serious mental health disorder should be a routine part of pediatric practice.

In addition, implementing screening tests makes sense in adolescence. Such screening tests are publicly available and reviewed by the American Academy of Pediatrics Bright Futures: Mental Health effort (http://brightfutures.aap.org/3rd_Edition_Guidelines_and_Pocket_Guide.htmlwww.aap.org/pcorss/demos/mht.html

Children with ADHD make up another high-risk group. They also seem to be more vulnerable to cigarettes, alcohol, and marijuana. These teenagers can be driven to find relief in these substances because of brain biology and/or secondary to the stress of living with their ADHD symptoms.

On the plus side, ADHD kids treated appropriately with stimulants and support services have lower levels of substance use and probably higher self-esteem. Ensure they have the best treatment possible for their ADHD to minimize their substance use risk as much as possible.

As they become young teenagers, they should have a full review of all their ADHD treatment and how their typical day plays out at school and at home. Consider some additional preventative counseling to help these children face their substance use challenges throughout adolescence.

Offer such guidance to all children and parents, but especially to those in one of these high-risk groups. Suggest strategies that support and reinforce their resiliency. A warm, positive relationship with an adult is a protective factor. Also, kids who feel connected to a school, church, or sports team tend to be a little more resilient to some of these influences.

 

 

Facilitate open, honest communication between the parent and adolescent. Many parents might not realize that this is more effective than are attempts to control their teen's behavior 24/7. Over-control is impossible given the lifestyle of most 15-, 16- or 17-year-olds. Between cell phones, cars, and the amount of time they are out of the house, parents cannot control them to a level of stopping all substance use.

Even if more control is possible, the effort works against the adolescent's developmental trajectory toward increasing autonomy. The real goal of adolescence in our culture is to learn how to live in the real world and to prepare for autonomy as young adults. High school is a preparation for college life and adulthood where good judgment, safety, and socialization are important learned behaviors. Teaching adolescents how to navigate all this is an essential role for physicians and parents.

Part of growing autonomy is privacy. While it is tempting to invade that privacy to learn about substance use or to clarify other concerns, it is not clear that such efforts support the long-term goals of raising a teenager. Such efforts at control and investigation, including blood or urine testing, are best reserved when the benefits outweigh the risks, when there is a substance abuse problem that needs to be addressed.

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Adolescent substance use is a big, difficult issue. Alcohol, cigarettes, and marijuana – and to a lesser extent, cocaine – are endemic to most high schools.

It's not at all unusual for high school juniors and seniors to drink or even to binge drink. In addition, about 15%-20% of them smoke cigarettes, depending on the community, and probably more than that try or intermittently smoke marijuana.

One approach is to educate parents about which children may be at higher risk for substance use.

For example, most teenagers are at some risk and need to be watched for any high-risk activities, especially drinking and driving or riding in a car with a friend who has been drinking. If parents begin to see a serious problem – such as binge drinking every weekend, obvious use of marijuana on a regular basis, or obvious use of cocaine – take it very seriously. Talk with patients and parents, and provide a referral for services as indicated.

Other kids might be at slightly higher risk in high school, based on their achievements. A good athlete who joins the varsity team in 9th or 10th grade, or the talented 9th-grader who lands the lead in the high school play, tend to spend more time with juniors and seniors. They get invited to parties and events outside their peer groups. Without the judgment of an older child and while trying to “keep up,” they might be more vulnerable to problematic substance use. The difference between a 14-year-old and a 17-year-old is enormous, and the peer pressure of being with seniors is considerable.

Recognize that some kids start high school already predisposed and at quite high risk for substance use problems: A patient with biologic or genetic risk factors; a patient with untreated depression or anxiety; and/or an adolescent with attention-deficit/hyperactivity disorder (ADHD) are examples.

Left unaddressed, these kids are predisposed to earlier and more serious substance use. Some children with genetic and/or biological risk factors begin drinking heavily before their 14th birthday. In contrast, the typical age of onset for alcohol use includes some experimentation at 15 or 16 years that becomes binge drinking for some a year or two later.

Biology predisposes some adolescents to nicotine addiction or heavy use of marijuana or alcohol. While adolescent brains are in development and experience the expected stress of puberty and building an identity, some teenagers' brains may be more susceptible to addiction than others. In addition, genetics and environment can play a role, evidenced by the higher risks for children whose parents have a personal or family history of substance use problems. If there is a strong history of alcoholism in the family or if a parent is a recovering alcoholic, discuss with parents how their past might influence how they treat their teenager. Advise them what information should be shared to alert the teenager to the potential risks. It might help a child at age 12 or 13 to know that he or she may be especially vulnerable to the dangers of substance use, and this may well open up an avenue of communication and trust that could be helpful later.

Anxiety and depression also have genetic roots in some patients. Asking parents about their family history of substance abuse, depression, or serious mental health disorder should be a routine part of pediatric practice.

In addition, implementing screening tests makes sense in adolescence. Such screening tests are publicly available and reviewed by the American Academy of Pediatrics Bright Futures: Mental Health effort (http://brightfutures.aap.org/3rd_Edition_Guidelines_and_Pocket_Guide.htmlwww.aap.org/pcorss/demos/mht.html

Children with ADHD make up another high-risk group. They also seem to be more vulnerable to cigarettes, alcohol, and marijuana. These teenagers can be driven to find relief in these substances because of brain biology and/or secondary to the stress of living with their ADHD symptoms.

On the plus side, ADHD kids treated appropriately with stimulants and support services have lower levels of substance use and probably higher self-esteem. Ensure they have the best treatment possible for their ADHD to minimize their substance use risk as much as possible.

As they become young teenagers, they should have a full review of all their ADHD treatment and how their typical day plays out at school and at home. Consider some additional preventative counseling to help these children face their substance use challenges throughout adolescence.

Offer such guidance to all children and parents, but especially to those in one of these high-risk groups. Suggest strategies that support and reinforce their resiliency. A warm, positive relationship with an adult is a protective factor. Also, kids who feel connected to a school, church, or sports team tend to be a little more resilient to some of these influences.

 

 

Facilitate open, honest communication between the parent and adolescent. Many parents might not realize that this is more effective than are attempts to control their teen's behavior 24/7. Over-control is impossible given the lifestyle of most 15-, 16- or 17-year-olds. Between cell phones, cars, and the amount of time they are out of the house, parents cannot control them to a level of stopping all substance use.

Even if more control is possible, the effort works against the adolescent's developmental trajectory toward increasing autonomy. The real goal of adolescence in our culture is to learn how to live in the real world and to prepare for autonomy as young adults. High school is a preparation for college life and adulthood where good judgment, safety, and socialization are important learned behaviors. Teaching adolescents how to navigate all this is an essential role for physicians and parents.

Part of growing autonomy is privacy. While it is tempting to invade that privacy to learn about substance use or to clarify other concerns, it is not clear that such efforts support the long-term goals of raising a teenager. Such efforts at control and investigation, including blood or urine testing, are best reserved when the benefits outweigh the risks, when there is a substance abuse problem that needs to be addressed.

 

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Adolescent substance use is a big, difficult issue. Alcohol, cigarettes, and marijuana – and to a lesser extent, cocaine – are endemic to most high schools.

It's not at all unusual for high school juniors and seniors to drink or even to binge drink. In addition, about 15%-20% of them smoke cigarettes, depending on the community, and probably more than that try or intermittently smoke marijuana.

One approach is to educate parents about which children may be at higher risk for substance use.

For example, most teenagers are at some risk and need to be watched for any high-risk activities, especially drinking and driving or riding in a car with a friend who has been drinking. If parents begin to see a serious problem – such as binge drinking every weekend, obvious use of marijuana on a regular basis, or obvious use of cocaine – take it very seriously. Talk with patients and parents, and provide a referral for services as indicated.

Other kids might be at slightly higher risk in high school, based on their achievements. A good athlete who joins the varsity team in 9th or 10th grade, or the talented 9th-grader who lands the lead in the high school play, tend to spend more time with juniors and seniors. They get invited to parties and events outside their peer groups. Without the judgment of an older child and while trying to “keep up,” they might be more vulnerable to problematic substance use. The difference between a 14-year-old and a 17-year-old is enormous, and the peer pressure of being with seniors is considerable.

Recognize that some kids start high school already predisposed and at quite high risk for substance use problems: A patient with biologic or genetic risk factors; a patient with untreated depression or anxiety; and/or an adolescent with attention-deficit/hyperactivity disorder (ADHD) are examples.

Left unaddressed, these kids are predisposed to earlier and more serious substance use. Some children with genetic and/or biological risk factors begin drinking heavily before their 14th birthday. In contrast, the typical age of onset for alcohol use includes some experimentation at 15 or 16 years that becomes binge drinking for some a year or two later.

Biology predisposes some adolescents to nicotine addiction or heavy use of marijuana or alcohol. While adolescent brains are in development and experience the expected stress of puberty and building an identity, some teenagers' brains may be more susceptible to addiction than others. In addition, genetics and environment can play a role, evidenced by the higher risks for children whose parents have a personal or family history of substance use problems. If there is a strong history of alcoholism in the family or if a parent is a recovering alcoholic, discuss with parents how their past might influence how they treat their teenager. Advise them what information should be shared to alert the teenager to the potential risks. It might help a child at age 12 or 13 to know that he or she may be especially vulnerable to the dangers of substance use, and this may well open up an avenue of communication and trust that could be helpful later.

Anxiety and depression also have genetic roots in some patients. Asking parents about their family history of substance abuse, depression, or serious mental health disorder should be a routine part of pediatric practice.

In addition, implementing screening tests makes sense in adolescence. Such screening tests are publicly available and reviewed by the American Academy of Pediatrics Bright Futures: Mental Health effort (http://brightfutures.aap.org/3rd_Edition_Guidelines_and_Pocket_Guide.htmlwww.aap.org/pcorss/demos/mht.html

Children with ADHD make up another high-risk group. They also seem to be more vulnerable to cigarettes, alcohol, and marijuana. These teenagers can be driven to find relief in these substances because of brain biology and/or secondary to the stress of living with their ADHD symptoms.

On the plus side, ADHD kids treated appropriately with stimulants and support services have lower levels of substance use and probably higher self-esteem. Ensure they have the best treatment possible for their ADHD to minimize their substance use risk as much as possible.

As they become young teenagers, they should have a full review of all their ADHD treatment and how their typical day plays out at school and at home. Consider some additional preventative counseling to help these children face their substance use challenges throughout adolescence.

Offer such guidance to all children and parents, but especially to those in one of these high-risk groups. Suggest strategies that support and reinforce their resiliency. A warm, positive relationship with an adult is a protective factor. Also, kids who feel connected to a school, church, or sports team tend to be a little more resilient to some of these influences.

 

 

Facilitate open, honest communication between the parent and adolescent. Many parents might not realize that this is more effective than are attempts to control their teen's behavior 24/7. Over-control is impossible given the lifestyle of most 15-, 16- or 17-year-olds. Between cell phones, cars, and the amount of time they are out of the house, parents cannot control them to a level of stopping all substance use.

Even if more control is possible, the effort works against the adolescent's developmental trajectory toward increasing autonomy. The real goal of adolescence in our culture is to learn how to live in the real world and to prepare for autonomy as young adults. High school is a preparation for college life and adulthood where good judgment, safety, and socialization are important learned behaviors. Teaching adolescents how to navigate all this is an essential role for physicians and parents.

Part of growing autonomy is privacy. While it is tempting to invade that privacy to learn about substance use or to clarify other concerns, it is not clear that such efforts support the long-term goals of raising a teenager. Such efforts at control and investigation, including blood or urine testing, are best reserved when the benefits outweigh the risks, when there is a substance abuse problem that needs to be addressed.

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BCBS of North Carolina’s refund to customers due to changes in health reform legislation

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Bryn Nelson in the October issue of The Hospitalist (see “A Taxing Future for HM?,” p. 16) incorrectly states that Blue Cross Blue Shield of North Carolina’s refund to customers was a result of an overcharge. In point of fact, the refund is a result of a one-time opportunity due to the changes brought about by the health reform law. The new rating and grandfathering rules in the Patient Protection and Affordable Care Act create a one-time circumstance enabling these refunds.

The funds come from active life reserves, which are portions of the premium set aside in the early years of a policy to pay future claims and keep rates stable as customers’ medical expenses rise during the life of the policy. However, policies purchased or substantially modified after March 23, 2010, will end in 2014 under the new healthcare reform law, which is when the new products under health reform will be introduced. Therefore, the reserves held for these products will cover a much shorter period of time, allowing for these funds to be released.

Lew Borman,

media relations,

Blue Cross Blue Shield of North Carolina

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Bryn Nelson in the October issue of The Hospitalist (see “A Taxing Future for HM?,” p. 16) incorrectly states that Blue Cross Blue Shield of North Carolina’s refund to customers was a result of an overcharge. In point of fact, the refund is a result of a one-time opportunity due to the changes brought about by the health reform law. The new rating and grandfathering rules in the Patient Protection and Affordable Care Act create a one-time circumstance enabling these refunds.

The funds come from active life reserves, which are portions of the premium set aside in the early years of a policy to pay future claims and keep rates stable as customers’ medical expenses rise during the life of the policy. However, policies purchased or substantially modified after March 23, 2010, will end in 2014 under the new healthcare reform law, which is when the new products under health reform will be introduced. Therefore, the reserves held for these products will cover a much shorter period of time, allowing for these funds to be released.

Lew Borman,

media relations,

Blue Cross Blue Shield of North Carolina

Bryn Nelson in the October issue of The Hospitalist (see “A Taxing Future for HM?,” p. 16) incorrectly states that Blue Cross Blue Shield of North Carolina’s refund to customers was a result of an overcharge. In point of fact, the refund is a result of a one-time opportunity due to the changes brought about by the health reform law. The new rating and grandfathering rules in the Patient Protection and Affordable Care Act create a one-time circumstance enabling these refunds.

The funds come from active life reserves, which are portions of the premium set aside in the early years of a policy to pay future claims and keep rates stable as customers’ medical expenses rise during the life of the policy. However, policies purchased or substantially modified after March 23, 2010, will end in 2014 under the new healthcare reform law, which is when the new products under health reform will be introduced. Therefore, the reserves held for these products will cover a much shorter period of time, allowing for these funds to be released.

Lew Borman,

media relations,

Blue Cross Blue Shield of North Carolina

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Night or Weekend Admission and Outcomes

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The association between night or weekend admission and hospitalization‐relevant patient outcomes

The hospitalist movement and increasingly stringent resident work hour restrictions have led to the utilization of shift work in many hospitals.1 Use of nocturnist and night float systems, while often necessary, results in increased patient hand‐offs. Research suggests that hand‐offs in the inpatient setting can adversely affect patient outcomes as lack of continuity may increase the possibility of medical error.2, 3 In 2001, Bell et al.4 found that mortality was higher among patients admitted on weekends as compared to weekdays. Uneven staffing, lack of supervision, and fragmented care were cited as potential contributing factors.4 Similarly, Peberdy et al.5 in 2008 revealed that patients were less likely to survive a cardiac arrest if it occurred at night or on weekends, again attributed in part to fragmented patient care and understaffing.

The results of these studies raise concerns as to whether increased reliance on shift work and resulting handoffs compromises patient care.6, 7 The aim of this study was to evaluate the potential association between night admission and hospitalization‐relevant outcomes (length of stay [LOS], hospital charges, intensive care unit [ICU] transfer during hospitalization, repeat emergency department [ED] visit within 30 days of discharge, readmission within 30 days of discharge, and poor outcome [transfer to the ICU, cardiac arrest, or death] within the first 24 hours of admission) at an institution that exclusively uses nocturnists (night‐shift based hospitalists) and a resident night float system for patients admitted at night to the general medicine service. A secondary aim was to determine the potential association between weekend admission and hospitalization‐relevant outcomes.

Methods

Study Sample and Selection

We conducted a retrospective medical record review at a large urban academic hospital. Using an administrative hospital data set, we assembled a list of approximately 9000 admissions to the general medicine service from the ED between January 2008 and October 2008. We sampled consecutive admissions from 3 distinct periods beginning in January, April, and July to capture outcomes at various points in the academic year. We attempted to review approximately 10% of all charts equally distributed among the 3 sampling periods (ie, 900 charts total with one‐third from each period) based on time available to the reviewers. We excluded patients not admitted to the general medicine service and patients without complete demographic or outcome information. We also excluded patients not admitted from the ED given that the vast majority of admissions to our hospital during the night (96%) or weekend (93%) are from the ED. Patients admitted to the general medicine service are cared for either by a hospitalist or by a teaching team comprised of 1 attending (about 40% of whom are hospitalists), 1 resident, 1 to 2 interns, and 1 to 3 medical students. From 7 am to 6:59 pm patients are admitted to the care of 1 of the primary daytime admitting teams. From 7 pm to 6:59 am patients are admitted by nocturnists (hospitalist service) or night float residents (teaching service). These patients are handed off to day teams at 7 am. Hospitalist teams change service on a weekly to biweekly basis and resident teams switch on a monthly basis; there is no difference in physician staffing between the weekend and weekdays. The Northwestern University Institutional Review Board approved this study.

Data Acquisition and Medical Records Reviews

We obtained demographic data including gender, age, race and ethnicity, patient insurance, admission day (weekday vs. weekend), admission time (defined as the time that a patient receives a hospital bed, which at our institution is also the time that admitting teams receive report and assume care for the patient), and the International Classification of Disease codes required to determine the Major Diagnostic Category (MDC) and calculate the Charlson Comorbidity Index8, 9 as part of an administrative data set. We divided the admission time into night admission (defined as 7 pm to 6:59 am) and day admission (defined as 7:00 am to 6:59 pm). We created a chart abstraction tool to allow manual recording of the additional fields of admitting team (hospitalist vs. resident), 30 day repeat ED visit, 30 day readmission, and poor outcomes within the first 24 hours of admission, directly from the electronic record.

Study Outcomes

We evaluated each admission for the following 6 primary outcomes which were specified a priori: LOS (defined as discharge date and time minus admission date and time), hospital charges (defined as charges billed as recorded in the administrative data set), ICU transfer during hospitalization (defined as 1 ICU day in the administrative data set), 30 day repeat ED visit (defined as a visit to our ED within 30 days of discharge as assessed by chart abstraction), 30 day readmission (defined as any planned or unplanned admission to any inpatient service at our institution within 30 days of discharge as assessed by chart abstraction), and poor outcome within 24 hours of admission (defined as transfer to the ICU, cardiac arrest, or death as assessed by chart abstraction). Each of these outcomes has been used in prior work to assess the quality of inpatient care.10, 11

Statistical Analysis

Interrater reliability between the 3 physician reviewers was assessed for 20 randomly selected admissions across the 4 separate review measures using interclass correlation coefficients. Comparisons between night admissions and day admissions, and between weekend and weekday admissions, for the continuous primary outcomes (LOS, hospital charges) were assessed using 2‐tailed t‐tests as well as Wilcoxon rank sum test. In the multivariable modeling, these outcomes were assessed by linear regression controlling for age, gender, race and ethnicity, Medicaid or self‐pay insurance, admission to the hospitalist or teaching service, most common MDC categories, and Charlson Comorbidity Index. Because both outcomes were right‐skewed, we separately assessed each after log‐transformation controlling for the same variables.

All comparisons of the dichotomous primary outcomes (ICU transfer during hospitalization, 30 day repeat ED visit, 30 day readmission, and poor outcome within the first 24 hours after admission) were assessed at the univariate level by chi‐squared test, and in the multivariable models using logistic regression, controlling for the same variables as the linear models above. All adjustments were specified a priori. All data analyses were conducted using Stata (College Station, TX; Version 11).

Results

We reviewed 857 records. After excluding 33 records lacking administrative data regarding gender, race and ethnicity, and other demographic variables, there were 824 medical records available for analysis. We reviewed a similar number of records from each time period: 274 from January 2008, 265 from April 2008, and 285 from July 2008. A total of 345 (42%) patients were admitted during the day, and 479 (58%) at night; 641 (78%) were admitted on weekdays, and 183 (22%) on weekends. The 33 excluded charts were similar to the included charts for both time of admission and outcomes. Results for parametric testing and nonparametric testing, as well as for log‐transformation and non‐log‐transformation of the continuous outcomes were similar in both magnitude and statistical significance, so we present the parametric and nonlog‐transformed results below for ease of interpretation.

Interrater reliability among the 3 reviewers was very high. There were no disagreements among the 20 multiple reviews for either poor outcomes within 24 hours of admission or admitting service; the interclass correlation coefficients for 30 day repeat ED visit and 30 day readmission were 0.97 and 0.87, respectively.

Patients admitted at night or on the weekend were similar to patients admitted during the day and week across age, gender, insurance class, MDC, and Charlson Comorbidity Index (Table 1). For unadjusted outcomes, patients admitted at night has a similar LOS, hospital charges, 30 day repeat ED visits, 30 day readmissions, and poor outcome within 24 hours of admission as those patients admitted during the day. They had a potentially lower chance of any ICU transfer during hospitalization though this did not reach statistical significance at P < 0.05 (night admission 6%, day admission 3%, P = 0.06) (Table 2).

Baseline Characteristics of Patients
CharacteristicsTime of DayDay of the Week
Day Admission (n = 345)Night Admission (n = 479)Weekday Admission (n = 641)Weekend Admission (n = 183)
  • NOTE: All P values > 0.05.

  • Abbreviation: ED, emergency department.

Age (years)60.859.760.658.7
Gender (% male)47434546
Race/Ethnicity (%)
White, Asian, other61545755
Black34383734
Hispanic58610
Medicaid or self pay (%)9101011
Major diagnostic category (%)
Respiratory disease14131413
Circulatory disease28232624
Digestive disease12121212
Other45524851
Charlson Comorbidity Index3.713.603.663.60
Outcomes, Unadjusted
OutcomesTime of DayDay of the Week
Day Admission (n = 345)Night Admission (n = 479)Weekday Admission (n = 641)Weekend Admission (n = 183)
  • Abbreviations: ED, emergency department; ICU, intensive care unit.

  • P < 0.05.

  • P= 0.06.

Length of stay4.34.14.33.8
Hospital charges$27,500$25,200$27,200*$22,700*
ICU transfer during hospitalization (%)635*1*
Repeat ED visit at 30 days (%)20222221
Readmission at 30 days (%)17202017
Poor outcome at 24 hours (ICU transfer, cardiac arrest, or death)(%)2121

Patients admitted to the hospital during the weekend were similar to patients admitted during the week for unadjusted LOS, 30 day repeat ED visit or readmission rate, and poor outcomes within 24 hours of admission as those admitted during the week; however, they had lower hospital charges (weekend admission $22,700, weekday admission $27,200; P = 0.02), and a lower chance of ICU transfer during hospitalization (weekend admission 1%, weekday admission 5%; P = 0.02) (Table 2).

In the multivariable linear and logistic regression models (Tables 3 and 4), we assessed the independent association between night admission or weekend admission and each hospitalization‐relevant outcome except for poor outcome within 24 hours of admission (poor outcome within 24 hours of admission was not modeled to avoid the risk of overfitting because there were only 13 total events). After adjustment for age, gender, race and ethnicity, admitting service (hospitalist or teaching), Medicaid or self‐pay insurance, MDC, and Charlson Comorbidity Index, there was no statistically significant association between night admission and worse outcomes for LOS, hospital charges, 30 day repeat ED visit, or 30 day readmission. Night admission was associated with a decreased chance of ICU transfer during hospitalization, but the difference was not statistically significant (odds ratio, 0.54; 95% confidence interval [CI], 0.26‐1.11, P = 0.09). Weekend admission was not associated with worse outcomes for LOS or 30 day repeat ED visit or readmission; however, weekend admission was associated with a decrease in overall charges ($4400; 95% CI, $8300 to $600) and a decreased chance of ICU transfer during hospitalization (odds ratio, 0.20; 95% CI, 0.050.88).

Linear Regressions for Continuous Outcomes (With Coefficients)
PredictorsLength of Stay (days), Coefficient (95% CI)Hospital Charges (dollars), Coefficient (95% CI)
  • Abbreviations: CI, confidence intervals; ICU, intensive care unit; MDC, major diagnostic category: comparison to other.

  • P < 0.05.

Night admission0.23 (0.77 to 0.32)2100 (5400 to 1100)
Weekend admission0.42 (1.07 to 0.23)4400 (8300 to 600)*
Age0.01 (0.01 to 0.03)0 (100 to 100)
Male gender0.15 (0.70 to 0.39)400 (3700 to 2800)
Race, Black0.18 (0.41 to 0.78)200 (3700 to 3400)
Ethnicity, Hispanic0.62 (1.73 to 0.49)2300 (8900 to 4300)
Medicaid or self‐pay insurance1.87 (0.93 to 2.82)*8900 (3300 to 14600)*
Hospitalist service0.26 (0.29 to 0.81)600 (3900 to 2700)
MDC: respiratory0.36 (1.18 to 0.46)700 (4200 to 5600)
MDC: circulatory1.36 (2.04 to 0.68)*600 (4600 to 3400)
MDC: digestive1.22 (2.08 to 0.35)*6800 (12000 to 1700)*
Charlson Comorbidity Index0.35 (0.22 to 0.49)*2200 (1400 to 3000)*
Logistic Regressions for Dichotomous Outcomes (With Odds Ratios)
PredictorsICU Transfer during Hospitalization, Odds Ratio (95% CI)Repeat ED Visit at 30 days, Odds Ratio (95% CI)Readmission at 30 days, Odds Ratio (95% CI)
  • Abbreviations: CI, confidence intervals; ICU, intensive care unit; MDC, major diagnostic category: comparison to other.

  • P < 0.05.

Night admission0.53 (0.26 to 1.11)1.13 (0.80 to 1.60)1.23 (0.86 to 1.78)
Weekend admission0.20 (0.05 to 0.88)*0.95 (0.63 to 1.44)0.80 (0.51 to 1.25)
Age1.00 (0.98 to 1.02)0.99 (0.98 to 1.002)1.00 (0.99 to 1.01)
Male gender0.98 (0.47 to 2.02)1.09 (0.78 to 1.54)0.91 (0.64 to 1.31)
Race, Black0.75 (0.33 to 1.70)1.48 (1.02 to 2.14)*1.12 (0.76 to 1.65)
Ethnicity, Hispanic0.76 (0.16 to 3.73)1.09 (0.55 to 2.17)1.11 (0.55 to 2.22)
Medicaid or self‐pay insurance0.75 (0.16 to 3.49)1.61 (0.95 to 2.72)2.14 (1.24 to 3.67)*
Hospitalist service0.68 (0.33 to 1.44)1.15 (0.81 to 1.63)0.99 (0.69 to 1.43)
MDC: respiratory1.18 (0.41 to 3.38)1.02 (0.61 to 1.69)1.16 (0.69 to 1.95)
MDC: circulatory1.22 (0.52 to 2.87)0.79 (0.51 to 1.22)0.80 (0.51 to 1.27)
MDC: digestive0.51 (0.11 to 2.32)0.83 (0.47 to 1.46)1.08 (0.62 to 1.91)
Charlson Comobrbidity Index1.25 (1.09 to 1.45)*1.09 (1.01 to 1.19)*1.11 (1.02 to 1.21)*

Our multivariate models explained very little of the variance in patient outcomes. For LOS and hospital charges, adjusted R2 values were 0.06 and 0.05, respectively. For ICU transfer during hospitalization, 30 day repeat ED visit, and 30 day readmission, the areas under the receiver operator curves were 0.75, 0.51, and 0.61 respectively.

To assess the robustness of our conclusions regarding night admission, we redefined night to include only patients admitted between the hours of 8 pm and 5:59 am. This did not change our conclusions. We also tested for interaction between night admission and weekend admission for all outcomes to assess whether night admissions on the weekend were in fact at increased risk of worse outcomes; we found no evidence of interaction (P > 0.3 for the interaction terms in each model).

Discussion

Among patients admitted to the medicine services at our academic medical center, night or weekend admission was not associated with worse hospitalization‐relevant outcomes. In some cases, night or weekend admission was associated with better outcomes, particularly in terms of ICU transfer during hospitalization and hospital charges. Prior research indicates worse outcomes during off‐hours,5 but we did not replicate this finding in our study.

The finding that admission at night was not associated with worse outcomes, particularly proximal outcomes such as LOS or ICU transfer during hospitalization, was surprising, though reassuring in view of the fact that more than half of our patients are admitted at night. We believe a few factors may be responsible. First, our general medicine service is staffed during the night (7 pm to 7 am) by in‐house nocturnists and night float residents. Second, our staffing ratio, while lower at night than during the day, remains the same on weekends and may be higher than in other settings. In continuously well‐staffed settings such as the ED12 and ICU,13 night and weekend admissions are only inconsistently associated with worse outcomes, which may be the same phenomena we observed in the current study. Third, the hospital used as the site of this study has received Nursing Magnet recognition and numerous quality awards such as the National Research Corporation's Consumer Choice Award and recognition as a Distinguished Hospital for Clinical Excellence by HealthGrades. Fourth, our integrated electronic medical record, computerized physician order entry system, and automatically generated sign out serve as complements to the morning hand off. Fifth, hospitalists and teaching teams rotate on a weekly, biweekly, or every 4 week basis, which may protect against discontinuity associated with the weekend. We believe that all of these factors may facilitate alert, comprehensive care during the night and weekend as well as safe and efficient transfer of patients from the night to the day providers.

We were also surprised by the association between weekend admission and lower charges and a lower chance of ICU transfer during hospitalization. We believe many of the same factors noted above may have played a role in these findings. In terms of hospital charges, it is possible that some workups were completed outside of the hospital rather than during the hospitalization, and that some tests were not ordered at all due to unavailability on weekends. The decreased chance of ICU transfer is unexplained. We hypothesize that there may have been a more conservative admission strategy within the ED, such that patients with high baseline severity were admitted directly to the ICU on the weekend rather than being admitted first to the general medicine floor. This hypothesis requires further study.

Our study had important limitations. It was a retrospective study from a single academic hospital. The sample size lacked sufficient power to detect differences in the low frequency of certain outcomes such as poor outcomes within 24 hours of admission (2% vs. 1%), and also for more frequent outcomes such as 30 day readmission; it is possible that with a larger sample there would have been statistically significant differences. Further, we recognize that the Charlson Comorbidity Index, which was developed to predict 1‐year mortality for medicine service patients, does not adjust for severity of illness at presentation, particularly for outcomes such as readmission. If patients admitted at night and during the weekend were less acutely ill despite having similar comorbidities and MDCs at admission, true associations between time of admission and worse outcomes could have been masked. Furthermore, the multivariable modeling explained very little of the variance in patient outcomes such that significant unmeasured confounding may still be present, and consequently our results cannot be interpreted in a causal way. Data was collected from electronic records, so it is possible that some adverse events were not recorded. However, it seems unlikely that major events such as death and transfer to an ICU would have been missed.

Several aspects of the study strengthen our confidence in the findings, including a large sample size, relevance of the outcomes, the adjustment for confounders, and an assessment for robustness of the conclusions based on restricting the definition of night and also testing for interaction between night and weekend admission. Our patient demographics and insurance mix resemble that of other academic hospitals,10 and perhaps our results may be generalizable to these settings, if not to non‐urban or community hospitals. Furthermore, the Charlson Comorbidity Index was associated with all 5 of the modeled outcomes we chose for our study, reaffirming their utility in assessing the quality of hospital care. Future directions for investigation may include examining the association of night admission with hospitalization‐relevant outcomes in nonacademic, nonurban settings, and examining whether the lack of association between night and weekend admission and worse outcomes persists with adjustment for initial severity of illness.

In summary, at a large, well‐staffed urban academic hospital, day or time of admission were not associated with worse hospitalization‐relevant outcomes. The use of nocturnists and night float teams for night admissions and continuity across weekends appears to be a safe approach to handling the increased volume of patients admitted at night, and a viable alternative to overnight call in the era of work hour restrictions.

References
  1. Vaughn DM,Stout CL,McCampbell BL, et al.Three‐year results of mandated work hour restrictions: attending and resident perspectives and effects in a community hospital.Am Surg.2008;74(6):542546; discussion 546–547.
  2. Kitch BT,Cooper JB,Zapol WM, et al.Handoffs causing patient harm: a survey of medical and surgical house staff.Jt Comm J Qual Patient Saf.2008;34(10):563570.
  3. Petersen LA,Brennan TA,O'Neil AC,Cook EF,Lee TH.Does housestaff discontinuity of care increase the risk for preventable adverse events?Ann Intern Med.1994;121(11):866872.
  4. Bell CM,Redelmeier DA.Mortality among patients admitted to hospitals on weekends as compared with weekdays.N Engl J Med.2001;345(9):663668.
  5. Peberdy MA,Ornato JP,Larkin GL, et al.Survival from in‐hospital cardiac arrest during nights and weekends.JAMA.2008;299(7):785792.
  6. Sharma G,Freeman J,Zhang D,Goodwin JS.Continuity of care and intensive care unit use at the end of life.Arch Intern Med.2009;169(1):8186.
  7. Sharma G,Fletcher KE,Zhang D,Kuo YF,Freeman JL,Goodwin JS.Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults.JAMA.2009;301(16):16711680.
  8. Charlson ME,Ales KL,Simon R,MacKenzie CR.Why predictive indexes perform less well in validation studies: is it magic or methods?Arch Intern Med.1987;147:21552161.
  9. Deyo RA,Cherkin DC,Ciol MA.Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613619.
  10. Roy CL,Liang CL,Lund M, et al.Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes.J Hosp Med.2008;3(5):361368.
  11. Groarke JD,Gallagher J,Stack J, et al.Use of an admission early warning score to predict patient morbidity and mortality and treatment success.Emerg Med J.2008;25(12):803806.
  12. Schmulewitz L,Proudfoot A,Bell D.The impact of weekends on outcome for emergency patients.Clin Med.2005;5(6):621625.
  13. Meynaar IA,van der Spoel JI,Rommes JH,van Spreuwel‐Verheijen M,Bosman RJ,Spronk PE.Off hour admission to an intensivist‐led ICU is not associated with increased mortality.Crit Care.2009;13(3):R84.
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The hospitalist movement and increasingly stringent resident work hour restrictions have led to the utilization of shift work in many hospitals.1 Use of nocturnist and night float systems, while often necessary, results in increased patient hand‐offs. Research suggests that hand‐offs in the inpatient setting can adversely affect patient outcomes as lack of continuity may increase the possibility of medical error.2, 3 In 2001, Bell et al.4 found that mortality was higher among patients admitted on weekends as compared to weekdays. Uneven staffing, lack of supervision, and fragmented care were cited as potential contributing factors.4 Similarly, Peberdy et al.5 in 2008 revealed that patients were less likely to survive a cardiac arrest if it occurred at night or on weekends, again attributed in part to fragmented patient care and understaffing.

The results of these studies raise concerns as to whether increased reliance on shift work and resulting handoffs compromises patient care.6, 7 The aim of this study was to evaluate the potential association between night admission and hospitalization‐relevant outcomes (length of stay [LOS], hospital charges, intensive care unit [ICU] transfer during hospitalization, repeat emergency department [ED] visit within 30 days of discharge, readmission within 30 days of discharge, and poor outcome [transfer to the ICU, cardiac arrest, or death] within the first 24 hours of admission) at an institution that exclusively uses nocturnists (night‐shift based hospitalists) and a resident night float system for patients admitted at night to the general medicine service. A secondary aim was to determine the potential association between weekend admission and hospitalization‐relevant outcomes.

Methods

Study Sample and Selection

We conducted a retrospective medical record review at a large urban academic hospital. Using an administrative hospital data set, we assembled a list of approximately 9000 admissions to the general medicine service from the ED between January 2008 and October 2008. We sampled consecutive admissions from 3 distinct periods beginning in January, April, and July to capture outcomes at various points in the academic year. We attempted to review approximately 10% of all charts equally distributed among the 3 sampling periods (ie, 900 charts total with one‐third from each period) based on time available to the reviewers. We excluded patients not admitted to the general medicine service and patients without complete demographic or outcome information. We also excluded patients not admitted from the ED given that the vast majority of admissions to our hospital during the night (96%) or weekend (93%) are from the ED. Patients admitted to the general medicine service are cared for either by a hospitalist or by a teaching team comprised of 1 attending (about 40% of whom are hospitalists), 1 resident, 1 to 2 interns, and 1 to 3 medical students. From 7 am to 6:59 pm patients are admitted to the care of 1 of the primary daytime admitting teams. From 7 pm to 6:59 am patients are admitted by nocturnists (hospitalist service) or night float residents (teaching service). These patients are handed off to day teams at 7 am. Hospitalist teams change service on a weekly to biweekly basis and resident teams switch on a monthly basis; there is no difference in physician staffing between the weekend and weekdays. The Northwestern University Institutional Review Board approved this study.

Data Acquisition and Medical Records Reviews

We obtained demographic data including gender, age, race and ethnicity, patient insurance, admission day (weekday vs. weekend), admission time (defined as the time that a patient receives a hospital bed, which at our institution is also the time that admitting teams receive report and assume care for the patient), and the International Classification of Disease codes required to determine the Major Diagnostic Category (MDC) and calculate the Charlson Comorbidity Index8, 9 as part of an administrative data set. We divided the admission time into night admission (defined as 7 pm to 6:59 am) and day admission (defined as 7:00 am to 6:59 pm). We created a chart abstraction tool to allow manual recording of the additional fields of admitting team (hospitalist vs. resident), 30 day repeat ED visit, 30 day readmission, and poor outcomes within the first 24 hours of admission, directly from the electronic record.

Study Outcomes

We evaluated each admission for the following 6 primary outcomes which were specified a priori: LOS (defined as discharge date and time minus admission date and time), hospital charges (defined as charges billed as recorded in the administrative data set), ICU transfer during hospitalization (defined as 1 ICU day in the administrative data set), 30 day repeat ED visit (defined as a visit to our ED within 30 days of discharge as assessed by chart abstraction), 30 day readmission (defined as any planned or unplanned admission to any inpatient service at our institution within 30 days of discharge as assessed by chart abstraction), and poor outcome within 24 hours of admission (defined as transfer to the ICU, cardiac arrest, or death as assessed by chart abstraction). Each of these outcomes has been used in prior work to assess the quality of inpatient care.10, 11

Statistical Analysis

Interrater reliability between the 3 physician reviewers was assessed for 20 randomly selected admissions across the 4 separate review measures using interclass correlation coefficients. Comparisons between night admissions and day admissions, and between weekend and weekday admissions, for the continuous primary outcomes (LOS, hospital charges) were assessed using 2‐tailed t‐tests as well as Wilcoxon rank sum test. In the multivariable modeling, these outcomes were assessed by linear regression controlling for age, gender, race and ethnicity, Medicaid or self‐pay insurance, admission to the hospitalist or teaching service, most common MDC categories, and Charlson Comorbidity Index. Because both outcomes were right‐skewed, we separately assessed each after log‐transformation controlling for the same variables.

All comparisons of the dichotomous primary outcomes (ICU transfer during hospitalization, 30 day repeat ED visit, 30 day readmission, and poor outcome within the first 24 hours after admission) were assessed at the univariate level by chi‐squared test, and in the multivariable models using logistic regression, controlling for the same variables as the linear models above. All adjustments were specified a priori. All data analyses were conducted using Stata (College Station, TX; Version 11).

Results

We reviewed 857 records. After excluding 33 records lacking administrative data regarding gender, race and ethnicity, and other demographic variables, there were 824 medical records available for analysis. We reviewed a similar number of records from each time period: 274 from January 2008, 265 from April 2008, and 285 from July 2008. A total of 345 (42%) patients were admitted during the day, and 479 (58%) at night; 641 (78%) were admitted on weekdays, and 183 (22%) on weekends. The 33 excluded charts were similar to the included charts for both time of admission and outcomes. Results for parametric testing and nonparametric testing, as well as for log‐transformation and non‐log‐transformation of the continuous outcomes were similar in both magnitude and statistical significance, so we present the parametric and nonlog‐transformed results below for ease of interpretation.

Interrater reliability among the 3 reviewers was very high. There were no disagreements among the 20 multiple reviews for either poor outcomes within 24 hours of admission or admitting service; the interclass correlation coefficients for 30 day repeat ED visit and 30 day readmission were 0.97 and 0.87, respectively.

Patients admitted at night or on the weekend were similar to patients admitted during the day and week across age, gender, insurance class, MDC, and Charlson Comorbidity Index (Table 1). For unadjusted outcomes, patients admitted at night has a similar LOS, hospital charges, 30 day repeat ED visits, 30 day readmissions, and poor outcome within 24 hours of admission as those patients admitted during the day. They had a potentially lower chance of any ICU transfer during hospitalization though this did not reach statistical significance at P < 0.05 (night admission 6%, day admission 3%, P = 0.06) (Table 2).

Baseline Characteristics of Patients
CharacteristicsTime of DayDay of the Week
Day Admission (n = 345)Night Admission (n = 479)Weekday Admission (n = 641)Weekend Admission (n = 183)
  • NOTE: All P values > 0.05.

  • Abbreviation: ED, emergency department.

Age (years)60.859.760.658.7
Gender (% male)47434546
Race/Ethnicity (%)
White, Asian, other61545755
Black34383734
Hispanic58610
Medicaid or self pay (%)9101011
Major diagnostic category (%)
Respiratory disease14131413
Circulatory disease28232624
Digestive disease12121212
Other45524851
Charlson Comorbidity Index3.713.603.663.60
Outcomes, Unadjusted
OutcomesTime of DayDay of the Week
Day Admission (n = 345)Night Admission (n = 479)Weekday Admission (n = 641)Weekend Admission (n = 183)
  • Abbreviations: ED, emergency department; ICU, intensive care unit.

  • P < 0.05.

  • P= 0.06.

Length of stay4.34.14.33.8
Hospital charges$27,500$25,200$27,200*$22,700*
ICU transfer during hospitalization (%)635*1*
Repeat ED visit at 30 days (%)20222221
Readmission at 30 days (%)17202017
Poor outcome at 24 hours (ICU transfer, cardiac arrest, or death)(%)2121

Patients admitted to the hospital during the weekend were similar to patients admitted during the week for unadjusted LOS, 30 day repeat ED visit or readmission rate, and poor outcomes within 24 hours of admission as those admitted during the week; however, they had lower hospital charges (weekend admission $22,700, weekday admission $27,200; P = 0.02), and a lower chance of ICU transfer during hospitalization (weekend admission 1%, weekday admission 5%; P = 0.02) (Table 2).

In the multivariable linear and logistic regression models (Tables 3 and 4), we assessed the independent association between night admission or weekend admission and each hospitalization‐relevant outcome except for poor outcome within 24 hours of admission (poor outcome within 24 hours of admission was not modeled to avoid the risk of overfitting because there were only 13 total events). After adjustment for age, gender, race and ethnicity, admitting service (hospitalist or teaching), Medicaid or self‐pay insurance, MDC, and Charlson Comorbidity Index, there was no statistically significant association between night admission and worse outcomes for LOS, hospital charges, 30 day repeat ED visit, or 30 day readmission. Night admission was associated with a decreased chance of ICU transfer during hospitalization, but the difference was not statistically significant (odds ratio, 0.54; 95% confidence interval [CI], 0.26‐1.11, P = 0.09). Weekend admission was not associated with worse outcomes for LOS or 30 day repeat ED visit or readmission; however, weekend admission was associated with a decrease in overall charges ($4400; 95% CI, $8300 to $600) and a decreased chance of ICU transfer during hospitalization (odds ratio, 0.20; 95% CI, 0.050.88).

Linear Regressions for Continuous Outcomes (With Coefficients)
PredictorsLength of Stay (days), Coefficient (95% CI)Hospital Charges (dollars), Coefficient (95% CI)
  • Abbreviations: CI, confidence intervals; ICU, intensive care unit; MDC, major diagnostic category: comparison to other.

  • P < 0.05.

Night admission0.23 (0.77 to 0.32)2100 (5400 to 1100)
Weekend admission0.42 (1.07 to 0.23)4400 (8300 to 600)*
Age0.01 (0.01 to 0.03)0 (100 to 100)
Male gender0.15 (0.70 to 0.39)400 (3700 to 2800)
Race, Black0.18 (0.41 to 0.78)200 (3700 to 3400)
Ethnicity, Hispanic0.62 (1.73 to 0.49)2300 (8900 to 4300)
Medicaid or self‐pay insurance1.87 (0.93 to 2.82)*8900 (3300 to 14600)*
Hospitalist service0.26 (0.29 to 0.81)600 (3900 to 2700)
MDC: respiratory0.36 (1.18 to 0.46)700 (4200 to 5600)
MDC: circulatory1.36 (2.04 to 0.68)*600 (4600 to 3400)
MDC: digestive1.22 (2.08 to 0.35)*6800 (12000 to 1700)*
Charlson Comorbidity Index0.35 (0.22 to 0.49)*2200 (1400 to 3000)*
Logistic Regressions for Dichotomous Outcomes (With Odds Ratios)
PredictorsICU Transfer during Hospitalization, Odds Ratio (95% CI)Repeat ED Visit at 30 days, Odds Ratio (95% CI)Readmission at 30 days, Odds Ratio (95% CI)
  • Abbreviations: CI, confidence intervals; ICU, intensive care unit; MDC, major diagnostic category: comparison to other.

  • P < 0.05.

Night admission0.53 (0.26 to 1.11)1.13 (0.80 to 1.60)1.23 (0.86 to 1.78)
Weekend admission0.20 (0.05 to 0.88)*0.95 (0.63 to 1.44)0.80 (0.51 to 1.25)
Age1.00 (0.98 to 1.02)0.99 (0.98 to 1.002)1.00 (0.99 to 1.01)
Male gender0.98 (0.47 to 2.02)1.09 (0.78 to 1.54)0.91 (0.64 to 1.31)
Race, Black0.75 (0.33 to 1.70)1.48 (1.02 to 2.14)*1.12 (0.76 to 1.65)
Ethnicity, Hispanic0.76 (0.16 to 3.73)1.09 (0.55 to 2.17)1.11 (0.55 to 2.22)
Medicaid or self‐pay insurance0.75 (0.16 to 3.49)1.61 (0.95 to 2.72)2.14 (1.24 to 3.67)*
Hospitalist service0.68 (0.33 to 1.44)1.15 (0.81 to 1.63)0.99 (0.69 to 1.43)
MDC: respiratory1.18 (0.41 to 3.38)1.02 (0.61 to 1.69)1.16 (0.69 to 1.95)
MDC: circulatory1.22 (0.52 to 2.87)0.79 (0.51 to 1.22)0.80 (0.51 to 1.27)
MDC: digestive0.51 (0.11 to 2.32)0.83 (0.47 to 1.46)1.08 (0.62 to 1.91)
Charlson Comobrbidity Index1.25 (1.09 to 1.45)*1.09 (1.01 to 1.19)*1.11 (1.02 to 1.21)*

Our multivariate models explained very little of the variance in patient outcomes. For LOS and hospital charges, adjusted R2 values were 0.06 and 0.05, respectively. For ICU transfer during hospitalization, 30 day repeat ED visit, and 30 day readmission, the areas under the receiver operator curves were 0.75, 0.51, and 0.61 respectively.

To assess the robustness of our conclusions regarding night admission, we redefined night to include only patients admitted between the hours of 8 pm and 5:59 am. This did not change our conclusions. We also tested for interaction between night admission and weekend admission for all outcomes to assess whether night admissions on the weekend were in fact at increased risk of worse outcomes; we found no evidence of interaction (P > 0.3 for the interaction terms in each model).

Discussion

Among patients admitted to the medicine services at our academic medical center, night or weekend admission was not associated with worse hospitalization‐relevant outcomes. In some cases, night or weekend admission was associated with better outcomes, particularly in terms of ICU transfer during hospitalization and hospital charges. Prior research indicates worse outcomes during off‐hours,5 but we did not replicate this finding in our study.

The finding that admission at night was not associated with worse outcomes, particularly proximal outcomes such as LOS or ICU transfer during hospitalization, was surprising, though reassuring in view of the fact that more than half of our patients are admitted at night. We believe a few factors may be responsible. First, our general medicine service is staffed during the night (7 pm to 7 am) by in‐house nocturnists and night float residents. Second, our staffing ratio, while lower at night than during the day, remains the same on weekends and may be higher than in other settings. In continuously well‐staffed settings such as the ED12 and ICU,13 night and weekend admissions are only inconsistently associated with worse outcomes, which may be the same phenomena we observed in the current study. Third, the hospital used as the site of this study has received Nursing Magnet recognition and numerous quality awards such as the National Research Corporation's Consumer Choice Award and recognition as a Distinguished Hospital for Clinical Excellence by HealthGrades. Fourth, our integrated electronic medical record, computerized physician order entry system, and automatically generated sign out serve as complements to the morning hand off. Fifth, hospitalists and teaching teams rotate on a weekly, biweekly, or every 4 week basis, which may protect against discontinuity associated with the weekend. We believe that all of these factors may facilitate alert, comprehensive care during the night and weekend as well as safe and efficient transfer of patients from the night to the day providers.

We were also surprised by the association between weekend admission and lower charges and a lower chance of ICU transfer during hospitalization. We believe many of the same factors noted above may have played a role in these findings. In terms of hospital charges, it is possible that some workups were completed outside of the hospital rather than during the hospitalization, and that some tests were not ordered at all due to unavailability on weekends. The decreased chance of ICU transfer is unexplained. We hypothesize that there may have been a more conservative admission strategy within the ED, such that patients with high baseline severity were admitted directly to the ICU on the weekend rather than being admitted first to the general medicine floor. This hypothesis requires further study.

Our study had important limitations. It was a retrospective study from a single academic hospital. The sample size lacked sufficient power to detect differences in the low frequency of certain outcomes such as poor outcomes within 24 hours of admission (2% vs. 1%), and also for more frequent outcomes such as 30 day readmission; it is possible that with a larger sample there would have been statistically significant differences. Further, we recognize that the Charlson Comorbidity Index, which was developed to predict 1‐year mortality for medicine service patients, does not adjust for severity of illness at presentation, particularly for outcomes such as readmission. If patients admitted at night and during the weekend were less acutely ill despite having similar comorbidities and MDCs at admission, true associations between time of admission and worse outcomes could have been masked. Furthermore, the multivariable modeling explained very little of the variance in patient outcomes such that significant unmeasured confounding may still be present, and consequently our results cannot be interpreted in a causal way. Data was collected from electronic records, so it is possible that some adverse events were not recorded. However, it seems unlikely that major events such as death and transfer to an ICU would have been missed.

Several aspects of the study strengthen our confidence in the findings, including a large sample size, relevance of the outcomes, the adjustment for confounders, and an assessment for robustness of the conclusions based on restricting the definition of night and also testing for interaction between night and weekend admission. Our patient demographics and insurance mix resemble that of other academic hospitals,10 and perhaps our results may be generalizable to these settings, if not to non‐urban or community hospitals. Furthermore, the Charlson Comorbidity Index was associated with all 5 of the modeled outcomes we chose for our study, reaffirming their utility in assessing the quality of hospital care. Future directions for investigation may include examining the association of night admission with hospitalization‐relevant outcomes in nonacademic, nonurban settings, and examining whether the lack of association between night and weekend admission and worse outcomes persists with adjustment for initial severity of illness.

In summary, at a large, well‐staffed urban academic hospital, day or time of admission were not associated with worse hospitalization‐relevant outcomes. The use of nocturnists and night float teams for night admissions and continuity across weekends appears to be a safe approach to handling the increased volume of patients admitted at night, and a viable alternative to overnight call in the era of work hour restrictions.

The hospitalist movement and increasingly stringent resident work hour restrictions have led to the utilization of shift work in many hospitals.1 Use of nocturnist and night float systems, while often necessary, results in increased patient hand‐offs. Research suggests that hand‐offs in the inpatient setting can adversely affect patient outcomes as lack of continuity may increase the possibility of medical error.2, 3 In 2001, Bell et al.4 found that mortality was higher among patients admitted on weekends as compared to weekdays. Uneven staffing, lack of supervision, and fragmented care were cited as potential contributing factors.4 Similarly, Peberdy et al.5 in 2008 revealed that patients were less likely to survive a cardiac arrest if it occurred at night or on weekends, again attributed in part to fragmented patient care and understaffing.

The results of these studies raise concerns as to whether increased reliance on shift work and resulting handoffs compromises patient care.6, 7 The aim of this study was to evaluate the potential association between night admission and hospitalization‐relevant outcomes (length of stay [LOS], hospital charges, intensive care unit [ICU] transfer during hospitalization, repeat emergency department [ED] visit within 30 days of discharge, readmission within 30 days of discharge, and poor outcome [transfer to the ICU, cardiac arrest, or death] within the first 24 hours of admission) at an institution that exclusively uses nocturnists (night‐shift based hospitalists) and a resident night float system for patients admitted at night to the general medicine service. A secondary aim was to determine the potential association between weekend admission and hospitalization‐relevant outcomes.

Methods

Study Sample and Selection

We conducted a retrospective medical record review at a large urban academic hospital. Using an administrative hospital data set, we assembled a list of approximately 9000 admissions to the general medicine service from the ED between January 2008 and October 2008. We sampled consecutive admissions from 3 distinct periods beginning in January, April, and July to capture outcomes at various points in the academic year. We attempted to review approximately 10% of all charts equally distributed among the 3 sampling periods (ie, 900 charts total with one‐third from each period) based on time available to the reviewers. We excluded patients not admitted to the general medicine service and patients without complete demographic or outcome information. We also excluded patients not admitted from the ED given that the vast majority of admissions to our hospital during the night (96%) or weekend (93%) are from the ED. Patients admitted to the general medicine service are cared for either by a hospitalist or by a teaching team comprised of 1 attending (about 40% of whom are hospitalists), 1 resident, 1 to 2 interns, and 1 to 3 medical students. From 7 am to 6:59 pm patients are admitted to the care of 1 of the primary daytime admitting teams. From 7 pm to 6:59 am patients are admitted by nocturnists (hospitalist service) or night float residents (teaching service). These patients are handed off to day teams at 7 am. Hospitalist teams change service on a weekly to biweekly basis and resident teams switch on a monthly basis; there is no difference in physician staffing between the weekend and weekdays. The Northwestern University Institutional Review Board approved this study.

Data Acquisition and Medical Records Reviews

We obtained demographic data including gender, age, race and ethnicity, patient insurance, admission day (weekday vs. weekend), admission time (defined as the time that a patient receives a hospital bed, which at our institution is also the time that admitting teams receive report and assume care for the patient), and the International Classification of Disease codes required to determine the Major Diagnostic Category (MDC) and calculate the Charlson Comorbidity Index8, 9 as part of an administrative data set. We divided the admission time into night admission (defined as 7 pm to 6:59 am) and day admission (defined as 7:00 am to 6:59 pm). We created a chart abstraction tool to allow manual recording of the additional fields of admitting team (hospitalist vs. resident), 30 day repeat ED visit, 30 day readmission, and poor outcomes within the first 24 hours of admission, directly from the electronic record.

Study Outcomes

We evaluated each admission for the following 6 primary outcomes which were specified a priori: LOS (defined as discharge date and time minus admission date and time), hospital charges (defined as charges billed as recorded in the administrative data set), ICU transfer during hospitalization (defined as 1 ICU day in the administrative data set), 30 day repeat ED visit (defined as a visit to our ED within 30 days of discharge as assessed by chart abstraction), 30 day readmission (defined as any planned or unplanned admission to any inpatient service at our institution within 30 days of discharge as assessed by chart abstraction), and poor outcome within 24 hours of admission (defined as transfer to the ICU, cardiac arrest, or death as assessed by chart abstraction). Each of these outcomes has been used in prior work to assess the quality of inpatient care.10, 11

Statistical Analysis

Interrater reliability between the 3 physician reviewers was assessed for 20 randomly selected admissions across the 4 separate review measures using interclass correlation coefficients. Comparisons between night admissions and day admissions, and between weekend and weekday admissions, for the continuous primary outcomes (LOS, hospital charges) were assessed using 2‐tailed t‐tests as well as Wilcoxon rank sum test. In the multivariable modeling, these outcomes were assessed by linear regression controlling for age, gender, race and ethnicity, Medicaid or self‐pay insurance, admission to the hospitalist or teaching service, most common MDC categories, and Charlson Comorbidity Index. Because both outcomes were right‐skewed, we separately assessed each after log‐transformation controlling for the same variables.

All comparisons of the dichotomous primary outcomes (ICU transfer during hospitalization, 30 day repeat ED visit, 30 day readmission, and poor outcome within the first 24 hours after admission) were assessed at the univariate level by chi‐squared test, and in the multivariable models using logistic regression, controlling for the same variables as the linear models above. All adjustments were specified a priori. All data analyses were conducted using Stata (College Station, TX; Version 11).

Results

We reviewed 857 records. After excluding 33 records lacking administrative data regarding gender, race and ethnicity, and other demographic variables, there were 824 medical records available for analysis. We reviewed a similar number of records from each time period: 274 from January 2008, 265 from April 2008, and 285 from July 2008. A total of 345 (42%) patients were admitted during the day, and 479 (58%) at night; 641 (78%) were admitted on weekdays, and 183 (22%) on weekends. The 33 excluded charts were similar to the included charts for both time of admission and outcomes. Results for parametric testing and nonparametric testing, as well as for log‐transformation and non‐log‐transformation of the continuous outcomes were similar in both magnitude and statistical significance, so we present the parametric and nonlog‐transformed results below for ease of interpretation.

Interrater reliability among the 3 reviewers was very high. There were no disagreements among the 20 multiple reviews for either poor outcomes within 24 hours of admission or admitting service; the interclass correlation coefficients for 30 day repeat ED visit and 30 day readmission were 0.97 and 0.87, respectively.

Patients admitted at night or on the weekend were similar to patients admitted during the day and week across age, gender, insurance class, MDC, and Charlson Comorbidity Index (Table 1). For unadjusted outcomes, patients admitted at night has a similar LOS, hospital charges, 30 day repeat ED visits, 30 day readmissions, and poor outcome within 24 hours of admission as those patients admitted during the day. They had a potentially lower chance of any ICU transfer during hospitalization though this did not reach statistical significance at P < 0.05 (night admission 6%, day admission 3%, P = 0.06) (Table 2).

Baseline Characteristics of Patients
CharacteristicsTime of DayDay of the Week
Day Admission (n = 345)Night Admission (n = 479)Weekday Admission (n = 641)Weekend Admission (n = 183)
  • NOTE: All P values > 0.05.

  • Abbreviation: ED, emergency department.

Age (years)60.859.760.658.7
Gender (% male)47434546
Race/Ethnicity (%)
White, Asian, other61545755
Black34383734
Hispanic58610
Medicaid or self pay (%)9101011
Major diagnostic category (%)
Respiratory disease14131413
Circulatory disease28232624
Digestive disease12121212
Other45524851
Charlson Comorbidity Index3.713.603.663.60
Outcomes, Unadjusted
OutcomesTime of DayDay of the Week
Day Admission (n = 345)Night Admission (n = 479)Weekday Admission (n = 641)Weekend Admission (n = 183)
  • Abbreviations: ED, emergency department; ICU, intensive care unit.

  • P < 0.05.

  • P= 0.06.

Length of stay4.34.14.33.8
Hospital charges$27,500$25,200$27,200*$22,700*
ICU transfer during hospitalization (%)635*1*
Repeat ED visit at 30 days (%)20222221
Readmission at 30 days (%)17202017
Poor outcome at 24 hours (ICU transfer, cardiac arrest, or death)(%)2121

Patients admitted to the hospital during the weekend were similar to patients admitted during the week for unadjusted LOS, 30 day repeat ED visit or readmission rate, and poor outcomes within 24 hours of admission as those admitted during the week; however, they had lower hospital charges (weekend admission $22,700, weekday admission $27,200; P = 0.02), and a lower chance of ICU transfer during hospitalization (weekend admission 1%, weekday admission 5%; P = 0.02) (Table 2).

In the multivariable linear and logistic regression models (Tables 3 and 4), we assessed the independent association between night admission or weekend admission and each hospitalization‐relevant outcome except for poor outcome within 24 hours of admission (poor outcome within 24 hours of admission was not modeled to avoid the risk of overfitting because there were only 13 total events). After adjustment for age, gender, race and ethnicity, admitting service (hospitalist or teaching), Medicaid or self‐pay insurance, MDC, and Charlson Comorbidity Index, there was no statistically significant association between night admission and worse outcomes for LOS, hospital charges, 30 day repeat ED visit, or 30 day readmission. Night admission was associated with a decreased chance of ICU transfer during hospitalization, but the difference was not statistically significant (odds ratio, 0.54; 95% confidence interval [CI], 0.26‐1.11, P = 0.09). Weekend admission was not associated with worse outcomes for LOS or 30 day repeat ED visit or readmission; however, weekend admission was associated with a decrease in overall charges ($4400; 95% CI, $8300 to $600) and a decreased chance of ICU transfer during hospitalization (odds ratio, 0.20; 95% CI, 0.050.88).

Linear Regressions for Continuous Outcomes (With Coefficients)
PredictorsLength of Stay (days), Coefficient (95% CI)Hospital Charges (dollars), Coefficient (95% CI)
  • Abbreviations: CI, confidence intervals; ICU, intensive care unit; MDC, major diagnostic category: comparison to other.

  • P < 0.05.

Night admission0.23 (0.77 to 0.32)2100 (5400 to 1100)
Weekend admission0.42 (1.07 to 0.23)4400 (8300 to 600)*
Age0.01 (0.01 to 0.03)0 (100 to 100)
Male gender0.15 (0.70 to 0.39)400 (3700 to 2800)
Race, Black0.18 (0.41 to 0.78)200 (3700 to 3400)
Ethnicity, Hispanic0.62 (1.73 to 0.49)2300 (8900 to 4300)
Medicaid or self‐pay insurance1.87 (0.93 to 2.82)*8900 (3300 to 14600)*
Hospitalist service0.26 (0.29 to 0.81)600 (3900 to 2700)
MDC: respiratory0.36 (1.18 to 0.46)700 (4200 to 5600)
MDC: circulatory1.36 (2.04 to 0.68)*600 (4600 to 3400)
MDC: digestive1.22 (2.08 to 0.35)*6800 (12000 to 1700)*
Charlson Comorbidity Index0.35 (0.22 to 0.49)*2200 (1400 to 3000)*
Logistic Regressions for Dichotomous Outcomes (With Odds Ratios)
PredictorsICU Transfer during Hospitalization, Odds Ratio (95% CI)Repeat ED Visit at 30 days, Odds Ratio (95% CI)Readmission at 30 days, Odds Ratio (95% CI)
  • Abbreviations: CI, confidence intervals; ICU, intensive care unit; MDC, major diagnostic category: comparison to other.

  • P < 0.05.

Night admission0.53 (0.26 to 1.11)1.13 (0.80 to 1.60)1.23 (0.86 to 1.78)
Weekend admission0.20 (0.05 to 0.88)*0.95 (0.63 to 1.44)0.80 (0.51 to 1.25)
Age1.00 (0.98 to 1.02)0.99 (0.98 to 1.002)1.00 (0.99 to 1.01)
Male gender0.98 (0.47 to 2.02)1.09 (0.78 to 1.54)0.91 (0.64 to 1.31)
Race, Black0.75 (0.33 to 1.70)1.48 (1.02 to 2.14)*1.12 (0.76 to 1.65)
Ethnicity, Hispanic0.76 (0.16 to 3.73)1.09 (0.55 to 2.17)1.11 (0.55 to 2.22)
Medicaid or self‐pay insurance0.75 (0.16 to 3.49)1.61 (0.95 to 2.72)2.14 (1.24 to 3.67)*
Hospitalist service0.68 (0.33 to 1.44)1.15 (0.81 to 1.63)0.99 (0.69 to 1.43)
MDC: respiratory1.18 (0.41 to 3.38)1.02 (0.61 to 1.69)1.16 (0.69 to 1.95)
MDC: circulatory1.22 (0.52 to 2.87)0.79 (0.51 to 1.22)0.80 (0.51 to 1.27)
MDC: digestive0.51 (0.11 to 2.32)0.83 (0.47 to 1.46)1.08 (0.62 to 1.91)
Charlson Comobrbidity Index1.25 (1.09 to 1.45)*1.09 (1.01 to 1.19)*1.11 (1.02 to 1.21)*

Our multivariate models explained very little of the variance in patient outcomes. For LOS and hospital charges, adjusted R2 values were 0.06 and 0.05, respectively. For ICU transfer during hospitalization, 30 day repeat ED visit, and 30 day readmission, the areas under the receiver operator curves were 0.75, 0.51, and 0.61 respectively.

To assess the robustness of our conclusions regarding night admission, we redefined night to include only patients admitted between the hours of 8 pm and 5:59 am. This did not change our conclusions. We also tested for interaction between night admission and weekend admission for all outcomes to assess whether night admissions on the weekend were in fact at increased risk of worse outcomes; we found no evidence of interaction (P > 0.3 for the interaction terms in each model).

Discussion

Among patients admitted to the medicine services at our academic medical center, night or weekend admission was not associated with worse hospitalization‐relevant outcomes. In some cases, night or weekend admission was associated with better outcomes, particularly in terms of ICU transfer during hospitalization and hospital charges. Prior research indicates worse outcomes during off‐hours,5 but we did not replicate this finding in our study.

The finding that admission at night was not associated with worse outcomes, particularly proximal outcomes such as LOS or ICU transfer during hospitalization, was surprising, though reassuring in view of the fact that more than half of our patients are admitted at night. We believe a few factors may be responsible. First, our general medicine service is staffed during the night (7 pm to 7 am) by in‐house nocturnists and night float residents. Second, our staffing ratio, while lower at night than during the day, remains the same on weekends and may be higher than in other settings. In continuously well‐staffed settings such as the ED12 and ICU,13 night and weekend admissions are only inconsistently associated with worse outcomes, which may be the same phenomena we observed in the current study. Third, the hospital used as the site of this study has received Nursing Magnet recognition and numerous quality awards such as the National Research Corporation's Consumer Choice Award and recognition as a Distinguished Hospital for Clinical Excellence by HealthGrades. Fourth, our integrated electronic medical record, computerized physician order entry system, and automatically generated sign out serve as complements to the morning hand off. Fifth, hospitalists and teaching teams rotate on a weekly, biweekly, or every 4 week basis, which may protect against discontinuity associated with the weekend. We believe that all of these factors may facilitate alert, comprehensive care during the night and weekend as well as safe and efficient transfer of patients from the night to the day providers.

We were also surprised by the association between weekend admission and lower charges and a lower chance of ICU transfer during hospitalization. We believe many of the same factors noted above may have played a role in these findings. In terms of hospital charges, it is possible that some workups were completed outside of the hospital rather than during the hospitalization, and that some tests were not ordered at all due to unavailability on weekends. The decreased chance of ICU transfer is unexplained. We hypothesize that there may have been a more conservative admission strategy within the ED, such that patients with high baseline severity were admitted directly to the ICU on the weekend rather than being admitted first to the general medicine floor. This hypothesis requires further study.

Our study had important limitations. It was a retrospective study from a single academic hospital. The sample size lacked sufficient power to detect differences in the low frequency of certain outcomes such as poor outcomes within 24 hours of admission (2% vs. 1%), and also for more frequent outcomes such as 30 day readmission; it is possible that with a larger sample there would have been statistically significant differences. Further, we recognize that the Charlson Comorbidity Index, which was developed to predict 1‐year mortality for medicine service patients, does not adjust for severity of illness at presentation, particularly for outcomes such as readmission. If patients admitted at night and during the weekend were less acutely ill despite having similar comorbidities and MDCs at admission, true associations between time of admission and worse outcomes could have been masked. Furthermore, the multivariable modeling explained very little of the variance in patient outcomes such that significant unmeasured confounding may still be present, and consequently our results cannot be interpreted in a causal way. Data was collected from electronic records, so it is possible that some adverse events were not recorded. However, it seems unlikely that major events such as death and transfer to an ICU would have been missed.

Several aspects of the study strengthen our confidence in the findings, including a large sample size, relevance of the outcomes, the adjustment for confounders, and an assessment for robustness of the conclusions based on restricting the definition of night and also testing for interaction between night and weekend admission. Our patient demographics and insurance mix resemble that of other academic hospitals,10 and perhaps our results may be generalizable to these settings, if not to non‐urban or community hospitals. Furthermore, the Charlson Comorbidity Index was associated with all 5 of the modeled outcomes we chose for our study, reaffirming their utility in assessing the quality of hospital care. Future directions for investigation may include examining the association of night admission with hospitalization‐relevant outcomes in nonacademic, nonurban settings, and examining whether the lack of association between night and weekend admission and worse outcomes persists with adjustment for initial severity of illness.

In summary, at a large, well‐staffed urban academic hospital, day or time of admission were not associated with worse hospitalization‐relevant outcomes. The use of nocturnists and night float teams for night admissions and continuity across weekends appears to be a safe approach to handling the increased volume of patients admitted at night, and a viable alternative to overnight call in the era of work hour restrictions.

References
  1. Vaughn DM,Stout CL,McCampbell BL, et al.Three‐year results of mandated work hour restrictions: attending and resident perspectives and effects in a community hospital.Am Surg.2008;74(6):542546; discussion 546–547.
  2. Kitch BT,Cooper JB,Zapol WM, et al.Handoffs causing patient harm: a survey of medical and surgical house staff.Jt Comm J Qual Patient Saf.2008;34(10):563570.
  3. Petersen LA,Brennan TA,O'Neil AC,Cook EF,Lee TH.Does housestaff discontinuity of care increase the risk for preventable adverse events?Ann Intern Med.1994;121(11):866872.
  4. Bell CM,Redelmeier DA.Mortality among patients admitted to hospitals on weekends as compared with weekdays.N Engl J Med.2001;345(9):663668.
  5. Peberdy MA,Ornato JP,Larkin GL, et al.Survival from in‐hospital cardiac arrest during nights and weekends.JAMA.2008;299(7):785792.
  6. Sharma G,Freeman J,Zhang D,Goodwin JS.Continuity of care and intensive care unit use at the end of life.Arch Intern Med.2009;169(1):8186.
  7. Sharma G,Fletcher KE,Zhang D,Kuo YF,Freeman JL,Goodwin JS.Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults.JAMA.2009;301(16):16711680.
  8. Charlson ME,Ales KL,Simon R,MacKenzie CR.Why predictive indexes perform less well in validation studies: is it magic or methods?Arch Intern Med.1987;147:21552161.
  9. Deyo RA,Cherkin DC,Ciol MA.Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613619.
  10. Roy CL,Liang CL,Lund M, et al.Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes.J Hosp Med.2008;3(5):361368.
  11. Groarke JD,Gallagher J,Stack J, et al.Use of an admission early warning score to predict patient morbidity and mortality and treatment success.Emerg Med J.2008;25(12):803806.
  12. Schmulewitz L,Proudfoot A,Bell D.The impact of weekends on outcome for emergency patients.Clin Med.2005;5(6):621625.
  13. Meynaar IA,van der Spoel JI,Rommes JH,van Spreuwel‐Verheijen M,Bosman RJ,Spronk PE.Off hour admission to an intensivist‐led ICU is not associated with increased mortality.Crit Care.2009;13(3):R84.
References
  1. Vaughn DM,Stout CL,McCampbell BL, et al.Three‐year results of mandated work hour restrictions: attending and resident perspectives and effects in a community hospital.Am Surg.2008;74(6):542546; discussion 546–547.
  2. Kitch BT,Cooper JB,Zapol WM, et al.Handoffs causing patient harm: a survey of medical and surgical house staff.Jt Comm J Qual Patient Saf.2008;34(10):563570.
  3. Petersen LA,Brennan TA,O'Neil AC,Cook EF,Lee TH.Does housestaff discontinuity of care increase the risk for preventable adverse events?Ann Intern Med.1994;121(11):866872.
  4. Bell CM,Redelmeier DA.Mortality among patients admitted to hospitals on weekends as compared with weekdays.N Engl J Med.2001;345(9):663668.
  5. Peberdy MA,Ornato JP,Larkin GL, et al.Survival from in‐hospital cardiac arrest during nights and weekends.JAMA.2008;299(7):785792.
  6. Sharma G,Freeman J,Zhang D,Goodwin JS.Continuity of care and intensive care unit use at the end of life.Arch Intern Med.2009;169(1):8186.
  7. Sharma G,Fletcher KE,Zhang D,Kuo YF,Freeman JL,Goodwin JS.Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults.JAMA.2009;301(16):16711680.
  8. Charlson ME,Ales KL,Simon R,MacKenzie CR.Why predictive indexes perform less well in validation studies: is it magic or methods?Arch Intern Med.1987;147:21552161.
  9. Deyo RA,Cherkin DC,Ciol MA.Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613619.
  10. Roy CL,Liang CL,Lund M, et al.Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes.J Hosp Med.2008;3(5):361368.
  11. Groarke JD,Gallagher J,Stack J, et al.Use of an admission early warning score to predict patient morbidity and mortality and treatment success.Emerg Med J.2008;25(12):803806.
  12. Schmulewitz L,Proudfoot A,Bell D.The impact of weekends on outcome for emergency patients.Clin Med.2005;5(6):621625.
  13. Meynaar IA,van der Spoel JI,Rommes JH,van Spreuwel‐Verheijen M,Bosman RJ,Spronk PE.Off hour admission to an intensivist‐led ICU is not associated with increased mortality.Crit Care.2009;13(3):R84.
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Journal of Hospital Medicine - 6(1)
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The association between night or weekend admission and hospitalization‐relevant patient outcomes
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The association between night or weekend admission and hospitalization‐relevant patient outcomes
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communication, continuity of care transition and discharge planning, education, outcomes measurement, patient safety, resident
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communication, continuity of care transition and discharge planning, education, outcomes measurement, patient safety, resident
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Hospital‐Based Tobacco Treatment Service

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Evaluation of a hospital‐based tobacco treatment service: Outcomes and lessons learned

Hospitalization can be considered a teachable moment for smoking cessation13 for the 6.5 million adult smokers who are hospitalized in the United States each year.4 Smokers who receive tobacco treatment during hospitalization and outpatient follow‐up treatment for at least 1 month are more likely to quit than patients who receive no treatment.5, 6

Unless tobacco treatment is explicitly delegated to other providers, physicians shoulder the responsibility of encouraging smokers to quit and prescribing smoking cessation medications. This is problematic in that physicians sometimes fail to counsel their patients about quitting smoking7, 8 or recommend outpatient follow‐up.9 Few hospitals provide comprehensive treatment. In a review of 33 studies on the prevalence of smoking care delivery in hospitals, 3 hospitals reported they provided advice to quit alone, 29 provided advice plus counseling and assistance in quitting, and 8 provided advice or prescription for cessation pharmacotherapy.9 Although post‐discharge support is a key component of effective treatment for hospitalized smokers,6 only 11 reported providing follow‐up treatment, or referral for follow‐up treatment, after discharge. Among these 11 hospitals, respondents reported they provided referral or follow‐up to 1% to 74% of their smokers, with a median percentage of 24%. The 1 study that specified the type of outpatient treatment provided reported the hospital provided the state quitline number to smokers.

Instituting a dedicated smoking cessation program may enhance inpatient treatment, outpatient follow‐up, and treatment outcomes. Two studies have found that institutional smoking cessation programs increased the likelihood that patients would receive treatment and quit compared to hospitals without dedicated programs.10, 11

Although many US hospitals are developing programs to provide systematic treatment for tobacco dependence,9 little is known regarding how programs structure their staff, enroll patients, or provide treatment to patients that smoke. Instituting tobacco treatment services usually requires policy change and system‐wide approaches with quality improvement endpoint goals.8, 1214 In the United States, elements of these services include: 1) developing a cadre of trained tobacco treatment specialists, 2) implementing hospital systems for identifying smokers and referring them to the service, 3) providing inpatient treatment based on current treatment guidelines15 and 4) providing or facilitating follow‐up treatment after discharge, often via fax‐referral to tobacco quitlines. This systematic approach is still lacking in many hospitals.

To date, few evaluations of dedicated hospital‐based smoking cessation programs have been reported in the literature.8, 11 The purpose of this study is to describe patient characteristics and outcomes of a dedicated tobacco treatment service, with paid staff, in a large academic medical center. We describe treatment protocols, profile patients served, treatments provided, and summarization of 6‐month post‐discharge outcomes for smokers referred to the UKanQuit service over a 1‐year period. We close with lessons learned on how to improve the delivery of tobacco treatment to hospitalized patients.

Methods

Design and Setting

This is a descriptive observational study of a tobacco treatment program in a large Midwestern academic medical center between September 1, 2007 and August 31, 2008. The specialty tobacco treatment service (UKanQuit) was established when the hospital campus went smoke free on September 1, 2006. Patients are referred to the service via the hospital electronic medical record (EMR). As nurses complete electronic forms on patients admitted to their units, the EMR prompts nurses to ask patients if they smoke, ask smokers if they would like tobacco treatment medication to prevent withdrawal symptoms while in the hospital, and ask smokers if they would like to talk to a tobacco treatment specialist during their hospital stay. Those who respond yes to the final question are placed on an electronic list for UKanQuit services. Physicians and other health care providers can also order consultation from the UKanQuit service. A description of smokers admitted to the hospital and predictors of referral to UKanQuit within the first year of service is presented elsewhere.16, 17

The UKanQuit staff consists of an interdisciplinary team of counselors with a Ph.D., Masters degrees, and/or substantial experience in case management and substance abuse treatment. All have received intensive training and supervision in treating tobacco dependence. All participate in UKanQuit counseling on a part‐time basis, and spend the remainder of their effort as research assistants and counselors on smoking cessation research projects in the medical center. Hence, staffing consists of 1 full‐time equivalent counselor, 0.15 full‐time equivalent director (Richter), and 0.05 full‐time equivalent medical director (Ellerbeck). The program is funded through a contract with the hospital. We are in the process of hiring a nurse practitioner to create a more sustainable funding stream for the program because nurse practitioners can bill cessation services.

UKanQuit provides hospital counseling from 9 AM to 5 PM on weekdays. UKanQuit staff meets weekly for counseling supervision, strategic planning, continuing education, and troubleshooting difficult cases. In addition to treating smokers, the UKanQuit staff provides training and consultation to hospital personnel via grand rounds and other presentations. The service also provides a platform for medical students and residents to conduct focused research related to quality improvement. To facilitate systematic treatment of tobacco, UKanQuit developed the hospital treatment protocol for nursing staff, developed evidence‐based written self‐help materials that are accessible to hospital staff via the hospital printing system, and developed and instituted a tobacco treatment order set that was recently integrated into the EMR and automatically becomes prioritized as a recommended order set for all patients who report they have smoked in the past 30 days.

Procedures

UKanQuit staff retrieves patient details from the EMR and visits patients at their bedside. All hospital services refer to UKanQuit. UKanQuit provides counseling to Spanish speakers through bilingual/bicultural staff and hospital translators assist UKanQuit staff in counseling patients who speak other languages. The staff conducts a brief assessment at the bedside to inform treatment and contacts patients 6 months following inpatient treatment to assess outcomes and provide additional support and referral. This study evaluating the UKanQuit program was approved by the medical center's Institutional Review Board.

Program Intervention

UKanQuit staff visit patients at the bedside to deliver tobacco treatment. This consists of: (a) assessing withdrawal; (b) working with the health care team to adjust nicotine replacement to keep the patient comfortable; (c) assessing patients' interest in quitting smoking; (d) providing brief motivational intervention to patients not interested in quitting; and (e) providing assistance in quitting (developing a quit plan, arranging for medications on discharge) to patients interested in quitting (Figure 1). UKanQuit staff recommend medications based on the patients' level of dependence, history of cessation, and cessation medication preferences. The recommendation is communicated in person and by chart documentation to the medical team, usually by the nursing staff. The patients' resident or attending physician makes the final determination regarding medication provided. The hospital has nicotine replacement therapy (NRT; patch, gum, and lozenge), bupropion, and varenicline in its formulary. Patients are then offered an option of fax referral to the state tobacco quitline for follow‐up counseling. UKanQuit staff documents the services provided in the EMR via SOAR (Subjective, Objective, Assessment and Referral) notes.

Figure 1
Flow diagram showing the UkanQuit care process.

Measures

Baseline Measures

These were collected from the UKanQuit 1‐page program intake form, which UKanQuit designed to collect the minimal information necessary to conduct medication and behavioral counseling, to maximize counseling time, and to fit into the dense schedule of each patient's hospital stay. Demographic measures include age, gender and ethnicity. Smoking behavior measures include number of years smoked, number of cigarettes per day, a single item from the Fagerstrom Test for Nicotine Dependence that assesses time to first cigarette after waking,18, 19 interest in quitting smoking (on a 0‐10 scale, with 10 being very interested in quitting), and a single item from the self report version of the Minnesota Nicotine Withdrawal Scale (MNWS) that asks smokers to rate their desire or craving to smoke over a specified period. The single item craving measure from the MNWS has been found to have high reliability and good construct validity and is neither less sensitive to abstinence nor less reliable than the ten‐item brief questionnaire of smoking urges (QSU‐brief) used in laboratory and clinical trials.20 We asked about craving over the past 24 hours on a scale from 0 (none) to 4 (severe).21

Process Measures

Counselors also document the treatment they provided to smokers including the time spent with patients during counseling, provision of written self‐help materials, whether smokers set goals for quitting, hospital staff had already placed the smoker on a tobacco treatment medication, smokers are interested in increasing or changing their medication, the smoker wants smoking cessation medication on discharge, UKanQuit staff submitted a recommendation to hospital staff to make a medication change and/or provide a prescription for medication on discharge, plans for post‐discharge follow‐up (fax‐referral of patients to the state tobacco quitline or acceptance of UKanQuit counseling after discharge), and the patient agrees to be contacted at 6 months post‐discharge for follow‐up assessment and assistance.

Follow‐Up Measures

Outcome measures were collected by telephone 6 months post‐discharge by study staff who were not involved in the in‐hospital counseling. Call attempts to reach each patient ranged from 1 to 11. Measures included self‐reported 7‐day point prevalence abstinence rates, the number of quit attempts lasting over 24 hours, and cigarettes smoked per day among continuing smokers. Patients are asked if they participated in counseling through the tobacco quitline. Scaled (0‐10) items assess how important it is to the patients to quit smoking or remain quit, how confident they are in being able to quit or remain quit, and how satisfied they were with the assistance provided by UKanQuit. A yes/no item assesses whether patients think the program should be continued. In addition, UKanQuit asked two open‐ended questions to qualitatively assess satisfaction with the program and elicit suggestions for improvements. The questions were: What, if anything, was helpful to you about our services? and How can UKanQuit better help people stop smoking?

Analyses

Categorical variables were summarized by frequencies and percentages; continuous variables were summarized by means and standard deviations (SDs). We compared baseline characteristic differences between respondents and nonrespondents at 6 months follow‐up using chi‐square for categorical variables and t‐test for continuous variables. We also compared cigarettes per day at baseline and 6 months post‐discharge in smokers who were not able to quit using paired t‐test. All analyses were done with SPSS 17.0 statistical package. Open‐ended questions were analyzed using the framework synthesis method.22 Following examination and familiarization with the data, we developed an initial list of themes. We then categorized the responses by these themes using numerical codes. Each thematic code was summarized as a percentage of all responses. Those responses that fit into multiple thematic codes were multiply coded.

Results

Baseline

Within the study period (September 1, 2007 to August 31, 2008), 22,624 patients were admitted to the medical center (Figure 2). A total of 4150 were current smokers (ie, smoked within the past 30 days). UKanQuit staff met with 513 (68%) of 753 patients referred to the service. Some of the reasons why 32% of referred patients were not seen by the UKanQuit staff have been described in our previous paper.17 These include patient was asleep, doctor in the room, out of bed for procedure, and unable to speak. Table 1 displays the characteristics of 513 smokers treated by UKanQuit from September 1, 2007 to August 31, 2008. Patients were predominantly white (74%) with mean age of 50 years. Slightly more than half of smokers were male (57%). They had smoked an average of 18 cigarettes per day for a mean duration of 29 years, and over half (58%) smoked within 5 minutes of waking suggesting a high level of dependence. On a scale of 1 to 10 the mean interest in quitting was 7.9 (SD 2.9) and the mean craving score on a scale of 0 to 4 was 1.2 (SD 1.4) suggesting slight to mild craving.

Figure 2
Study flow diagram showing enrollment and follow‐up completion.
Demographic, Smoking Characteristics and Treatment Provided to 513 Patients Seen by UKanQuit Service From Sept 1, 2007 to Aug 31, 2008
CharacteristicsTreated (n = 513)
  • NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis.

  • Abbreviations: AA, Afro American; SD, standard deviation.

  • Interest in quitting range from 0 to 10.

  • craving range from 0 to 4.

Demographics 
Mean age (SD), years50.2 (13.6)
Male, n (%)291 (56.7)
Ethnicity, n (%) 
White371 (73.6)
AA107 (21.2)
Latino18 (3.6)
Other8 (1.6)
Referral source, n (%) 
Nursing profile477 (94.1)
Physician5 (1.0)
Other25 (4.9)
Smoking characteristics 
Mean number of years smoked (SD)28.9 (14.6)
Smokes within 5 minutes of waking n (%)270 (58.3)
Mean cigarettes smoked per day (SD)18.4 (12.6)
Mean interest in quitting (SD)*7.9 (2.9)
Mean craving (SD)1.2 (1.4)
Tobacco treatment provided 
Counseling 
Average time spent with patients (SD)19.9 (9.1)
Received information packet, n (%)490 (97.4)
Set goals for quitting, n (%)352 (73.3)
Had quit plan, n (%)151 (33.2)
Accepted fax referral to quitline, n (%)277 (55.8)
Opted for UKanQuit counseling, n (%)29 (5.9)
Medication 
On smoking cessation medication, n (%)133 (26.2)
Interested in receiving or changing smoking cessation medication, n (%)132 (26.7)
Added or changed smoking cessation medication, n (%)195 (40.5)
Discharge med, n (%)196 (40.2)

In‐Hospital Treatment

Hospital staff had placed 1 in 4 of the patients on smoking cessation medication prior to the UKanQuit staff visit. Nineteen percent were on NRT (16.2% transdermal patch, 2.5% on lozenge, 0.8% on nicotine gum); 5% on bupropion, 16.5% on varenicline, and 2.5% on clonidine. A total of 1.7% used a combination of Patch and bupropion while 2.5% used a combination of patch and gum. Staff provided 97% of the patients with written materials. Most patients (73%) set a goal for quitting or cutting down, and one‐third developed quit plans. Fifty‐six percent accepted fax referral to their state quitline, and 6% opted for follow‐up counseling with a UKanQuit counselor. Average time spent by UKanQuit with the patient was 20 minutes. Most of the patients treated (n = 426, 86%) agreed that UKanQuit staff can contact them for follow‐up assessment at 6 months.

Outcomes

Staff successfully contacted 196 (46%) of the 426 patients who agreed to 6‐month follow‐up. Responders were older (mean age 53 years, SD 12.6 vs. mean age 48 years, SD 13.8; P < 0.001); were more interested in quitting (mean interest in quitting 8.4, SD 2.5 vs. 7.6, SD 3.1 P = 0.001); and had a lower craving score at baseline (mean craving score 0.99, SD 1.3 vs. 1.29, SD 1.5; P < 0.001) compared to nonresponders. There were no differences between responders and nonresponders by gender, number of cigarettes smoked per day, years of smoking, referral source, inpatient smoking cessation medication used or time spent with UKanQuit hospital staff during the inpatient visit.

Table 2 displays smoking behavior and smoking cessation‐related characteristics of the respondents 6‐month post‐discharge. Over 70% attempted a quit attempt lasting at least 24 hours. The self reported 7‐day point prevalence abstinence rate was 31.8% among respondents. The intent‐to‐treat quit rate was 14.6% among all participants who agreed to follow‐up, counting those who we could not contact as smokers. While 34% used pharmacotherapy, only 5% of those who were fax‐referred to the quitline utilized the service. Most of the patients seen by the UKanQuit counselor considered quitting and staying quit important, mean 8.7, SD 2.3, and their confidence to quit or stay quit was above average, mean 6.6, SD 3.6. They rated the UKanQuit program very high, at 8.3, SD 2.8, on a scale of 0 to 10, and 98% of them wanted the program to continue. Of those who were not able to quit at 6 months, the mean number of cigarettes smoked per day decreased significantly from 17.8, SD 12.0 at baseline to 14.0, SD 9.7 at 6 months follow‐up (P < 0.001).

Follow‐Up Data Received From 196 Patients Seen by UKanQuit Between September 1, 2007 and August 31, 2008 (426 accepted 6 months follow‐up: response rate 46%)
Variables 
  • NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis.

  • Abbreviations: CPD, citrate phosphate dextrose; SD, standard deviation.

  • Faxed to quit line n = 121.

  • Importance/confidence, satisfaction with UKanQuit service range from 0 to 10.

Smoking characteristics 
7 day point prevalence abstinence rate, n (%)62 (31.8)
Among current smokers at 6 months follow‐up, n = 134 
Proportion of smokers who attempted to quit within 6 months, n (%)99 (73.9)
Mean CPD for smokers at 6 months (SD)14.0 (9.7)
Used formal quit smoking program 
Quit smoking medication, n (%)65 (34.4)
Quitline use among those faxed to quitline, n (%)*6 (5.0)
Importance/ Confidence 
How important is it to quit or stay quit, mean (SD)8.7 (2.3)
How confident are you to quit or stay quit, mean (SD)6.6 (3.6)
Views about service 
Satisfaction with UKanQuit service, mean (SD)8.3 (2.8)
Wants the UKanQuit program to continue, n (%)165 (97.6)

Satisfaction and Recommendations for Improvement

Most (96%) of participants contacted at follow‐up commented on what was helpful about the services. Table 3 displays the distribution of themes and illustrative comments. Themes included staff encouragement, support and counseling (41.8%); other (27%); information and education materials (20.9%); medication advice and referral (2.6%), and referral to quitline (0.5%)

What Was Helpful About UKanQuit Services
 n (%)
  • NOTE: n = represents number of comments coded. Some of the comments fall within multiple domains and were so coded. Other included don't remember visit, Don't know/Not sure/Can't think of anything.

Staff encouragement, support and counseling82 (41.8)
You guys did excellent. The friendliness of the people who visited me. 
Her outlook and her encouragement 
Other53 (27.1)
I don't remember the visit because I was heavily medicated, 
A lot was helpful but I couldn't tell you exactly what part was the most helpful 
Information/education material41 (20.9)
Provided me with a lot of info and the packet was helpful. 
The information packet 
Program not helpful14 (7.1)
I really didn't need their information, I was able to quit without it. 
Nothing helpful except the companionship 
Medication advice/referral5 (2.6)
They took the time and set up the patches for me 
She helped me with questions about medication, especially Chantix 
Referral to Quitline1 (0.5)
Talking on the phone 

Discussion

Among patients served by this inpatient program, interest in quitting was high but administration of inpatient medications upon admission was low (26%). Nearly all patients were provided with written materials, a majority set some form of goal for quitting or cutting down, and many developed quit plans and received assistance adjusting inpatient and/or discharge medications. After discharge, the majority of study participants made unassisted quit attempts, as utilization of medications and quitline services was suboptimal. Fax‐referral to quitline may not, on its own, fulfill guideline recommendations for post‐discharge follow‐up.

Our intent‐to‐treat quit rate was about half of what was found in Taylor et al.'s11 hospital program dissemination trial. Their program may have had better effects as it was somewhat more intensive. It included at least 1 follow‐up phone call immediately after discharge, as well as an accompanying video and relaxation audiotape or compact disc. However, differences in outcomes may also be due to large differences between the study populations. Hospitals participating in Taylor's study only conducted intervention and outcome assessment among smokers who were ready to quit, willing to enroll in a clinical trial, and willing to complete informed consent. Our intervention and outcome data included patients who agreed to speak with UKanQuit staff, regardless of readiness to quit. Our participants did not have to complete informed consent and enroll in a trial as our analyses were conducted post hoc. Our study outcomes might better reflect quit rates for a program serving all smokers, at all levels of readiness to quit, in actual hospital practices. The mean reduction in cigarette smoking among smokers who continued to smoke at 6 months' follow‐up was statistically significant. However, findings from a lung health study show that 50% or more reduction in smoking was ultimately related to successful quitting.23

Strengths and Limitations

The strengths of the study include the fact that the program attempts to intervene with all smokers, and provides stage‐appropriate intervention based on readiness to quit. It provides a snapshot of how a program is incorporated into clinical practice and describes implementation of protocol components.

This study has a number of limitations. We do not know exactly how many patients received the in‐hospital medication change agreed upon by the counselor and medical team immediately following the patient encounter. Our follow‐up rate was low and abstinence rates were based on self‐report, which limits our ability to draw conclusions about cessation outcomes. Process of care measures are based on counselor self‐report, without verification of services rendered. We are not able to identify the impact of our intervention above and beyond our patients' hospital experience, because we did not have a control group. We collected limited data from study participants so we are not able to better understand causes of nonadherence to quitline or poor pharmacotherapy utilization. Lastly, when the respondents were asked to comment about what was helpful about UKanQuit, 23% of the respondents said they could not remember the UKanQuit visit during their hospital stay. Many hospital medications induce brief amnesia, and patients have numerous consults during their stay and might not be able to separate one from the other. The 6‐month interval between their visit and follow‐up call may also account for their inability to remember the cessation consult.

Our patient population is in fact a subset of all smokers admitted (11% of smokers) because they are motivated enough to agree to talk with a counselor. Our intervention procedures, and results, might be quite different if all smokers were visited by the counselor. Efficacy trials of tobacco treatment in hospitals have focused on smokers who are ready to quit.24 Hence, procedures for working with unmotivated smokers in hospitals are less well established. Policymakers, hospitals, and hospital tobacco treatment programs should examine the most efficient (ie, effective and cost‐effective) approaches for addressing smoking in hospitals and specifically focus on whether all smokers should be treated by dedicated tobacco treatment staff, or only those who agree to a consult.

Lessons Learned

Linking Patients With In‐Hospital Cessation Medications Requires Collaboration With the Entire Health Care Team

Only 1 in 4 UKanQuit participants had been given smoking cessation medication to ameliorate withdrawal before counselors met with the patients. Although we have not systematically collected reasons patients do not receive cessation medication on admission, the 2 most common causes are that patients refuse it or physicians refuse it. Patients refuse medication perhaps because they do not want to quit, they feel they will cope without smoking during their hospital stay, or they are paying out of pocket and want to reduce costs. Physicians do not permit it because they believe it is contraindicated for the patient's health condition, it is contraindicated for the procedure the patient is receiving in the hospital, or they believe it will interfere with wound healing. There is also a considerable delay between ordering and receiving medications; patients who become uncomfortable during their stay sometimes change their minds, but end up being discharged before their medication arrives. Most of these issues pertain to nicotine replacement. Although patients not eligible for NRT may be good candidates for bupropion, varenicline, or even the second line cessation medications of clonidine or nortryptiline, these medications do not provide immediate relief from tobacco withdrawal symptoms and staff are reluctant to start patients on medications they may not receive on an outpatient basis. It is not clear what proportion of hospitalized patients should receive ameliorative medication. Not every hospitalized smoker is a candidate for NRT, due to contraindicated medical conditions, patients' level of dependence, and patients' willingness to accept cessation medication in order to prevent withdrawal. Koplan et al.25 achieved hospital‐wide increases in NRT orders from 1.6% to 2.5% after the introduction of an electronic tobacco treatment order set. These percentages seem low but actually were calculated from all hospital admissions, including smokers and nonsmokers. Moreover, their hospital population had a relatively low smoking rate of 12%. Our in‐hospital and post‐discharge (26.2% and 34.4% respectively) pharmacotherapy utilization rates were based only on smokers who had been seen by our service. Even though 1 in 4 smokers were already on medication when they were seen by counselors, 1 in 4 of patients seen wanted to either add a cessation medication or change their current dose. There is clearly room for improvement in how we offer and administer cessation medications on admission. Also, assessing medication efficacy and adjusting as needed appears to be an important role for in‐hospital counselors.

Facilitating Medications Post‐Discharge Will Require Creativity and Outpatient Follow‐Up

Post‐discharge, only 1 in 3 of our patients reported they used cessation medications. This may, again, be a function of patients' readiness to quit. However, it could also be related to knowledge/attitudes regarding the efficacy of medications or access to low‐cost medications. Some of our patients commented that making medication affordable would be helpful. Although our materials provide information on sources for free or low‐cost medications, this information may not have been salient during the hospital stay. To increase access to medications post‐discharge, programs should consider providing a booster mailer to the home with information on sources for free or reduced medications, providing take‐home starter pharmacotherapy kits lasting 1 to 2 weeks to bridge the gap between hospital discharge and finding another source of medications, and/or a follow‐up call shortly after discharge to verify use of pharmacotherapy and troubleshoot problems with medications or procurement.

Providing Follow‐Up Via Fax Referral to Quitlines Is Not as Simple as It Seems

Although our overall quitline fax‐referral rate was high (over half of all patients seen), rate of enrollment among those referred is much lower than the rates reported elsewhere, which range from 16% to 53%.2628 In our sample of fax‐referred smokers, we do not know how many were not enrolled due to failure to make contact vs. patient refusal once contact was made. One possible factor impacting enrollment rates is whether or not smokers are prescreened for readiness to quit. Nearly half of US quitlines require smokers to be ready to quit in order to receive a full course of treatment,29 but only 20% of smokers are ready to quit at any given time.30 In the cited studies with higher conversion rates, counselors prescreened patients for readiness to quit and only offered fax‐referral to those ready to quit in the next 30 days. Our program offers fax‐referral to all smokers. Our findings suggest that doing so results in high rates of referral but low rates of enrollment among those referred. Future studies should examine the impact of prescreening for readiness versus offering referral to all smokers on net enrollment and cessation.

Linking hospitalized smokers with tobacco quitlines has many potential benefits.31, 32 Proactive tobacco quitlines are effective15 and cost effective33 for smoking cessation; they are available, free, to all US smokers; services are delivered via telephone which minimizes many access barriers; hospitals do not have to bear the costs of the services; and many quitlines are undersubscribed and eager to increase their reach.34 Potential methods for increasing conversion to enrollment include building motivation to accept counseling and preparing patients for the quitline intake procedures. Our program is considering providing a warm handoff to patients by calling the quitline during the bedside consult to permit the quitline to enroll the patient during their hospital stay.

Hospital‐based cessation programs have the potential to deliver tobacco treatment to millions of hospitalized smokers annually. To deliver high‐quality, effective care, hospital cessation programs will have to solve problems inherent in hospital‐based carehow best to integrate into existing hospital systems, how to effectively communicate with other hospital care providers, and how to facilitate transitions in care to ensure patients receive evidence‐based post‐discharge care. We offer this report as the first of hopefully many that address quality improvement for specialized programs dedicated to treating tobacco in hospitals.

Acknowledgements

The authors gratefully acknowledge the contributions of the following UKanQuit Counselors in the planning and development of this manuscript: Brian Hernandez, Alex Perez‐Estrada, Grace Meikenhous, Meredith Benson, Terri Tapp. We also thank Chip Hulen, Albers Bart and Chris Wittkopp of the Organizational Improvement Department of KU Hospital; Marilyn Painter, Joanne McNair and Karisa Deculus of the KU Preventive Medicine and Public Health Department.

References
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Article PDF
Issue
Journal of Hospital Medicine - 6(4)
Page Number
211-218
Legacy Keywords
outcomes measurement, quality improvement, smoking cessation
Sections
Article PDF
Article PDF

Hospitalization can be considered a teachable moment for smoking cessation13 for the 6.5 million adult smokers who are hospitalized in the United States each year.4 Smokers who receive tobacco treatment during hospitalization and outpatient follow‐up treatment for at least 1 month are more likely to quit than patients who receive no treatment.5, 6

Unless tobacco treatment is explicitly delegated to other providers, physicians shoulder the responsibility of encouraging smokers to quit and prescribing smoking cessation medications. This is problematic in that physicians sometimes fail to counsel their patients about quitting smoking7, 8 or recommend outpatient follow‐up.9 Few hospitals provide comprehensive treatment. In a review of 33 studies on the prevalence of smoking care delivery in hospitals, 3 hospitals reported they provided advice to quit alone, 29 provided advice plus counseling and assistance in quitting, and 8 provided advice or prescription for cessation pharmacotherapy.9 Although post‐discharge support is a key component of effective treatment for hospitalized smokers,6 only 11 reported providing follow‐up treatment, or referral for follow‐up treatment, after discharge. Among these 11 hospitals, respondents reported they provided referral or follow‐up to 1% to 74% of their smokers, with a median percentage of 24%. The 1 study that specified the type of outpatient treatment provided reported the hospital provided the state quitline number to smokers.

Instituting a dedicated smoking cessation program may enhance inpatient treatment, outpatient follow‐up, and treatment outcomes. Two studies have found that institutional smoking cessation programs increased the likelihood that patients would receive treatment and quit compared to hospitals without dedicated programs.10, 11

Although many US hospitals are developing programs to provide systematic treatment for tobacco dependence,9 little is known regarding how programs structure their staff, enroll patients, or provide treatment to patients that smoke. Instituting tobacco treatment services usually requires policy change and system‐wide approaches with quality improvement endpoint goals.8, 1214 In the United States, elements of these services include: 1) developing a cadre of trained tobacco treatment specialists, 2) implementing hospital systems for identifying smokers and referring them to the service, 3) providing inpatient treatment based on current treatment guidelines15 and 4) providing or facilitating follow‐up treatment after discharge, often via fax‐referral to tobacco quitlines. This systematic approach is still lacking in many hospitals.

To date, few evaluations of dedicated hospital‐based smoking cessation programs have been reported in the literature.8, 11 The purpose of this study is to describe patient characteristics and outcomes of a dedicated tobacco treatment service, with paid staff, in a large academic medical center. We describe treatment protocols, profile patients served, treatments provided, and summarization of 6‐month post‐discharge outcomes for smokers referred to the UKanQuit service over a 1‐year period. We close with lessons learned on how to improve the delivery of tobacco treatment to hospitalized patients.

Methods

Design and Setting

This is a descriptive observational study of a tobacco treatment program in a large Midwestern academic medical center between September 1, 2007 and August 31, 2008. The specialty tobacco treatment service (UKanQuit) was established when the hospital campus went smoke free on September 1, 2006. Patients are referred to the service via the hospital electronic medical record (EMR). As nurses complete electronic forms on patients admitted to their units, the EMR prompts nurses to ask patients if they smoke, ask smokers if they would like tobacco treatment medication to prevent withdrawal symptoms while in the hospital, and ask smokers if they would like to talk to a tobacco treatment specialist during their hospital stay. Those who respond yes to the final question are placed on an electronic list for UKanQuit services. Physicians and other health care providers can also order consultation from the UKanQuit service. A description of smokers admitted to the hospital and predictors of referral to UKanQuit within the first year of service is presented elsewhere.16, 17

The UKanQuit staff consists of an interdisciplinary team of counselors with a Ph.D., Masters degrees, and/or substantial experience in case management and substance abuse treatment. All have received intensive training and supervision in treating tobacco dependence. All participate in UKanQuit counseling on a part‐time basis, and spend the remainder of their effort as research assistants and counselors on smoking cessation research projects in the medical center. Hence, staffing consists of 1 full‐time equivalent counselor, 0.15 full‐time equivalent director (Richter), and 0.05 full‐time equivalent medical director (Ellerbeck). The program is funded through a contract with the hospital. We are in the process of hiring a nurse practitioner to create a more sustainable funding stream for the program because nurse practitioners can bill cessation services.

UKanQuit provides hospital counseling from 9 AM to 5 PM on weekdays. UKanQuit staff meets weekly for counseling supervision, strategic planning, continuing education, and troubleshooting difficult cases. In addition to treating smokers, the UKanQuit staff provides training and consultation to hospital personnel via grand rounds and other presentations. The service also provides a platform for medical students and residents to conduct focused research related to quality improvement. To facilitate systematic treatment of tobacco, UKanQuit developed the hospital treatment protocol for nursing staff, developed evidence‐based written self‐help materials that are accessible to hospital staff via the hospital printing system, and developed and instituted a tobacco treatment order set that was recently integrated into the EMR and automatically becomes prioritized as a recommended order set for all patients who report they have smoked in the past 30 days.

Procedures

UKanQuit staff retrieves patient details from the EMR and visits patients at their bedside. All hospital services refer to UKanQuit. UKanQuit provides counseling to Spanish speakers through bilingual/bicultural staff and hospital translators assist UKanQuit staff in counseling patients who speak other languages. The staff conducts a brief assessment at the bedside to inform treatment and contacts patients 6 months following inpatient treatment to assess outcomes and provide additional support and referral. This study evaluating the UKanQuit program was approved by the medical center's Institutional Review Board.

Program Intervention

UKanQuit staff visit patients at the bedside to deliver tobacco treatment. This consists of: (a) assessing withdrawal; (b) working with the health care team to adjust nicotine replacement to keep the patient comfortable; (c) assessing patients' interest in quitting smoking; (d) providing brief motivational intervention to patients not interested in quitting; and (e) providing assistance in quitting (developing a quit plan, arranging for medications on discharge) to patients interested in quitting (Figure 1). UKanQuit staff recommend medications based on the patients' level of dependence, history of cessation, and cessation medication preferences. The recommendation is communicated in person and by chart documentation to the medical team, usually by the nursing staff. The patients' resident or attending physician makes the final determination regarding medication provided. The hospital has nicotine replacement therapy (NRT; patch, gum, and lozenge), bupropion, and varenicline in its formulary. Patients are then offered an option of fax referral to the state tobacco quitline for follow‐up counseling. UKanQuit staff documents the services provided in the EMR via SOAR (Subjective, Objective, Assessment and Referral) notes.

Figure 1
Flow diagram showing the UkanQuit care process.

Measures

Baseline Measures

These were collected from the UKanQuit 1‐page program intake form, which UKanQuit designed to collect the minimal information necessary to conduct medication and behavioral counseling, to maximize counseling time, and to fit into the dense schedule of each patient's hospital stay. Demographic measures include age, gender and ethnicity. Smoking behavior measures include number of years smoked, number of cigarettes per day, a single item from the Fagerstrom Test for Nicotine Dependence that assesses time to first cigarette after waking,18, 19 interest in quitting smoking (on a 0‐10 scale, with 10 being very interested in quitting), and a single item from the self report version of the Minnesota Nicotine Withdrawal Scale (MNWS) that asks smokers to rate their desire or craving to smoke over a specified period. The single item craving measure from the MNWS has been found to have high reliability and good construct validity and is neither less sensitive to abstinence nor less reliable than the ten‐item brief questionnaire of smoking urges (QSU‐brief) used in laboratory and clinical trials.20 We asked about craving over the past 24 hours on a scale from 0 (none) to 4 (severe).21

Process Measures

Counselors also document the treatment they provided to smokers including the time spent with patients during counseling, provision of written self‐help materials, whether smokers set goals for quitting, hospital staff had already placed the smoker on a tobacco treatment medication, smokers are interested in increasing or changing their medication, the smoker wants smoking cessation medication on discharge, UKanQuit staff submitted a recommendation to hospital staff to make a medication change and/or provide a prescription for medication on discharge, plans for post‐discharge follow‐up (fax‐referral of patients to the state tobacco quitline or acceptance of UKanQuit counseling after discharge), and the patient agrees to be contacted at 6 months post‐discharge for follow‐up assessment and assistance.

Follow‐Up Measures

Outcome measures were collected by telephone 6 months post‐discharge by study staff who were not involved in the in‐hospital counseling. Call attempts to reach each patient ranged from 1 to 11. Measures included self‐reported 7‐day point prevalence abstinence rates, the number of quit attempts lasting over 24 hours, and cigarettes smoked per day among continuing smokers. Patients are asked if they participated in counseling through the tobacco quitline. Scaled (0‐10) items assess how important it is to the patients to quit smoking or remain quit, how confident they are in being able to quit or remain quit, and how satisfied they were with the assistance provided by UKanQuit. A yes/no item assesses whether patients think the program should be continued. In addition, UKanQuit asked two open‐ended questions to qualitatively assess satisfaction with the program and elicit suggestions for improvements. The questions were: What, if anything, was helpful to you about our services? and How can UKanQuit better help people stop smoking?

Analyses

Categorical variables were summarized by frequencies and percentages; continuous variables were summarized by means and standard deviations (SDs). We compared baseline characteristic differences between respondents and nonrespondents at 6 months follow‐up using chi‐square for categorical variables and t‐test for continuous variables. We also compared cigarettes per day at baseline and 6 months post‐discharge in smokers who were not able to quit using paired t‐test. All analyses were done with SPSS 17.0 statistical package. Open‐ended questions were analyzed using the framework synthesis method.22 Following examination and familiarization with the data, we developed an initial list of themes. We then categorized the responses by these themes using numerical codes. Each thematic code was summarized as a percentage of all responses. Those responses that fit into multiple thematic codes were multiply coded.

Results

Baseline

Within the study period (September 1, 2007 to August 31, 2008), 22,624 patients were admitted to the medical center (Figure 2). A total of 4150 were current smokers (ie, smoked within the past 30 days). UKanQuit staff met with 513 (68%) of 753 patients referred to the service. Some of the reasons why 32% of referred patients were not seen by the UKanQuit staff have been described in our previous paper.17 These include patient was asleep, doctor in the room, out of bed for procedure, and unable to speak. Table 1 displays the characteristics of 513 smokers treated by UKanQuit from September 1, 2007 to August 31, 2008. Patients were predominantly white (74%) with mean age of 50 years. Slightly more than half of smokers were male (57%). They had smoked an average of 18 cigarettes per day for a mean duration of 29 years, and over half (58%) smoked within 5 minutes of waking suggesting a high level of dependence. On a scale of 1 to 10 the mean interest in quitting was 7.9 (SD 2.9) and the mean craving score on a scale of 0 to 4 was 1.2 (SD 1.4) suggesting slight to mild craving.

Figure 2
Study flow diagram showing enrollment and follow‐up completion.
Demographic, Smoking Characteristics and Treatment Provided to 513 Patients Seen by UKanQuit Service From Sept 1, 2007 to Aug 31, 2008
CharacteristicsTreated (n = 513)
  • NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis.

  • Abbreviations: AA, Afro American; SD, standard deviation.

  • Interest in quitting range from 0 to 10.

  • craving range from 0 to 4.

Demographics 
Mean age (SD), years50.2 (13.6)
Male, n (%)291 (56.7)
Ethnicity, n (%) 
White371 (73.6)
AA107 (21.2)
Latino18 (3.6)
Other8 (1.6)
Referral source, n (%) 
Nursing profile477 (94.1)
Physician5 (1.0)
Other25 (4.9)
Smoking characteristics 
Mean number of years smoked (SD)28.9 (14.6)
Smokes within 5 minutes of waking n (%)270 (58.3)
Mean cigarettes smoked per day (SD)18.4 (12.6)
Mean interest in quitting (SD)*7.9 (2.9)
Mean craving (SD)1.2 (1.4)
Tobacco treatment provided 
Counseling 
Average time spent with patients (SD)19.9 (9.1)
Received information packet, n (%)490 (97.4)
Set goals for quitting, n (%)352 (73.3)
Had quit plan, n (%)151 (33.2)
Accepted fax referral to quitline, n (%)277 (55.8)
Opted for UKanQuit counseling, n (%)29 (5.9)
Medication 
On smoking cessation medication, n (%)133 (26.2)
Interested in receiving or changing smoking cessation medication, n (%)132 (26.7)
Added or changed smoking cessation medication, n (%)195 (40.5)
Discharge med, n (%)196 (40.2)

In‐Hospital Treatment

Hospital staff had placed 1 in 4 of the patients on smoking cessation medication prior to the UKanQuit staff visit. Nineteen percent were on NRT (16.2% transdermal patch, 2.5% on lozenge, 0.8% on nicotine gum); 5% on bupropion, 16.5% on varenicline, and 2.5% on clonidine. A total of 1.7% used a combination of Patch and bupropion while 2.5% used a combination of patch and gum. Staff provided 97% of the patients with written materials. Most patients (73%) set a goal for quitting or cutting down, and one‐third developed quit plans. Fifty‐six percent accepted fax referral to their state quitline, and 6% opted for follow‐up counseling with a UKanQuit counselor. Average time spent by UKanQuit with the patient was 20 minutes. Most of the patients treated (n = 426, 86%) agreed that UKanQuit staff can contact them for follow‐up assessment at 6 months.

Outcomes

Staff successfully contacted 196 (46%) of the 426 patients who agreed to 6‐month follow‐up. Responders were older (mean age 53 years, SD 12.6 vs. mean age 48 years, SD 13.8; P < 0.001); were more interested in quitting (mean interest in quitting 8.4, SD 2.5 vs. 7.6, SD 3.1 P = 0.001); and had a lower craving score at baseline (mean craving score 0.99, SD 1.3 vs. 1.29, SD 1.5; P < 0.001) compared to nonresponders. There were no differences between responders and nonresponders by gender, number of cigarettes smoked per day, years of smoking, referral source, inpatient smoking cessation medication used or time spent with UKanQuit hospital staff during the inpatient visit.

Table 2 displays smoking behavior and smoking cessation‐related characteristics of the respondents 6‐month post‐discharge. Over 70% attempted a quit attempt lasting at least 24 hours. The self reported 7‐day point prevalence abstinence rate was 31.8% among respondents. The intent‐to‐treat quit rate was 14.6% among all participants who agreed to follow‐up, counting those who we could not contact as smokers. While 34% used pharmacotherapy, only 5% of those who were fax‐referred to the quitline utilized the service. Most of the patients seen by the UKanQuit counselor considered quitting and staying quit important, mean 8.7, SD 2.3, and their confidence to quit or stay quit was above average, mean 6.6, SD 3.6. They rated the UKanQuit program very high, at 8.3, SD 2.8, on a scale of 0 to 10, and 98% of them wanted the program to continue. Of those who were not able to quit at 6 months, the mean number of cigarettes smoked per day decreased significantly from 17.8, SD 12.0 at baseline to 14.0, SD 9.7 at 6 months follow‐up (P < 0.001).

Follow‐Up Data Received From 196 Patients Seen by UKanQuit Between September 1, 2007 and August 31, 2008 (426 accepted 6 months follow‐up: response rate 46%)
Variables 
  • NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis.

  • Abbreviations: CPD, citrate phosphate dextrose; SD, standard deviation.

  • Faxed to quit line n = 121.

  • Importance/confidence, satisfaction with UKanQuit service range from 0 to 10.

Smoking characteristics 
7 day point prevalence abstinence rate, n (%)62 (31.8)
Among current smokers at 6 months follow‐up, n = 134 
Proportion of smokers who attempted to quit within 6 months, n (%)99 (73.9)
Mean CPD for smokers at 6 months (SD)14.0 (9.7)
Used formal quit smoking program 
Quit smoking medication, n (%)65 (34.4)
Quitline use among those faxed to quitline, n (%)*6 (5.0)
Importance/ Confidence 
How important is it to quit or stay quit, mean (SD)8.7 (2.3)
How confident are you to quit or stay quit, mean (SD)6.6 (3.6)
Views about service 
Satisfaction with UKanQuit service, mean (SD)8.3 (2.8)
Wants the UKanQuit program to continue, n (%)165 (97.6)

Satisfaction and Recommendations for Improvement

Most (96%) of participants contacted at follow‐up commented on what was helpful about the services. Table 3 displays the distribution of themes and illustrative comments. Themes included staff encouragement, support and counseling (41.8%); other (27%); information and education materials (20.9%); medication advice and referral (2.6%), and referral to quitline (0.5%)

What Was Helpful About UKanQuit Services
 n (%)
  • NOTE: n = represents number of comments coded. Some of the comments fall within multiple domains and were so coded. Other included don't remember visit, Don't know/Not sure/Can't think of anything.

Staff encouragement, support and counseling82 (41.8)
You guys did excellent. The friendliness of the people who visited me. 
Her outlook and her encouragement 
Other53 (27.1)
I don't remember the visit because I was heavily medicated, 
A lot was helpful but I couldn't tell you exactly what part was the most helpful 
Information/education material41 (20.9)
Provided me with a lot of info and the packet was helpful. 
The information packet 
Program not helpful14 (7.1)
I really didn't need their information, I was able to quit without it. 
Nothing helpful except the companionship 
Medication advice/referral5 (2.6)
They took the time and set up the patches for me 
She helped me with questions about medication, especially Chantix 
Referral to Quitline1 (0.5)
Talking on the phone 

Discussion

Among patients served by this inpatient program, interest in quitting was high but administration of inpatient medications upon admission was low (26%). Nearly all patients were provided with written materials, a majority set some form of goal for quitting or cutting down, and many developed quit plans and received assistance adjusting inpatient and/or discharge medications. After discharge, the majority of study participants made unassisted quit attempts, as utilization of medications and quitline services was suboptimal. Fax‐referral to quitline may not, on its own, fulfill guideline recommendations for post‐discharge follow‐up.

Our intent‐to‐treat quit rate was about half of what was found in Taylor et al.'s11 hospital program dissemination trial. Their program may have had better effects as it was somewhat more intensive. It included at least 1 follow‐up phone call immediately after discharge, as well as an accompanying video and relaxation audiotape or compact disc. However, differences in outcomes may also be due to large differences between the study populations. Hospitals participating in Taylor's study only conducted intervention and outcome assessment among smokers who were ready to quit, willing to enroll in a clinical trial, and willing to complete informed consent. Our intervention and outcome data included patients who agreed to speak with UKanQuit staff, regardless of readiness to quit. Our participants did not have to complete informed consent and enroll in a trial as our analyses were conducted post hoc. Our study outcomes might better reflect quit rates for a program serving all smokers, at all levels of readiness to quit, in actual hospital practices. The mean reduction in cigarette smoking among smokers who continued to smoke at 6 months' follow‐up was statistically significant. However, findings from a lung health study show that 50% or more reduction in smoking was ultimately related to successful quitting.23

Strengths and Limitations

The strengths of the study include the fact that the program attempts to intervene with all smokers, and provides stage‐appropriate intervention based on readiness to quit. It provides a snapshot of how a program is incorporated into clinical practice and describes implementation of protocol components.

This study has a number of limitations. We do not know exactly how many patients received the in‐hospital medication change agreed upon by the counselor and medical team immediately following the patient encounter. Our follow‐up rate was low and abstinence rates were based on self‐report, which limits our ability to draw conclusions about cessation outcomes. Process of care measures are based on counselor self‐report, without verification of services rendered. We are not able to identify the impact of our intervention above and beyond our patients' hospital experience, because we did not have a control group. We collected limited data from study participants so we are not able to better understand causes of nonadherence to quitline or poor pharmacotherapy utilization. Lastly, when the respondents were asked to comment about what was helpful about UKanQuit, 23% of the respondents said they could not remember the UKanQuit visit during their hospital stay. Many hospital medications induce brief amnesia, and patients have numerous consults during their stay and might not be able to separate one from the other. The 6‐month interval between their visit and follow‐up call may also account for their inability to remember the cessation consult.

Our patient population is in fact a subset of all smokers admitted (11% of smokers) because they are motivated enough to agree to talk with a counselor. Our intervention procedures, and results, might be quite different if all smokers were visited by the counselor. Efficacy trials of tobacco treatment in hospitals have focused on smokers who are ready to quit.24 Hence, procedures for working with unmotivated smokers in hospitals are less well established. Policymakers, hospitals, and hospital tobacco treatment programs should examine the most efficient (ie, effective and cost‐effective) approaches for addressing smoking in hospitals and specifically focus on whether all smokers should be treated by dedicated tobacco treatment staff, or only those who agree to a consult.

Lessons Learned

Linking Patients With In‐Hospital Cessation Medications Requires Collaboration With the Entire Health Care Team

Only 1 in 4 UKanQuit participants had been given smoking cessation medication to ameliorate withdrawal before counselors met with the patients. Although we have not systematically collected reasons patients do not receive cessation medication on admission, the 2 most common causes are that patients refuse it or physicians refuse it. Patients refuse medication perhaps because they do not want to quit, they feel they will cope without smoking during their hospital stay, or they are paying out of pocket and want to reduce costs. Physicians do not permit it because they believe it is contraindicated for the patient's health condition, it is contraindicated for the procedure the patient is receiving in the hospital, or they believe it will interfere with wound healing. There is also a considerable delay between ordering and receiving medications; patients who become uncomfortable during their stay sometimes change their minds, but end up being discharged before their medication arrives. Most of these issues pertain to nicotine replacement. Although patients not eligible for NRT may be good candidates for bupropion, varenicline, or even the second line cessation medications of clonidine or nortryptiline, these medications do not provide immediate relief from tobacco withdrawal symptoms and staff are reluctant to start patients on medications they may not receive on an outpatient basis. It is not clear what proportion of hospitalized patients should receive ameliorative medication. Not every hospitalized smoker is a candidate for NRT, due to contraindicated medical conditions, patients' level of dependence, and patients' willingness to accept cessation medication in order to prevent withdrawal. Koplan et al.25 achieved hospital‐wide increases in NRT orders from 1.6% to 2.5% after the introduction of an electronic tobacco treatment order set. These percentages seem low but actually were calculated from all hospital admissions, including smokers and nonsmokers. Moreover, their hospital population had a relatively low smoking rate of 12%. Our in‐hospital and post‐discharge (26.2% and 34.4% respectively) pharmacotherapy utilization rates were based only on smokers who had been seen by our service. Even though 1 in 4 smokers were already on medication when they were seen by counselors, 1 in 4 of patients seen wanted to either add a cessation medication or change their current dose. There is clearly room for improvement in how we offer and administer cessation medications on admission. Also, assessing medication efficacy and adjusting as needed appears to be an important role for in‐hospital counselors.

Facilitating Medications Post‐Discharge Will Require Creativity and Outpatient Follow‐Up

Post‐discharge, only 1 in 3 of our patients reported they used cessation medications. This may, again, be a function of patients' readiness to quit. However, it could also be related to knowledge/attitudes regarding the efficacy of medications or access to low‐cost medications. Some of our patients commented that making medication affordable would be helpful. Although our materials provide information on sources for free or low‐cost medications, this information may not have been salient during the hospital stay. To increase access to medications post‐discharge, programs should consider providing a booster mailer to the home with information on sources for free or reduced medications, providing take‐home starter pharmacotherapy kits lasting 1 to 2 weeks to bridge the gap between hospital discharge and finding another source of medications, and/or a follow‐up call shortly after discharge to verify use of pharmacotherapy and troubleshoot problems with medications or procurement.

Providing Follow‐Up Via Fax Referral to Quitlines Is Not as Simple as It Seems

Although our overall quitline fax‐referral rate was high (over half of all patients seen), rate of enrollment among those referred is much lower than the rates reported elsewhere, which range from 16% to 53%.2628 In our sample of fax‐referred smokers, we do not know how many were not enrolled due to failure to make contact vs. patient refusal once contact was made. One possible factor impacting enrollment rates is whether or not smokers are prescreened for readiness to quit. Nearly half of US quitlines require smokers to be ready to quit in order to receive a full course of treatment,29 but only 20% of smokers are ready to quit at any given time.30 In the cited studies with higher conversion rates, counselors prescreened patients for readiness to quit and only offered fax‐referral to those ready to quit in the next 30 days. Our program offers fax‐referral to all smokers. Our findings suggest that doing so results in high rates of referral but low rates of enrollment among those referred. Future studies should examine the impact of prescreening for readiness versus offering referral to all smokers on net enrollment and cessation.

Linking hospitalized smokers with tobacco quitlines has many potential benefits.31, 32 Proactive tobacco quitlines are effective15 and cost effective33 for smoking cessation; they are available, free, to all US smokers; services are delivered via telephone which minimizes many access barriers; hospitals do not have to bear the costs of the services; and many quitlines are undersubscribed and eager to increase their reach.34 Potential methods for increasing conversion to enrollment include building motivation to accept counseling and preparing patients for the quitline intake procedures. Our program is considering providing a warm handoff to patients by calling the quitline during the bedside consult to permit the quitline to enroll the patient during their hospital stay.

Hospital‐based cessation programs have the potential to deliver tobacco treatment to millions of hospitalized smokers annually. To deliver high‐quality, effective care, hospital cessation programs will have to solve problems inherent in hospital‐based carehow best to integrate into existing hospital systems, how to effectively communicate with other hospital care providers, and how to facilitate transitions in care to ensure patients receive evidence‐based post‐discharge care. We offer this report as the first of hopefully many that address quality improvement for specialized programs dedicated to treating tobacco in hospitals.

Acknowledgements

The authors gratefully acknowledge the contributions of the following UKanQuit Counselors in the planning and development of this manuscript: Brian Hernandez, Alex Perez‐Estrada, Grace Meikenhous, Meredith Benson, Terri Tapp. We also thank Chip Hulen, Albers Bart and Chris Wittkopp of the Organizational Improvement Department of KU Hospital; Marilyn Painter, Joanne McNair and Karisa Deculus of the KU Preventive Medicine and Public Health Department.

Hospitalization can be considered a teachable moment for smoking cessation13 for the 6.5 million adult smokers who are hospitalized in the United States each year.4 Smokers who receive tobacco treatment during hospitalization and outpatient follow‐up treatment for at least 1 month are more likely to quit than patients who receive no treatment.5, 6

Unless tobacco treatment is explicitly delegated to other providers, physicians shoulder the responsibility of encouraging smokers to quit and prescribing smoking cessation medications. This is problematic in that physicians sometimes fail to counsel their patients about quitting smoking7, 8 or recommend outpatient follow‐up.9 Few hospitals provide comprehensive treatment. In a review of 33 studies on the prevalence of smoking care delivery in hospitals, 3 hospitals reported they provided advice to quit alone, 29 provided advice plus counseling and assistance in quitting, and 8 provided advice or prescription for cessation pharmacotherapy.9 Although post‐discharge support is a key component of effective treatment for hospitalized smokers,6 only 11 reported providing follow‐up treatment, or referral for follow‐up treatment, after discharge. Among these 11 hospitals, respondents reported they provided referral or follow‐up to 1% to 74% of their smokers, with a median percentage of 24%. The 1 study that specified the type of outpatient treatment provided reported the hospital provided the state quitline number to smokers.

Instituting a dedicated smoking cessation program may enhance inpatient treatment, outpatient follow‐up, and treatment outcomes. Two studies have found that institutional smoking cessation programs increased the likelihood that patients would receive treatment and quit compared to hospitals without dedicated programs.10, 11

Although many US hospitals are developing programs to provide systematic treatment for tobacco dependence,9 little is known regarding how programs structure their staff, enroll patients, or provide treatment to patients that smoke. Instituting tobacco treatment services usually requires policy change and system‐wide approaches with quality improvement endpoint goals.8, 1214 In the United States, elements of these services include: 1) developing a cadre of trained tobacco treatment specialists, 2) implementing hospital systems for identifying smokers and referring them to the service, 3) providing inpatient treatment based on current treatment guidelines15 and 4) providing or facilitating follow‐up treatment after discharge, often via fax‐referral to tobacco quitlines. This systematic approach is still lacking in many hospitals.

To date, few evaluations of dedicated hospital‐based smoking cessation programs have been reported in the literature.8, 11 The purpose of this study is to describe patient characteristics and outcomes of a dedicated tobacco treatment service, with paid staff, in a large academic medical center. We describe treatment protocols, profile patients served, treatments provided, and summarization of 6‐month post‐discharge outcomes for smokers referred to the UKanQuit service over a 1‐year period. We close with lessons learned on how to improve the delivery of tobacco treatment to hospitalized patients.

Methods

Design and Setting

This is a descriptive observational study of a tobacco treatment program in a large Midwestern academic medical center between September 1, 2007 and August 31, 2008. The specialty tobacco treatment service (UKanQuit) was established when the hospital campus went smoke free on September 1, 2006. Patients are referred to the service via the hospital electronic medical record (EMR). As nurses complete electronic forms on patients admitted to their units, the EMR prompts nurses to ask patients if they smoke, ask smokers if they would like tobacco treatment medication to prevent withdrawal symptoms while in the hospital, and ask smokers if they would like to talk to a tobacco treatment specialist during their hospital stay. Those who respond yes to the final question are placed on an electronic list for UKanQuit services. Physicians and other health care providers can also order consultation from the UKanQuit service. A description of smokers admitted to the hospital and predictors of referral to UKanQuit within the first year of service is presented elsewhere.16, 17

The UKanQuit staff consists of an interdisciplinary team of counselors with a Ph.D., Masters degrees, and/or substantial experience in case management and substance abuse treatment. All have received intensive training and supervision in treating tobacco dependence. All participate in UKanQuit counseling on a part‐time basis, and spend the remainder of their effort as research assistants and counselors on smoking cessation research projects in the medical center. Hence, staffing consists of 1 full‐time equivalent counselor, 0.15 full‐time equivalent director (Richter), and 0.05 full‐time equivalent medical director (Ellerbeck). The program is funded through a contract with the hospital. We are in the process of hiring a nurse practitioner to create a more sustainable funding stream for the program because nurse practitioners can bill cessation services.

UKanQuit provides hospital counseling from 9 AM to 5 PM on weekdays. UKanQuit staff meets weekly for counseling supervision, strategic planning, continuing education, and troubleshooting difficult cases. In addition to treating smokers, the UKanQuit staff provides training and consultation to hospital personnel via grand rounds and other presentations. The service also provides a platform for medical students and residents to conduct focused research related to quality improvement. To facilitate systematic treatment of tobacco, UKanQuit developed the hospital treatment protocol for nursing staff, developed evidence‐based written self‐help materials that are accessible to hospital staff via the hospital printing system, and developed and instituted a tobacco treatment order set that was recently integrated into the EMR and automatically becomes prioritized as a recommended order set for all patients who report they have smoked in the past 30 days.

Procedures

UKanQuit staff retrieves patient details from the EMR and visits patients at their bedside. All hospital services refer to UKanQuit. UKanQuit provides counseling to Spanish speakers through bilingual/bicultural staff and hospital translators assist UKanQuit staff in counseling patients who speak other languages. The staff conducts a brief assessment at the bedside to inform treatment and contacts patients 6 months following inpatient treatment to assess outcomes and provide additional support and referral. This study evaluating the UKanQuit program was approved by the medical center's Institutional Review Board.

Program Intervention

UKanQuit staff visit patients at the bedside to deliver tobacco treatment. This consists of: (a) assessing withdrawal; (b) working with the health care team to adjust nicotine replacement to keep the patient comfortable; (c) assessing patients' interest in quitting smoking; (d) providing brief motivational intervention to patients not interested in quitting; and (e) providing assistance in quitting (developing a quit plan, arranging for medications on discharge) to patients interested in quitting (Figure 1). UKanQuit staff recommend medications based on the patients' level of dependence, history of cessation, and cessation medication preferences. The recommendation is communicated in person and by chart documentation to the medical team, usually by the nursing staff. The patients' resident or attending physician makes the final determination regarding medication provided. The hospital has nicotine replacement therapy (NRT; patch, gum, and lozenge), bupropion, and varenicline in its formulary. Patients are then offered an option of fax referral to the state tobacco quitline for follow‐up counseling. UKanQuit staff documents the services provided in the EMR via SOAR (Subjective, Objective, Assessment and Referral) notes.

Figure 1
Flow diagram showing the UkanQuit care process.

Measures

Baseline Measures

These were collected from the UKanQuit 1‐page program intake form, which UKanQuit designed to collect the minimal information necessary to conduct medication and behavioral counseling, to maximize counseling time, and to fit into the dense schedule of each patient's hospital stay. Demographic measures include age, gender and ethnicity. Smoking behavior measures include number of years smoked, number of cigarettes per day, a single item from the Fagerstrom Test for Nicotine Dependence that assesses time to first cigarette after waking,18, 19 interest in quitting smoking (on a 0‐10 scale, with 10 being very interested in quitting), and a single item from the self report version of the Minnesota Nicotine Withdrawal Scale (MNWS) that asks smokers to rate their desire or craving to smoke over a specified period. The single item craving measure from the MNWS has been found to have high reliability and good construct validity and is neither less sensitive to abstinence nor less reliable than the ten‐item brief questionnaire of smoking urges (QSU‐brief) used in laboratory and clinical trials.20 We asked about craving over the past 24 hours on a scale from 0 (none) to 4 (severe).21

Process Measures

Counselors also document the treatment they provided to smokers including the time spent with patients during counseling, provision of written self‐help materials, whether smokers set goals for quitting, hospital staff had already placed the smoker on a tobacco treatment medication, smokers are interested in increasing or changing their medication, the smoker wants smoking cessation medication on discharge, UKanQuit staff submitted a recommendation to hospital staff to make a medication change and/or provide a prescription for medication on discharge, plans for post‐discharge follow‐up (fax‐referral of patients to the state tobacco quitline or acceptance of UKanQuit counseling after discharge), and the patient agrees to be contacted at 6 months post‐discharge for follow‐up assessment and assistance.

Follow‐Up Measures

Outcome measures were collected by telephone 6 months post‐discharge by study staff who were not involved in the in‐hospital counseling. Call attempts to reach each patient ranged from 1 to 11. Measures included self‐reported 7‐day point prevalence abstinence rates, the number of quit attempts lasting over 24 hours, and cigarettes smoked per day among continuing smokers. Patients are asked if they participated in counseling through the tobacco quitline. Scaled (0‐10) items assess how important it is to the patients to quit smoking or remain quit, how confident they are in being able to quit or remain quit, and how satisfied they were with the assistance provided by UKanQuit. A yes/no item assesses whether patients think the program should be continued. In addition, UKanQuit asked two open‐ended questions to qualitatively assess satisfaction with the program and elicit suggestions for improvements. The questions were: What, if anything, was helpful to you about our services? and How can UKanQuit better help people stop smoking?

Analyses

Categorical variables were summarized by frequencies and percentages; continuous variables were summarized by means and standard deviations (SDs). We compared baseline characteristic differences between respondents and nonrespondents at 6 months follow‐up using chi‐square for categorical variables and t‐test for continuous variables. We also compared cigarettes per day at baseline and 6 months post‐discharge in smokers who were not able to quit using paired t‐test. All analyses were done with SPSS 17.0 statistical package. Open‐ended questions were analyzed using the framework synthesis method.22 Following examination and familiarization with the data, we developed an initial list of themes. We then categorized the responses by these themes using numerical codes. Each thematic code was summarized as a percentage of all responses. Those responses that fit into multiple thematic codes were multiply coded.

Results

Baseline

Within the study period (September 1, 2007 to August 31, 2008), 22,624 patients were admitted to the medical center (Figure 2). A total of 4150 were current smokers (ie, smoked within the past 30 days). UKanQuit staff met with 513 (68%) of 753 patients referred to the service. Some of the reasons why 32% of referred patients were not seen by the UKanQuit staff have been described in our previous paper.17 These include patient was asleep, doctor in the room, out of bed for procedure, and unable to speak. Table 1 displays the characteristics of 513 smokers treated by UKanQuit from September 1, 2007 to August 31, 2008. Patients were predominantly white (74%) with mean age of 50 years. Slightly more than half of smokers were male (57%). They had smoked an average of 18 cigarettes per day for a mean duration of 29 years, and over half (58%) smoked within 5 minutes of waking suggesting a high level of dependence. On a scale of 1 to 10 the mean interest in quitting was 7.9 (SD 2.9) and the mean craving score on a scale of 0 to 4 was 1.2 (SD 1.4) suggesting slight to mild craving.

Figure 2
Study flow diagram showing enrollment and follow‐up completion.
Demographic, Smoking Characteristics and Treatment Provided to 513 Patients Seen by UKanQuit Service From Sept 1, 2007 to Aug 31, 2008
CharacteristicsTreated (n = 513)
  • NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis.

  • Abbreviations: AA, Afro American; SD, standard deviation.

  • Interest in quitting range from 0 to 10.

  • craving range from 0 to 4.

Demographics 
Mean age (SD), years50.2 (13.6)
Male, n (%)291 (56.7)
Ethnicity, n (%) 
White371 (73.6)
AA107 (21.2)
Latino18 (3.6)
Other8 (1.6)
Referral source, n (%) 
Nursing profile477 (94.1)
Physician5 (1.0)
Other25 (4.9)
Smoking characteristics 
Mean number of years smoked (SD)28.9 (14.6)
Smokes within 5 minutes of waking n (%)270 (58.3)
Mean cigarettes smoked per day (SD)18.4 (12.6)
Mean interest in quitting (SD)*7.9 (2.9)
Mean craving (SD)1.2 (1.4)
Tobacco treatment provided 
Counseling 
Average time spent with patients (SD)19.9 (9.1)
Received information packet, n (%)490 (97.4)
Set goals for quitting, n (%)352 (73.3)
Had quit plan, n (%)151 (33.2)
Accepted fax referral to quitline, n (%)277 (55.8)
Opted for UKanQuit counseling, n (%)29 (5.9)
Medication 
On smoking cessation medication, n (%)133 (26.2)
Interested in receiving or changing smoking cessation medication, n (%)132 (26.7)
Added or changed smoking cessation medication, n (%)195 (40.5)
Discharge med, n (%)196 (40.2)

In‐Hospital Treatment

Hospital staff had placed 1 in 4 of the patients on smoking cessation medication prior to the UKanQuit staff visit. Nineteen percent were on NRT (16.2% transdermal patch, 2.5% on lozenge, 0.8% on nicotine gum); 5% on bupropion, 16.5% on varenicline, and 2.5% on clonidine. A total of 1.7% used a combination of Patch and bupropion while 2.5% used a combination of patch and gum. Staff provided 97% of the patients with written materials. Most patients (73%) set a goal for quitting or cutting down, and one‐third developed quit plans. Fifty‐six percent accepted fax referral to their state quitline, and 6% opted for follow‐up counseling with a UKanQuit counselor. Average time spent by UKanQuit with the patient was 20 minutes. Most of the patients treated (n = 426, 86%) agreed that UKanQuit staff can contact them for follow‐up assessment at 6 months.

Outcomes

Staff successfully contacted 196 (46%) of the 426 patients who agreed to 6‐month follow‐up. Responders were older (mean age 53 years, SD 12.6 vs. mean age 48 years, SD 13.8; P < 0.001); were more interested in quitting (mean interest in quitting 8.4, SD 2.5 vs. 7.6, SD 3.1 P = 0.001); and had a lower craving score at baseline (mean craving score 0.99, SD 1.3 vs. 1.29, SD 1.5; P < 0.001) compared to nonresponders. There were no differences between responders and nonresponders by gender, number of cigarettes smoked per day, years of smoking, referral source, inpatient smoking cessation medication used or time spent with UKanQuit hospital staff during the inpatient visit.

Table 2 displays smoking behavior and smoking cessation‐related characteristics of the respondents 6‐month post‐discharge. Over 70% attempted a quit attempt lasting at least 24 hours. The self reported 7‐day point prevalence abstinence rate was 31.8% among respondents. The intent‐to‐treat quit rate was 14.6% among all participants who agreed to follow‐up, counting those who we could not contact as smokers. While 34% used pharmacotherapy, only 5% of those who were fax‐referred to the quitline utilized the service. Most of the patients seen by the UKanQuit counselor considered quitting and staying quit important, mean 8.7, SD 2.3, and their confidence to quit or stay quit was above average, mean 6.6, SD 3.6. They rated the UKanQuit program very high, at 8.3, SD 2.8, on a scale of 0 to 10, and 98% of them wanted the program to continue. Of those who were not able to quit at 6 months, the mean number of cigarettes smoked per day decreased significantly from 17.8, SD 12.0 at baseline to 14.0, SD 9.7 at 6 months follow‐up (P < 0.001).

Follow‐Up Data Received From 196 Patients Seen by UKanQuit Between September 1, 2007 and August 31, 2008 (426 accepted 6 months follow‐up: response rate 46%)
Variables 
  • NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis.

  • Abbreviations: CPD, citrate phosphate dextrose; SD, standard deviation.

  • Faxed to quit line n = 121.

  • Importance/confidence, satisfaction with UKanQuit service range from 0 to 10.

Smoking characteristics 
7 day point prevalence abstinence rate, n (%)62 (31.8)
Among current smokers at 6 months follow‐up, n = 134 
Proportion of smokers who attempted to quit within 6 months, n (%)99 (73.9)
Mean CPD for smokers at 6 months (SD)14.0 (9.7)
Used formal quit smoking program 
Quit smoking medication, n (%)65 (34.4)
Quitline use among those faxed to quitline, n (%)*6 (5.0)
Importance/ Confidence 
How important is it to quit or stay quit, mean (SD)8.7 (2.3)
How confident are you to quit or stay quit, mean (SD)6.6 (3.6)
Views about service 
Satisfaction with UKanQuit service, mean (SD)8.3 (2.8)
Wants the UKanQuit program to continue, n (%)165 (97.6)

Satisfaction and Recommendations for Improvement

Most (96%) of participants contacted at follow‐up commented on what was helpful about the services. Table 3 displays the distribution of themes and illustrative comments. Themes included staff encouragement, support and counseling (41.8%); other (27%); information and education materials (20.9%); medication advice and referral (2.6%), and referral to quitline (0.5%)

What Was Helpful About UKanQuit Services
 n (%)
  • NOTE: n = represents number of comments coded. Some of the comments fall within multiple domains and were so coded. Other included don't remember visit, Don't know/Not sure/Can't think of anything.

Staff encouragement, support and counseling82 (41.8)
You guys did excellent. The friendliness of the people who visited me. 
Her outlook and her encouragement 
Other53 (27.1)
I don't remember the visit because I was heavily medicated, 
A lot was helpful but I couldn't tell you exactly what part was the most helpful 
Information/education material41 (20.9)
Provided me with a lot of info and the packet was helpful. 
The information packet 
Program not helpful14 (7.1)
I really didn't need their information, I was able to quit without it. 
Nothing helpful except the companionship 
Medication advice/referral5 (2.6)
They took the time and set up the patches for me 
She helped me with questions about medication, especially Chantix 
Referral to Quitline1 (0.5)
Talking on the phone 

Discussion

Among patients served by this inpatient program, interest in quitting was high but administration of inpatient medications upon admission was low (26%). Nearly all patients were provided with written materials, a majority set some form of goal for quitting or cutting down, and many developed quit plans and received assistance adjusting inpatient and/or discharge medications. After discharge, the majority of study participants made unassisted quit attempts, as utilization of medications and quitline services was suboptimal. Fax‐referral to quitline may not, on its own, fulfill guideline recommendations for post‐discharge follow‐up.

Our intent‐to‐treat quit rate was about half of what was found in Taylor et al.'s11 hospital program dissemination trial. Their program may have had better effects as it was somewhat more intensive. It included at least 1 follow‐up phone call immediately after discharge, as well as an accompanying video and relaxation audiotape or compact disc. However, differences in outcomes may also be due to large differences between the study populations. Hospitals participating in Taylor's study only conducted intervention and outcome assessment among smokers who were ready to quit, willing to enroll in a clinical trial, and willing to complete informed consent. Our intervention and outcome data included patients who agreed to speak with UKanQuit staff, regardless of readiness to quit. Our participants did not have to complete informed consent and enroll in a trial as our analyses were conducted post hoc. Our study outcomes might better reflect quit rates for a program serving all smokers, at all levels of readiness to quit, in actual hospital practices. The mean reduction in cigarette smoking among smokers who continued to smoke at 6 months' follow‐up was statistically significant. However, findings from a lung health study show that 50% or more reduction in smoking was ultimately related to successful quitting.23

Strengths and Limitations

The strengths of the study include the fact that the program attempts to intervene with all smokers, and provides stage‐appropriate intervention based on readiness to quit. It provides a snapshot of how a program is incorporated into clinical practice and describes implementation of protocol components.

This study has a number of limitations. We do not know exactly how many patients received the in‐hospital medication change agreed upon by the counselor and medical team immediately following the patient encounter. Our follow‐up rate was low and abstinence rates were based on self‐report, which limits our ability to draw conclusions about cessation outcomes. Process of care measures are based on counselor self‐report, without verification of services rendered. We are not able to identify the impact of our intervention above and beyond our patients' hospital experience, because we did not have a control group. We collected limited data from study participants so we are not able to better understand causes of nonadherence to quitline or poor pharmacotherapy utilization. Lastly, when the respondents were asked to comment about what was helpful about UKanQuit, 23% of the respondents said they could not remember the UKanQuit visit during their hospital stay. Many hospital medications induce brief amnesia, and patients have numerous consults during their stay and might not be able to separate one from the other. The 6‐month interval between their visit and follow‐up call may also account for their inability to remember the cessation consult.

Our patient population is in fact a subset of all smokers admitted (11% of smokers) because they are motivated enough to agree to talk with a counselor. Our intervention procedures, and results, might be quite different if all smokers were visited by the counselor. Efficacy trials of tobacco treatment in hospitals have focused on smokers who are ready to quit.24 Hence, procedures for working with unmotivated smokers in hospitals are less well established. Policymakers, hospitals, and hospital tobacco treatment programs should examine the most efficient (ie, effective and cost‐effective) approaches for addressing smoking in hospitals and specifically focus on whether all smokers should be treated by dedicated tobacco treatment staff, or only those who agree to a consult.

Lessons Learned

Linking Patients With In‐Hospital Cessation Medications Requires Collaboration With the Entire Health Care Team

Only 1 in 4 UKanQuit participants had been given smoking cessation medication to ameliorate withdrawal before counselors met with the patients. Although we have not systematically collected reasons patients do not receive cessation medication on admission, the 2 most common causes are that patients refuse it or physicians refuse it. Patients refuse medication perhaps because they do not want to quit, they feel they will cope without smoking during their hospital stay, or they are paying out of pocket and want to reduce costs. Physicians do not permit it because they believe it is contraindicated for the patient's health condition, it is contraindicated for the procedure the patient is receiving in the hospital, or they believe it will interfere with wound healing. There is also a considerable delay between ordering and receiving medications; patients who become uncomfortable during their stay sometimes change their minds, but end up being discharged before their medication arrives. Most of these issues pertain to nicotine replacement. Although patients not eligible for NRT may be good candidates for bupropion, varenicline, or even the second line cessation medications of clonidine or nortryptiline, these medications do not provide immediate relief from tobacco withdrawal symptoms and staff are reluctant to start patients on medications they may not receive on an outpatient basis. It is not clear what proportion of hospitalized patients should receive ameliorative medication. Not every hospitalized smoker is a candidate for NRT, due to contraindicated medical conditions, patients' level of dependence, and patients' willingness to accept cessation medication in order to prevent withdrawal. Koplan et al.25 achieved hospital‐wide increases in NRT orders from 1.6% to 2.5% after the introduction of an electronic tobacco treatment order set. These percentages seem low but actually were calculated from all hospital admissions, including smokers and nonsmokers. Moreover, their hospital population had a relatively low smoking rate of 12%. Our in‐hospital and post‐discharge (26.2% and 34.4% respectively) pharmacotherapy utilization rates were based only on smokers who had been seen by our service. Even though 1 in 4 smokers were already on medication when they were seen by counselors, 1 in 4 of patients seen wanted to either add a cessation medication or change their current dose. There is clearly room for improvement in how we offer and administer cessation medications on admission. Also, assessing medication efficacy and adjusting as needed appears to be an important role for in‐hospital counselors.

Facilitating Medications Post‐Discharge Will Require Creativity and Outpatient Follow‐Up

Post‐discharge, only 1 in 3 of our patients reported they used cessation medications. This may, again, be a function of patients' readiness to quit. However, it could also be related to knowledge/attitudes regarding the efficacy of medications or access to low‐cost medications. Some of our patients commented that making medication affordable would be helpful. Although our materials provide information on sources for free or low‐cost medications, this information may not have been salient during the hospital stay. To increase access to medications post‐discharge, programs should consider providing a booster mailer to the home with information on sources for free or reduced medications, providing take‐home starter pharmacotherapy kits lasting 1 to 2 weeks to bridge the gap between hospital discharge and finding another source of medications, and/or a follow‐up call shortly after discharge to verify use of pharmacotherapy and troubleshoot problems with medications or procurement.

Providing Follow‐Up Via Fax Referral to Quitlines Is Not as Simple as It Seems

Although our overall quitline fax‐referral rate was high (over half of all patients seen), rate of enrollment among those referred is much lower than the rates reported elsewhere, which range from 16% to 53%.2628 In our sample of fax‐referred smokers, we do not know how many were not enrolled due to failure to make contact vs. patient refusal once contact was made. One possible factor impacting enrollment rates is whether or not smokers are prescreened for readiness to quit. Nearly half of US quitlines require smokers to be ready to quit in order to receive a full course of treatment,29 but only 20% of smokers are ready to quit at any given time.30 In the cited studies with higher conversion rates, counselors prescreened patients for readiness to quit and only offered fax‐referral to those ready to quit in the next 30 days. Our program offers fax‐referral to all smokers. Our findings suggest that doing so results in high rates of referral but low rates of enrollment among those referred. Future studies should examine the impact of prescreening for readiness versus offering referral to all smokers on net enrollment and cessation.

Linking hospitalized smokers with tobacco quitlines has many potential benefits.31, 32 Proactive tobacco quitlines are effective15 and cost effective33 for smoking cessation; they are available, free, to all US smokers; services are delivered via telephone which minimizes many access barriers; hospitals do not have to bear the costs of the services; and many quitlines are undersubscribed and eager to increase their reach.34 Potential methods for increasing conversion to enrollment include building motivation to accept counseling and preparing patients for the quitline intake procedures. Our program is considering providing a warm handoff to patients by calling the quitline during the bedside consult to permit the quitline to enroll the patient during their hospital stay.

Hospital‐based cessation programs have the potential to deliver tobacco treatment to millions of hospitalized smokers annually. To deliver high‐quality, effective care, hospital cessation programs will have to solve problems inherent in hospital‐based carehow best to integrate into existing hospital systems, how to effectively communicate with other hospital care providers, and how to facilitate transitions in care to ensure patients receive evidence‐based post‐discharge care. We offer this report as the first of hopefully many that address quality improvement for specialized programs dedicated to treating tobacco in hospitals.

Acknowledgements

The authors gratefully acknowledge the contributions of the following UKanQuit Counselors in the planning and development of this manuscript: Brian Hernandez, Alex Perez‐Estrada, Grace Meikenhous, Meredith Benson, Terri Tapp. We also thank Chip Hulen, Albers Bart and Chris Wittkopp of the Organizational Improvement Department of KU Hospital; Marilyn Painter, Joanne McNair and Karisa Deculus of the KU Preventive Medicine and Public Health Department.

References
  1. Emmons KM,Goldstein MG.Smokers who are hospitalized: a window of opportunity for cessation interventions.Prev Med.1992;21:262269.
  2. McBride CM,Emmons KM,Lipkus IM.Understanding the potential of teachable moments: the case of smoking cessation.Health Educ Res.2003;18(2):156170.
  3. McBride CM,Ostroff JS.Teachable moments for promoting smoking cessation: the context of cancer care and survivorship.Cancer Control.2003;10(4):325333.
  4. Orleans CT,Kristeller JL,Gritz ER.Helping hospitalized smokers quit: new directions for treatment and research.J Consul Clin Psychol.1993;61(5):778789.
  5. Rigotti NA,Munafo MR,Stead LF.Smoking cessation interventions for hospitalized smokers: a systematic review.Arch Intern Med.2008;168(18):19501960.
  6. Rigotti NA,Munafo MR,Stead LF.Interventions for smoking cessation in hospitalised patients.Cochrane Database Syst Rev.2007(3):CD001837.
  7. CDC.Physician and other health‐care professional counseling of smokers to quit‐‐United States, 1991.MMWR Morb Mortal Wkly Rep.1993;42(44):854857.
  8. Hollis JF,Bills R,Whitlock E,Stevens VJ,Mullooly J,Lichtenstein E.Implementing tobacco interventions in the real world of managed care.Tob Control.2000;9Suppl 1:I18I24.
  9. Freund M,Campbell E,Paul C, et al.Smoking care provision in hospitals: a review of prevalence.Nicotine Tob Res.2008;10(5):757774.
  10. Dawood N,Vaccarino V,Reid KJ,Spertus JA,Hamid N,Parashar S.Predictors of smoking cessation after a myocardial infarction: the role of institutional smoking cessation programs in improving success.Arch Intern Med.2008;168(18):19611967.
  11. Taylor CB,Miller NH,Cameron RP,Fagans EW,Das S.Dissemination of an effective inpatient tobacco use cessation program.Nicotine Tob Res.2005;7(1):129137.
  12. Smith PM,Reilly KR,Houston Miller N,DeBusk RF,Taylor CB.Application of a nurse‐managed inpatient smoking cessation program.Nicotine Tob Res.2002;4(2):211222.
  13. Solberg LI,Kottke TE,Conn SA,Brekke ML,Calomeni CA,Conboy KS.Delivering clinical preventive services is a systems problem.Ann Behav Med.1997;19(3):271278.
  14. Solberg LI,Brekke ML,Fazio CJ, et al.Lessons from experienced guideline implementers: attend to many factors and use multiple strategies.Jt Comm J Qual Improv.2000;26(4):171188.
  15. U.S. Department of Health and Human Services (USDHHS).Treating Tobacco Use and Dependence: Clinical Practice Guideline 2008 Update.
  16. Faseru B,Yeh H,Ellerbeck EF,Befort CA,Richter KP.Referral and treatment for nicotine dependence among hospitalized patients.Subst Abus.2009;30(1):9495.
  17. Faseru B,Yeh HW,Ellerbeck EF,Befort C,Richter KP.Prevalence and predictors of tobacco treatment in an academic medical center.Jt Comm J Qual Patient Saf.2009;35(11):551557.
  18. Heatherton TF,Kozlowski LT,Frecker RC,Fagerstrom KO.The Fagerstrom Test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire.Br J Addict.1991;86(9):11191127.
  19. Center TTUR,Workgroup TDP,Baker TB, et al.Time to first cigarette in the morning as an index of ability to quit smoking: Implications for nicotine dependence.Nicotine Tob Res.2007;9 Supp 4:555570.
  20. West R,Ussher M.Is the ten‐item Questionnaire of Smoking Urges (QSU‐brief) more sensitive to abstinence than shorter craving measures?Psychopharmacology (Berl).2010;208(3):427432.
  21. Hughes JR,Hatsukami D.Signs and symptoms of tobacco withdrawal.Arch Gen Psychiatry.1986;43(3):289294.
  22. Pope C,Ziebland S,Mays N.Qualitative research in health care. Analysing qualitative data.BMJ.2000;320(7227):114116.
  23. Hughes J,Lindgren P,Connett J,Nides M.Smoking reduction in the Lung Health Study.Nicotine Tob Res.2004;6(2):275280.
  24. Rigotti N,Munafo M,Stead L.Interventions for smoking cessation in hospitalised patients.Cochrane Database Syst Rev.2007(3):CD001837.
  25. Koplan KE,Regan S,Goldszer RC,Schneider LI,Rigotti NA.A computerized aid to support smoking cessation treatment for hospital patients.J Gen Intern Med.2008;23(8):12141217.
  26. Bentz CJ,Bayley KB,Bonin KE,Fleming L,Hollis JF,McAfee T.The feasibility of connecting physician offices to a state‐level tobacco quit line.Am J Prev Med.2006;30(1):3137.
  27. Willett JG,Hood NE,Burns EK, et al.Clinical faxed referrals to a tobacco quitline: reach, enrollment, and participant characteristics.Am J Prev Med.2009;36(4):337340.
  28. Cupertino AP,Richter K,Cox LS, et al.Feasibility of a Spanish/English computerized decision aid to facilitate smoking cessation efforts in underserved communities.J Health Care Poor Underserved.2010;21(2):504517.
  29. North American Quitline Consortium. Available at: http://www.naquitline. org. Accessed July2010.
  30. Velicer WF,Fava JL,Prochaska JO,Abrams DB,Emmons KM,Pierce JP.Distribution of smokers by stage in three representative samples.Prev Med.1995;24:401411.
  31. Stead LF,Perera R,Lancaster T.Telephone counselling for smoking cessation.Cochrane Database Syst Rev.2006;3:CD002850.
  32. Borland R,Segan CJ.The potential of quitlines to increase smoking cessation.Drug Alcohol Rev.2006;25(1):7378.
  33. Zhu SH,Anderson CM,Tedeschi GJ, et al.Evidence of real‐world effectiveness of a telephone quitline for smokers.N Engl J Med.2002;347(14):10871093.
  34. Campaign for Tobacco Free Kids Fact Sheets 2008. Quitlines Help Smokers Quit. Available at: http://www.tobaccofreekids.org/research/factsheets/pdf/0326.pdf. Accessed July2010.
References
  1. Emmons KM,Goldstein MG.Smokers who are hospitalized: a window of opportunity for cessation interventions.Prev Med.1992;21:262269.
  2. McBride CM,Emmons KM,Lipkus IM.Understanding the potential of teachable moments: the case of smoking cessation.Health Educ Res.2003;18(2):156170.
  3. McBride CM,Ostroff JS.Teachable moments for promoting smoking cessation: the context of cancer care and survivorship.Cancer Control.2003;10(4):325333.
  4. Orleans CT,Kristeller JL,Gritz ER.Helping hospitalized smokers quit: new directions for treatment and research.J Consul Clin Psychol.1993;61(5):778789.
  5. Rigotti NA,Munafo MR,Stead LF.Smoking cessation interventions for hospitalized smokers: a systematic review.Arch Intern Med.2008;168(18):19501960.
  6. Rigotti NA,Munafo MR,Stead LF.Interventions for smoking cessation in hospitalised patients.Cochrane Database Syst Rev.2007(3):CD001837.
  7. CDC.Physician and other health‐care professional counseling of smokers to quit‐‐United States, 1991.MMWR Morb Mortal Wkly Rep.1993;42(44):854857.
  8. Hollis JF,Bills R,Whitlock E,Stevens VJ,Mullooly J,Lichtenstein E.Implementing tobacco interventions in the real world of managed care.Tob Control.2000;9Suppl 1:I18I24.
  9. Freund M,Campbell E,Paul C, et al.Smoking care provision in hospitals: a review of prevalence.Nicotine Tob Res.2008;10(5):757774.
  10. Dawood N,Vaccarino V,Reid KJ,Spertus JA,Hamid N,Parashar S.Predictors of smoking cessation after a myocardial infarction: the role of institutional smoking cessation programs in improving success.Arch Intern Med.2008;168(18):19611967.
  11. Taylor CB,Miller NH,Cameron RP,Fagans EW,Das S.Dissemination of an effective inpatient tobacco use cessation program.Nicotine Tob Res.2005;7(1):129137.
  12. Smith PM,Reilly KR,Houston Miller N,DeBusk RF,Taylor CB.Application of a nurse‐managed inpatient smoking cessation program.Nicotine Tob Res.2002;4(2):211222.
  13. Solberg LI,Kottke TE,Conn SA,Brekke ML,Calomeni CA,Conboy KS.Delivering clinical preventive services is a systems problem.Ann Behav Med.1997;19(3):271278.
  14. Solberg LI,Brekke ML,Fazio CJ, et al.Lessons from experienced guideline implementers: attend to many factors and use multiple strategies.Jt Comm J Qual Improv.2000;26(4):171188.
  15. U.S. Department of Health and Human Services (USDHHS).Treating Tobacco Use and Dependence: Clinical Practice Guideline 2008 Update.
  16. Faseru B,Yeh H,Ellerbeck EF,Befort CA,Richter KP.Referral and treatment for nicotine dependence among hospitalized patients.Subst Abus.2009;30(1):9495.
  17. Faseru B,Yeh HW,Ellerbeck EF,Befort C,Richter KP.Prevalence and predictors of tobacco treatment in an academic medical center.Jt Comm J Qual Patient Saf.2009;35(11):551557.
  18. Heatherton TF,Kozlowski LT,Frecker RC,Fagerstrom KO.The Fagerstrom Test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire.Br J Addict.1991;86(9):11191127.
  19. Center TTUR,Workgroup TDP,Baker TB, et al.Time to first cigarette in the morning as an index of ability to quit smoking: Implications for nicotine dependence.Nicotine Tob Res.2007;9 Supp 4:555570.
  20. West R,Ussher M.Is the ten‐item Questionnaire of Smoking Urges (QSU‐brief) more sensitive to abstinence than shorter craving measures?Psychopharmacology (Berl).2010;208(3):427432.
  21. Hughes JR,Hatsukami D.Signs and symptoms of tobacco withdrawal.Arch Gen Psychiatry.1986;43(3):289294.
  22. Pope C,Ziebland S,Mays N.Qualitative research in health care. Analysing qualitative data.BMJ.2000;320(7227):114116.
  23. Hughes J,Lindgren P,Connett J,Nides M.Smoking reduction in the Lung Health Study.Nicotine Tob Res.2004;6(2):275280.
  24. Rigotti N,Munafo M,Stead L.Interventions for smoking cessation in hospitalised patients.Cochrane Database Syst Rev.2007(3):CD001837.
  25. Koplan KE,Regan S,Goldszer RC,Schneider LI,Rigotti NA.A computerized aid to support smoking cessation treatment for hospital patients.J Gen Intern Med.2008;23(8):12141217.
  26. Bentz CJ,Bayley KB,Bonin KE,Fleming L,Hollis JF,McAfee T.The feasibility of connecting physician offices to a state‐level tobacco quit line.Am J Prev Med.2006;30(1):3137.
  27. Willett JG,Hood NE,Burns EK, et al.Clinical faxed referrals to a tobacco quitline: reach, enrollment, and participant characteristics.Am J Prev Med.2009;36(4):337340.
  28. Cupertino AP,Richter K,Cox LS, et al.Feasibility of a Spanish/English computerized decision aid to facilitate smoking cessation efforts in underserved communities.J Health Care Poor Underserved.2010;21(2):504517.
  29. North American Quitline Consortium. Available at: http://www.naquitline. org. Accessed July2010.
  30. Velicer WF,Fava JL,Prochaska JO,Abrams DB,Emmons KM,Pierce JP.Distribution of smokers by stage in three representative samples.Prev Med.1995;24:401411.
  31. Stead LF,Perera R,Lancaster T.Telephone counselling for smoking cessation.Cochrane Database Syst Rev.2006;3:CD002850.
  32. Borland R,Segan CJ.The potential of quitlines to increase smoking cessation.Drug Alcohol Rev.2006;25(1):7378.
  33. Zhu SH,Anderson CM,Tedeschi GJ, et al.Evidence of real‐world effectiveness of a telephone quitline for smokers.N Engl J Med.2002;347(14):10871093.
  34. Campaign for Tobacco Free Kids Fact Sheets 2008. Quitlines Help Smokers Quit. Available at: http://www.tobaccofreekids.org/research/factsheets/pdf/0326.pdf. Accessed July2010.
Issue
Journal of Hospital Medicine - 6(4)
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Journal of Hospital Medicine - 6(4)
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211-218
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Evaluation of a hospital‐based tobacco treatment service: Outcomes and lessons learned
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Evaluation of a hospital‐based tobacco treatment service: Outcomes and lessons learned
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outcomes measurement, quality improvement, smoking cessation
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outcomes measurement, quality improvement, smoking cessation
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Copyright © 2010 Society of Hospital Medicine

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Emergency Response at a Children's Hospital

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Retrospective review of emergency response activations during a 13‐year period at a tertiary care children's hospital

The incidence of sudden pediatric cardiac or respiratory arrest is low.1 Most inpatient pediatric arrests appear to occur as progression of respiratory distress or shock.2 The outcome of inpatient pediatric cardiorespiratory arrests continues to be poor, emphasizing the need for early recognition and intervention. In 2006, as part of its 100,000 Lives Campaign the Institute for Healthcare Improvement recommended the implementation of Rapid Response Teams (RRT) as 1 of the strategies to reduce the number of preventable inpatient deaths.3 We reviewed all emergency response team (ERT) activations for last 13 years at The Children's Hospital in Denver, CO to assist in the development of a new RRT and to identify at risk populations, situations, and system processes the RRT should address.

This is a retrospective review of 13 years of data collection on ERT activations at The Children's Hospital in Denver, CO. We describe demographic and clinical variables, including outcomes of ERT activations at a free‐standing tertiary care children's hospital.

Background/Methods

The Children's Hospital (TCH) is a 270 inpatient bed tertiary care free standing children's hospital associated with University of Colorado at Denver Health Sciences Center. The current distribution of inpatient beds includes: 168 medical/surgical beds, 102 critical care beds (26 pediatric intensive care unit, 16 cardiac intensive care unit, and 60 neonatal intensive care unit). In 2006, nearly 10,000 patients were admitted for care at this hospital with an average inpatient stay of 6 days. TCH is a Level One Regional Trauma Center serving a catchment area of seven states, and a major transplant center for heart, solid organs, and bone marrow.

The history of the TCH Emergency Response Team dates back to 1990 when we first began to follow cardiorespiratory arrests in noncritical care areas of the hospital. In 1992, a Cardiac Or Respiratory event (COR) team including the most senior in‐house specialist was available 24 hours a day, 7 days a week to respond to all arrests within the hospital. The COR committee provided oversight and monitoring of the arrest events, including standardization of crash carts and the development of mock codes to ensure that the responding personnel were qualified in resuscitation practices. The COR team has evolved over the ensuing years, including a name change to the Emergency Response Team (ERT); now a single number activated by any medical staff is used to call the operator who activates the ERT via overhead paging and via code pager system. The 14 member Emergency Response Team consists of PICU/CICU/anesthesia/surgical fellows, ED attending, in‐house residents, PICU/CICU/ED charge nurse, nursing supervisor, resource RN, pharmacist, respiratory therapist, and a messenger.

A database has been maintained by 1 of the authors (DBH) since the inception of the COR/ERT at TCH 16 years ago. This is a retrospective review of this database of ERT activations. An ERT activation could have been triggered by any event that was felt to be emergent, life threatening, and/or needing immediate medical attention. After the event, a debriefing form was filled out about the event. Data collected included date, time, medical record number, location, primary care service, age, sex, primary and secondary diagnoses, and disposition. Data not captured in this database included initial rhythm, need for compressions, cardiac medications, defibrillation or intubation.

Analyses were performed on data collected from January 1993 through April 2007. Medical records of the documented ERT activations were reviewed for missing information and/or clarification of the events. Categories entered in Statistical Package for Social Statistics (SPSS) were similar to information included in the debriefing form: age, sex, admission diagnosis, precipitating event, percentage of admissions, acute vs. chronic diagnosis, winter vs. nonwinter months (October‐March/April‐September), day (6 am‐6 pm) and night (6 pm‐6 am) shifts, survival of ERT activation, survival to discharge, and primary attending service. Data were analyzed using SPSS 16.0 (2007, Chicago, IL). The study was approved by the Colorado Multiple Institutional Review Board.

Results/Conclusion

There were 1537 ERT activations in the database. A total of 203 ERT activations were eliminated from the database: 177 were eliminated from analysis because of missing age, admission diagnosis or time of day of activation, and 26 were ERT activations that had been triggered on adult visitors or adult employees. The remaining 1334 ERT activations were included for analysis.

Table 1 shows the demographics of the patients. The median age was 1.8 years, with a range of 0 to 29 years. A total of 39%(511) of all ERT activations occurred in patients under the age of 1 year with the highest incidence between 1 month and 1 year. Overall, the children at highest risk were males less than 1 year of age with a chronic diagnosis. In addition, time of day and time of year of ERT activations were analyzed as shown. There was no statistical difference between nonwinter (April‐September) and winter (October‐March) months. Statistically, there were significantly more ERT activations during day shifts (6 am‐6 pm) as compared to night shifts (P < 0.001).

Demographic and Clinical Characteristics of ERT Activation Patients
Variable (n = 1334)n (%)
  • Abbreviation: ERT, emergency response team.

  • P < 0.001.

Age
Neonate127 (10)
<1 year384 (29)
1‐3 years324 (24)
4‐6 years137 (10)
7‐10 years120 (9)
11‐13 years84 (6)
14‐17 years120 (9)
>17 years38 (3)
Gender
Male807 (60)
Time of day*
Day (06:00‐18:00)784 (59)
Night (18:00‐06:00)550 (41)
Time of Year
April‐September669 (50)
October‐March665 (50)

The most common admission diagnosis (Table 2) and underlying chronic condition was cardiac disease; other common admission diagnoses were infectious disease, trauma, and pulmonary disease. The medical categories of admission diagnosis included congenital/metabolic (39%), gastrointestinal (29%), renal (18%), rheumatology (4%), toxicology (4%), psychiatry (3%), endocrine (2%), and allergy (1%). The surgery category of admission diagnosis included otolaryngology (63%), orthopedics (28%), urology (4%), dental (4%), and ophthalmology (1%).

Admission Diagnosis Categories of ERT Activation Patients
Admission Diagnosis (n = 1334)n (%)
  • Abbreviation: ERT, emergency response team.

Cardiac370 (28)
Infectious disease197 (15)
Trauma192 (14)
Other medical137 (10)
Pulmonary109 (8)
Neurology79 (6)
Other surgery76 (6)
Neurosurgery68 (5)
Hematology/oncology53 (4)
Surgery53 (4)

Finally, the patients' survival rate after an ERT itself was to be 90% (Table 3), with an overall survival rate to discharge of 78% (Table 4). Survival rate to discharge of those patients who survived the ERT event was 87%. Two patients were missing survival event data and 137 patients were missing survival to discharge data.

Survival of Event of ERT Activation Patients
Admission DiagnosisSurvival of Event n (%)
  • Abbreviation: ERT, emergency response team.

  • Two patients missing survival event data.

Cardiac (n = 370)315 (85)
Infectious disease (n = 197)186 (94)
Trauma (n = 191)168 (88)
Other medical (n = 136)125 (92)
Pulmonary (n = 109)97 (89)
Neurology (n = 79)75 (95)
Other surgery (n = 76)75 (99)
Neurosurgery (n = 68)63 (93)
Surgery (n = 53)50 (94)
Hematology/oncology (n = 53)46 (87)
Total (n = 1332)*1200 (90)

Discussion

We present a retrospective review of 1334 emergency response team activations over 13 years at an academic free‐standing tertiary care children's hospital. In keeping with previous reports, we found that children less than 1 year of age were at the highest risk for activation of the emergency response team.1, 4, 5

The National Registry for CardioPulmonary Resuscitation (NRCPR) database cite respiratory failure (asphyxia) and circulatory shock (ischemia) as the most common causes of in‐hospital cardiac arrests.1 Additionally, more than half of pediatric patients that experience a cardiopulmonary arrest have an underlying chronic illness.1, 2, 4, 5, 6 These are similar to our findings that chronic pulmonary and cardiac diseases were among the most frequent admission diagnosis.

Unlike Peberdy et al.,7we did not find an increase in the number of emergency response team activations at night or on weekends. Instead, we found that an ERT activation was more likely to be requested during the day shifts (6 am‐6 pm) which is a similar to that reported by Jones et al.8 Similarly, Jones et al.8 reported that the hourly rate of their medical emergency team activation was greater during the time between 8 am and 6 pm.

Our overall survival rate (78%) to discharge after an ERT event was much higher than what has been reported by Topjian et al.9 (25%). This likely reflects our inclusion of all emergency response team activations, not just apneic and asystolic arrests. The improved survival rate may also be influenced by the 24/7 presence of pediatric fellows, residents and surgeons in our hospital, which has been associated with improved 24‐hour survival for children receiving in‐hospital cardiopulmonary resuscitation.10

There are several limitations to our data collection and this report. This was a retrospective review and as previously noted some medical details were absent resulting in the exclusion of some cases. We also found that the original debriefing form which was used as the basis for the database did not include some important clinical variable, such as vital signs, more detailed events such as rhythm, medications used, and deficits or changes in baseline function. We suggest including multiple variables in a future multicenter study of pediatric RRT's: facility, admitting service, admission diagnosis, age (chronologic and gestational), sex, how long patient has been in hospital prior to the event, past medical history, vitals signs before and after activation, time/date, location, any precipitating events and actions taken by the RRT (eg, cardiopulmonary resuscitation, defibrillation, emergent intubation, and other emergent interventions), medications, survival of event, survival to discharge, deficits or changes in baseline function after the event and to discharge. In particular, information about history of prematurity would have been helpful in assessing further risk factors. Analysis of survival with or without significant deficits or changes in baseline function would be another useful outcome measure. Figure 1 is an example of a debriefing form a multi‐institutional study or hospitalist led quality improvement project may use to collect this data.

Figure 1
Code Blue Activation Follow‐Up and Debriefing Form.

We were able to identify a population of higher‐risk patients (less than 1 year of age with comorbidities and an admission diagnosis of cardiac or respiratory disease) to focus our educational efforts on earlier recognition of patient deterioration for both inpatient ward staff and RRT responders. These findings may assist in future quality assurance issues such as patient placement and early ICU admissions depending on age, chronic conditions, and/or admission diagnosis. Future directions should include multi‐center study of RRT to improve external validity. In addition, a more careful analysis of events surrounding the activation, including incorporating such tools as the Pediatric Early Warning (PEW) Score,11 may further assist hospitals and practitioners identify hospitalized children at risk for deterioration on the inpatient ward.

Survival to Discharge of ERT Activation Patients
Admission DiagnosisSurvival to Discharge n (%)
  • Abbreviation: ERT, emergency response team.

  • Total of 137 patients missing survival discharge data.

Cardiac (n = 317)217 (69)
Infectious disease (n = 175)139 (79)
Trauma (n = 182)151 (83)
Other medical (n = 125)99 (79)
Pulmonary (n = 98)76 (78)
Neurology (n = 71)63 (89)
Other surgery (n = 74)70 (95)
Neurosurgery (n = 64)53 (83)
Surgery (n = 47)39 (83)
Hematology/oncology (n = 44)24 (55)
Total (n = 1197)*931 (78)

Acknowledgements

The acknowledge Dr. Genie Roosevelt MD, MPH, and Sara Deakyne MPH for their assistance in data analysis.

References
  1. Berg MD,Nadkarni VM,Zuercher M,Berg RA.In‐hospital pediatric arrest.Pediatr Clin North Am.2008;55(3):589604.
  2. Nadkarni VM,Larkin GL,Peberdy MA, et al.First documented rhythm and clinical outcome from in‐hospital cardiac arrest among children and adults.JAMA.2006;295:5057.
  3. 57100K Lives Campaign‐getting started Kit: Rapid response Teams. Available at: http://www.ihi.org/IHI/Programs/Campaign/Campaign.htm?TabId=1 Accessed July2010.
  4. Meaney PA,Nadkarni VM,Cook EF, et al.Higher survival rates among younger patients after pediatric intensive care unity cardiac arrests.Pediatrics2006;118:24242433.
  5. Reis A.G,Nadkarni V.M. et al.A prospective investigation into the epidemiology of in‐hospital pediatric cardiopulmonary resuscitation using the internationalUtseinReporting Style.Pediatrics.2002;109:200209.
  6. Gillis J,Dickson D,Rieder M,Steward D,Edmonds J.Results of inpatient pediatric resuscitation.Crit Care Med.1986;14:469471
  7. Peberdy MA,Ornato JP,Larkin GL, et al.Survival from in‐hospital cardiac arrest during nights and weekends.JAMA.2008;299:785792.
  8. Jones D,Bates S,Warrillow S, et al.Circadian pattern of activation of the medical emergency team in a teaching hospital.Crit Care.2005;9:R303R306.
  9. Topjian AA,Berg RA,Nadkarni VM.Pediatric cardiopulmonary resuscitation: advances in science, techniques, and outcomes.Pediatrics.2008;122;10861098
  10. Donoghue AJ,Nadkarni VM,Elliott M, et al.Effect of hospital characteristics on outcomes from pediatric cardiopulmonary resuscitation: a report from the National Registry of Cardiopulmonary Resuscitation.Pediatrics.2006;118:9951001.
  11. Duncan H,Hutchinson J,Parshuram CS.The Pediatric Early Warning System score: a severity of illness score to predict urgent medical need in hospitalized children.J Crit Care.2006;21(3):271278.
Article PDF
Issue
Journal of Hospital Medicine - 6(3)
Page Number
131-135
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cardiac or respiratory event, emergency response team, pediatrics, rapid response team, the Children's Hospital
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Article PDF

The incidence of sudden pediatric cardiac or respiratory arrest is low.1 Most inpatient pediatric arrests appear to occur as progression of respiratory distress or shock.2 The outcome of inpatient pediatric cardiorespiratory arrests continues to be poor, emphasizing the need for early recognition and intervention. In 2006, as part of its 100,000 Lives Campaign the Institute for Healthcare Improvement recommended the implementation of Rapid Response Teams (RRT) as 1 of the strategies to reduce the number of preventable inpatient deaths.3 We reviewed all emergency response team (ERT) activations for last 13 years at The Children's Hospital in Denver, CO to assist in the development of a new RRT and to identify at risk populations, situations, and system processes the RRT should address.

This is a retrospective review of 13 years of data collection on ERT activations at The Children's Hospital in Denver, CO. We describe demographic and clinical variables, including outcomes of ERT activations at a free‐standing tertiary care children's hospital.

Background/Methods

The Children's Hospital (TCH) is a 270 inpatient bed tertiary care free standing children's hospital associated with University of Colorado at Denver Health Sciences Center. The current distribution of inpatient beds includes: 168 medical/surgical beds, 102 critical care beds (26 pediatric intensive care unit, 16 cardiac intensive care unit, and 60 neonatal intensive care unit). In 2006, nearly 10,000 patients were admitted for care at this hospital with an average inpatient stay of 6 days. TCH is a Level One Regional Trauma Center serving a catchment area of seven states, and a major transplant center for heart, solid organs, and bone marrow.

The history of the TCH Emergency Response Team dates back to 1990 when we first began to follow cardiorespiratory arrests in noncritical care areas of the hospital. In 1992, a Cardiac Or Respiratory event (COR) team including the most senior in‐house specialist was available 24 hours a day, 7 days a week to respond to all arrests within the hospital. The COR committee provided oversight and monitoring of the arrest events, including standardization of crash carts and the development of mock codes to ensure that the responding personnel were qualified in resuscitation practices. The COR team has evolved over the ensuing years, including a name change to the Emergency Response Team (ERT); now a single number activated by any medical staff is used to call the operator who activates the ERT via overhead paging and via code pager system. The 14 member Emergency Response Team consists of PICU/CICU/anesthesia/surgical fellows, ED attending, in‐house residents, PICU/CICU/ED charge nurse, nursing supervisor, resource RN, pharmacist, respiratory therapist, and a messenger.

A database has been maintained by 1 of the authors (DBH) since the inception of the COR/ERT at TCH 16 years ago. This is a retrospective review of this database of ERT activations. An ERT activation could have been triggered by any event that was felt to be emergent, life threatening, and/or needing immediate medical attention. After the event, a debriefing form was filled out about the event. Data collected included date, time, medical record number, location, primary care service, age, sex, primary and secondary diagnoses, and disposition. Data not captured in this database included initial rhythm, need for compressions, cardiac medications, defibrillation or intubation.

Analyses were performed on data collected from January 1993 through April 2007. Medical records of the documented ERT activations were reviewed for missing information and/or clarification of the events. Categories entered in Statistical Package for Social Statistics (SPSS) were similar to information included in the debriefing form: age, sex, admission diagnosis, precipitating event, percentage of admissions, acute vs. chronic diagnosis, winter vs. nonwinter months (October‐March/April‐September), day (6 am‐6 pm) and night (6 pm‐6 am) shifts, survival of ERT activation, survival to discharge, and primary attending service. Data were analyzed using SPSS 16.0 (2007, Chicago, IL). The study was approved by the Colorado Multiple Institutional Review Board.

Results/Conclusion

There were 1537 ERT activations in the database. A total of 203 ERT activations were eliminated from the database: 177 were eliminated from analysis because of missing age, admission diagnosis or time of day of activation, and 26 were ERT activations that had been triggered on adult visitors or adult employees. The remaining 1334 ERT activations were included for analysis.

Table 1 shows the demographics of the patients. The median age was 1.8 years, with a range of 0 to 29 years. A total of 39%(511) of all ERT activations occurred in patients under the age of 1 year with the highest incidence between 1 month and 1 year. Overall, the children at highest risk were males less than 1 year of age with a chronic diagnosis. In addition, time of day and time of year of ERT activations were analyzed as shown. There was no statistical difference between nonwinter (April‐September) and winter (October‐March) months. Statistically, there were significantly more ERT activations during day shifts (6 am‐6 pm) as compared to night shifts (P < 0.001).

Demographic and Clinical Characteristics of ERT Activation Patients
Variable (n = 1334)n (%)
  • Abbreviation: ERT, emergency response team.

  • P < 0.001.

Age
Neonate127 (10)
<1 year384 (29)
1‐3 years324 (24)
4‐6 years137 (10)
7‐10 years120 (9)
11‐13 years84 (6)
14‐17 years120 (9)
>17 years38 (3)
Gender
Male807 (60)
Time of day*
Day (06:00‐18:00)784 (59)
Night (18:00‐06:00)550 (41)
Time of Year
April‐September669 (50)
October‐March665 (50)

The most common admission diagnosis (Table 2) and underlying chronic condition was cardiac disease; other common admission diagnoses were infectious disease, trauma, and pulmonary disease. The medical categories of admission diagnosis included congenital/metabolic (39%), gastrointestinal (29%), renal (18%), rheumatology (4%), toxicology (4%), psychiatry (3%), endocrine (2%), and allergy (1%). The surgery category of admission diagnosis included otolaryngology (63%), orthopedics (28%), urology (4%), dental (4%), and ophthalmology (1%).

Admission Diagnosis Categories of ERT Activation Patients
Admission Diagnosis (n = 1334)n (%)
  • Abbreviation: ERT, emergency response team.

Cardiac370 (28)
Infectious disease197 (15)
Trauma192 (14)
Other medical137 (10)
Pulmonary109 (8)
Neurology79 (6)
Other surgery76 (6)
Neurosurgery68 (5)
Hematology/oncology53 (4)
Surgery53 (4)

Finally, the patients' survival rate after an ERT itself was to be 90% (Table 3), with an overall survival rate to discharge of 78% (Table 4). Survival rate to discharge of those patients who survived the ERT event was 87%. Two patients were missing survival event data and 137 patients were missing survival to discharge data.

Survival of Event of ERT Activation Patients
Admission DiagnosisSurvival of Event n (%)
  • Abbreviation: ERT, emergency response team.

  • Two patients missing survival event data.

Cardiac (n = 370)315 (85)
Infectious disease (n = 197)186 (94)
Trauma (n = 191)168 (88)
Other medical (n = 136)125 (92)
Pulmonary (n = 109)97 (89)
Neurology (n = 79)75 (95)
Other surgery (n = 76)75 (99)
Neurosurgery (n = 68)63 (93)
Surgery (n = 53)50 (94)
Hematology/oncology (n = 53)46 (87)
Total (n = 1332)*1200 (90)

Discussion

We present a retrospective review of 1334 emergency response team activations over 13 years at an academic free‐standing tertiary care children's hospital. In keeping with previous reports, we found that children less than 1 year of age were at the highest risk for activation of the emergency response team.1, 4, 5

The National Registry for CardioPulmonary Resuscitation (NRCPR) database cite respiratory failure (asphyxia) and circulatory shock (ischemia) as the most common causes of in‐hospital cardiac arrests.1 Additionally, more than half of pediatric patients that experience a cardiopulmonary arrest have an underlying chronic illness.1, 2, 4, 5, 6 These are similar to our findings that chronic pulmonary and cardiac diseases were among the most frequent admission diagnosis.

Unlike Peberdy et al.,7we did not find an increase in the number of emergency response team activations at night or on weekends. Instead, we found that an ERT activation was more likely to be requested during the day shifts (6 am‐6 pm) which is a similar to that reported by Jones et al.8 Similarly, Jones et al.8 reported that the hourly rate of their medical emergency team activation was greater during the time between 8 am and 6 pm.

Our overall survival rate (78%) to discharge after an ERT event was much higher than what has been reported by Topjian et al.9 (25%). This likely reflects our inclusion of all emergency response team activations, not just apneic and asystolic arrests. The improved survival rate may also be influenced by the 24/7 presence of pediatric fellows, residents and surgeons in our hospital, which has been associated with improved 24‐hour survival for children receiving in‐hospital cardiopulmonary resuscitation.10

There are several limitations to our data collection and this report. This was a retrospective review and as previously noted some medical details were absent resulting in the exclusion of some cases. We also found that the original debriefing form which was used as the basis for the database did not include some important clinical variable, such as vital signs, more detailed events such as rhythm, medications used, and deficits or changes in baseline function. We suggest including multiple variables in a future multicenter study of pediatric RRT's: facility, admitting service, admission diagnosis, age (chronologic and gestational), sex, how long patient has been in hospital prior to the event, past medical history, vitals signs before and after activation, time/date, location, any precipitating events and actions taken by the RRT (eg, cardiopulmonary resuscitation, defibrillation, emergent intubation, and other emergent interventions), medications, survival of event, survival to discharge, deficits or changes in baseline function after the event and to discharge. In particular, information about history of prematurity would have been helpful in assessing further risk factors. Analysis of survival with or without significant deficits or changes in baseline function would be another useful outcome measure. Figure 1 is an example of a debriefing form a multi‐institutional study or hospitalist led quality improvement project may use to collect this data.

Figure 1
Code Blue Activation Follow‐Up and Debriefing Form.

We were able to identify a population of higher‐risk patients (less than 1 year of age with comorbidities and an admission diagnosis of cardiac or respiratory disease) to focus our educational efforts on earlier recognition of patient deterioration for both inpatient ward staff and RRT responders. These findings may assist in future quality assurance issues such as patient placement and early ICU admissions depending on age, chronic conditions, and/or admission diagnosis. Future directions should include multi‐center study of RRT to improve external validity. In addition, a more careful analysis of events surrounding the activation, including incorporating such tools as the Pediatric Early Warning (PEW) Score,11 may further assist hospitals and practitioners identify hospitalized children at risk for deterioration on the inpatient ward.

Survival to Discharge of ERT Activation Patients
Admission DiagnosisSurvival to Discharge n (%)
  • Abbreviation: ERT, emergency response team.

  • Total of 137 patients missing survival discharge data.

Cardiac (n = 317)217 (69)
Infectious disease (n = 175)139 (79)
Trauma (n = 182)151 (83)
Other medical (n = 125)99 (79)
Pulmonary (n = 98)76 (78)
Neurology (n = 71)63 (89)
Other surgery (n = 74)70 (95)
Neurosurgery (n = 64)53 (83)
Surgery (n = 47)39 (83)
Hematology/oncology (n = 44)24 (55)
Total (n = 1197)*931 (78)

Acknowledgements

The acknowledge Dr. Genie Roosevelt MD, MPH, and Sara Deakyne MPH for their assistance in data analysis.

The incidence of sudden pediatric cardiac or respiratory arrest is low.1 Most inpatient pediatric arrests appear to occur as progression of respiratory distress or shock.2 The outcome of inpatient pediatric cardiorespiratory arrests continues to be poor, emphasizing the need for early recognition and intervention. In 2006, as part of its 100,000 Lives Campaign the Institute for Healthcare Improvement recommended the implementation of Rapid Response Teams (RRT) as 1 of the strategies to reduce the number of preventable inpatient deaths.3 We reviewed all emergency response team (ERT) activations for last 13 years at The Children's Hospital in Denver, CO to assist in the development of a new RRT and to identify at risk populations, situations, and system processes the RRT should address.

This is a retrospective review of 13 years of data collection on ERT activations at The Children's Hospital in Denver, CO. We describe demographic and clinical variables, including outcomes of ERT activations at a free‐standing tertiary care children's hospital.

Background/Methods

The Children's Hospital (TCH) is a 270 inpatient bed tertiary care free standing children's hospital associated with University of Colorado at Denver Health Sciences Center. The current distribution of inpatient beds includes: 168 medical/surgical beds, 102 critical care beds (26 pediatric intensive care unit, 16 cardiac intensive care unit, and 60 neonatal intensive care unit). In 2006, nearly 10,000 patients were admitted for care at this hospital with an average inpatient stay of 6 days. TCH is a Level One Regional Trauma Center serving a catchment area of seven states, and a major transplant center for heart, solid organs, and bone marrow.

The history of the TCH Emergency Response Team dates back to 1990 when we first began to follow cardiorespiratory arrests in noncritical care areas of the hospital. In 1992, a Cardiac Or Respiratory event (COR) team including the most senior in‐house specialist was available 24 hours a day, 7 days a week to respond to all arrests within the hospital. The COR committee provided oversight and monitoring of the arrest events, including standardization of crash carts and the development of mock codes to ensure that the responding personnel were qualified in resuscitation practices. The COR team has evolved over the ensuing years, including a name change to the Emergency Response Team (ERT); now a single number activated by any medical staff is used to call the operator who activates the ERT via overhead paging and via code pager system. The 14 member Emergency Response Team consists of PICU/CICU/anesthesia/surgical fellows, ED attending, in‐house residents, PICU/CICU/ED charge nurse, nursing supervisor, resource RN, pharmacist, respiratory therapist, and a messenger.

A database has been maintained by 1 of the authors (DBH) since the inception of the COR/ERT at TCH 16 years ago. This is a retrospective review of this database of ERT activations. An ERT activation could have been triggered by any event that was felt to be emergent, life threatening, and/or needing immediate medical attention. After the event, a debriefing form was filled out about the event. Data collected included date, time, medical record number, location, primary care service, age, sex, primary and secondary diagnoses, and disposition. Data not captured in this database included initial rhythm, need for compressions, cardiac medications, defibrillation or intubation.

Analyses were performed on data collected from January 1993 through April 2007. Medical records of the documented ERT activations were reviewed for missing information and/or clarification of the events. Categories entered in Statistical Package for Social Statistics (SPSS) were similar to information included in the debriefing form: age, sex, admission diagnosis, precipitating event, percentage of admissions, acute vs. chronic diagnosis, winter vs. nonwinter months (October‐March/April‐September), day (6 am‐6 pm) and night (6 pm‐6 am) shifts, survival of ERT activation, survival to discharge, and primary attending service. Data were analyzed using SPSS 16.0 (2007, Chicago, IL). The study was approved by the Colorado Multiple Institutional Review Board.

Results/Conclusion

There were 1537 ERT activations in the database. A total of 203 ERT activations were eliminated from the database: 177 were eliminated from analysis because of missing age, admission diagnosis or time of day of activation, and 26 were ERT activations that had been triggered on adult visitors or adult employees. The remaining 1334 ERT activations were included for analysis.

Table 1 shows the demographics of the patients. The median age was 1.8 years, with a range of 0 to 29 years. A total of 39%(511) of all ERT activations occurred in patients under the age of 1 year with the highest incidence between 1 month and 1 year. Overall, the children at highest risk were males less than 1 year of age with a chronic diagnosis. In addition, time of day and time of year of ERT activations were analyzed as shown. There was no statistical difference between nonwinter (April‐September) and winter (October‐March) months. Statistically, there were significantly more ERT activations during day shifts (6 am‐6 pm) as compared to night shifts (P < 0.001).

Demographic and Clinical Characteristics of ERT Activation Patients
Variable (n = 1334)n (%)
  • Abbreviation: ERT, emergency response team.

  • P < 0.001.

Age
Neonate127 (10)
<1 year384 (29)
1‐3 years324 (24)
4‐6 years137 (10)
7‐10 years120 (9)
11‐13 years84 (6)
14‐17 years120 (9)
>17 years38 (3)
Gender
Male807 (60)
Time of day*
Day (06:00‐18:00)784 (59)
Night (18:00‐06:00)550 (41)
Time of Year
April‐September669 (50)
October‐March665 (50)

The most common admission diagnosis (Table 2) and underlying chronic condition was cardiac disease; other common admission diagnoses were infectious disease, trauma, and pulmonary disease. The medical categories of admission diagnosis included congenital/metabolic (39%), gastrointestinal (29%), renal (18%), rheumatology (4%), toxicology (4%), psychiatry (3%), endocrine (2%), and allergy (1%). The surgery category of admission diagnosis included otolaryngology (63%), orthopedics (28%), urology (4%), dental (4%), and ophthalmology (1%).

Admission Diagnosis Categories of ERT Activation Patients
Admission Diagnosis (n = 1334)n (%)
  • Abbreviation: ERT, emergency response team.

Cardiac370 (28)
Infectious disease197 (15)
Trauma192 (14)
Other medical137 (10)
Pulmonary109 (8)
Neurology79 (6)
Other surgery76 (6)
Neurosurgery68 (5)
Hematology/oncology53 (4)
Surgery53 (4)

Finally, the patients' survival rate after an ERT itself was to be 90% (Table 3), with an overall survival rate to discharge of 78% (Table 4). Survival rate to discharge of those patients who survived the ERT event was 87%. Two patients were missing survival event data and 137 patients were missing survival to discharge data.

Survival of Event of ERT Activation Patients
Admission DiagnosisSurvival of Event n (%)
  • Abbreviation: ERT, emergency response team.

  • Two patients missing survival event data.

Cardiac (n = 370)315 (85)
Infectious disease (n = 197)186 (94)
Trauma (n = 191)168 (88)
Other medical (n = 136)125 (92)
Pulmonary (n = 109)97 (89)
Neurology (n = 79)75 (95)
Other surgery (n = 76)75 (99)
Neurosurgery (n = 68)63 (93)
Surgery (n = 53)50 (94)
Hematology/oncology (n = 53)46 (87)
Total (n = 1332)*1200 (90)

Discussion

We present a retrospective review of 1334 emergency response team activations over 13 years at an academic free‐standing tertiary care children's hospital. In keeping with previous reports, we found that children less than 1 year of age were at the highest risk for activation of the emergency response team.1, 4, 5

The National Registry for CardioPulmonary Resuscitation (NRCPR) database cite respiratory failure (asphyxia) and circulatory shock (ischemia) as the most common causes of in‐hospital cardiac arrests.1 Additionally, more than half of pediatric patients that experience a cardiopulmonary arrest have an underlying chronic illness.1, 2, 4, 5, 6 These are similar to our findings that chronic pulmonary and cardiac diseases were among the most frequent admission diagnosis.

Unlike Peberdy et al.,7we did not find an increase in the number of emergency response team activations at night or on weekends. Instead, we found that an ERT activation was more likely to be requested during the day shifts (6 am‐6 pm) which is a similar to that reported by Jones et al.8 Similarly, Jones et al.8 reported that the hourly rate of their medical emergency team activation was greater during the time between 8 am and 6 pm.

Our overall survival rate (78%) to discharge after an ERT event was much higher than what has been reported by Topjian et al.9 (25%). This likely reflects our inclusion of all emergency response team activations, not just apneic and asystolic arrests. The improved survival rate may also be influenced by the 24/7 presence of pediatric fellows, residents and surgeons in our hospital, which has been associated with improved 24‐hour survival for children receiving in‐hospital cardiopulmonary resuscitation.10

There are several limitations to our data collection and this report. This was a retrospective review and as previously noted some medical details were absent resulting in the exclusion of some cases. We also found that the original debriefing form which was used as the basis for the database did not include some important clinical variable, such as vital signs, more detailed events such as rhythm, medications used, and deficits or changes in baseline function. We suggest including multiple variables in a future multicenter study of pediatric RRT's: facility, admitting service, admission diagnosis, age (chronologic and gestational), sex, how long patient has been in hospital prior to the event, past medical history, vitals signs before and after activation, time/date, location, any precipitating events and actions taken by the RRT (eg, cardiopulmonary resuscitation, defibrillation, emergent intubation, and other emergent interventions), medications, survival of event, survival to discharge, deficits or changes in baseline function after the event and to discharge. In particular, information about history of prematurity would have been helpful in assessing further risk factors. Analysis of survival with or without significant deficits or changes in baseline function would be another useful outcome measure. Figure 1 is an example of a debriefing form a multi‐institutional study or hospitalist led quality improvement project may use to collect this data.

Figure 1
Code Blue Activation Follow‐Up and Debriefing Form.

We were able to identify a population of higher‐risk patients (less than 1 year of age with comorbidities and an admission diagnosis of cardiac or respiratory disease) to focus our educational efforts on earlier recognition of patient deterioration for both inpatient ward staff and RRT responders. These findings may assist in future quality assurance issues such as patient placement and early ICU admissions depending on age, chronic conditions, and/or admission diagnosis. Future directions should include multi‐center study of RRT to improve external validity. In addition, a more careful analysis of events surrounding the activation, including incorporating such tools as the Pediatric Early Warning (PEW) Score,11 may further assist hospitals and practitioners identify hospitalized children at risk for deterioration on the inpatient ward.

Survival to Discharge of ERT Activation Patients
Admission DiagnosisSurvival to Discharge n (%)
  • Abbreviation: ERT, emergency response team.

  • Total of 137 patients missing survival discharge data.

Cardiac (n = 317)217 (69)
Infectious disease (n = 175)139 (79)
Trauma (n = 182)151 (83)
Other medical (n = 125)99 (79)
Pulmonary (n = 98)76 (78)
Neurology (n = 71)63 (89)
Other surgery (n = 74)70 (95)
Neurosurgery (n = 64)53 (83)
Surgery (n = 47)39 (83)
Hematology/oncology (n = 44)24 (55)
Total (n = 1197)*931 (78)

Acknowledgements

The acknowledge Dr. Genie Roosevelt MD, MPH, and Sara Deakyne MPH for their assistance in data analysis.

References
  1. Berg MD,Nadkarni VM,Zuercher M,Berg RA.In‐hospital pediatric arrest.Pediatr Clin North Am.2008;55(3):589604.
  2. Nadkarni VM,Larkin GL,Peberdy MA, et al.First documented rhythm and clinical outcome from in‐hospital cardiac arrest among children and adults.JAMA.2006;295:5057.
  3. 57100K Lives Campaign‐getting started Kit: Rapid response Teams. Available at: http://www.ihi.org/IHI/Programs/Campaign/Campaign.htm?TabId=1 Accessed July2010.
  4. Meaney PA,Nadkarni VM,Cook EF, et al.Higher survival rates among younger patients after pediatric intensive care unity cardiac arrests.Pediatrics2006;118:24242433.
  5. Reis A.G,Nadkarni V.M. et al.A prospective investigation into the epidemiology of in‐hospital pediatric cardiopulmonary resuscitation using the internationalUtseinReporting Style.Pediatrics.2002;109:200209.
  6. Gillis J,Dickson D,Rieder M,Steward D,Edmonds J.Results of inpatient pediatric resuscitation.Crit Care Med.1986;14:469471
  7. Peberdy MA,Ornato JP,Larkin GL, et al.Survival from in‐hospital cardiac arrest during nights and weekends.JAMA.2008;299:785792.
  8. Jones D,Bates S,Warrillow S, et al.Circadian pattern of activation of the medical emergency team in a teaching hospital.Crit Care.2005;9:R303R306.
  9. Topjian AA,Berg RA,Nadkarni VM.Pediatric cardiopulmonary resuscitation: advances in science, techniques, and outcomes.Pediatrics.2008;122;10861098
  10. Donoghue AJ,Nadkarni VM,Elliott M, et al.Effect of hospital characteristics on outcomes from pediatric cardiopulmonary resuscitation: a report from the National Registry of Cardiopulmonary Resuscitation.Pediatrics.2006;118:9951001.
  11. Duncan H,Hutchinson J,Parshuram CS.The Pediatric Early Warning System score: a severity of illness score to predict urgent medical need in hospitalized children.J Crit Care.2006;21(3):271278.
References
  1. Berg MD,Nadkarni VM,Zuercher M,Berg RA.In‐hospital pediatric arrest.Pediatr Clin North Am.2008;55(3):589604.
  2. Nadkarni VM,Larkin GL,Peberdy MA, et al.First documented rhythm and clinical outcome from in‐hospital cardiac arrest among children and adults.JAMA.2006;295:5057.
  3. 57100K Lives Campaign‐getting started Kit: Rapid response Teams. Available at: http://www.ihi.org/IHI/Programs/Campaign/Campaign.htm?TabId=1 Accessed July2010.
  4. Meaney PA,Nadkarni VM,Cook EF, et al.Higher survival rates among younger patients after pediatric intensive care unity cardiac arrests.Pediatrics2006;118:24242433.
  5. Reis A.G,Nadkarni V.M. et al.A prospective investigation into the epidemiology of in‐hospital pediatric cardiopulmonary resuscitation using the internationalUtseinReporting Style.Pediatrics.2002;109:200209.
  6. Gillis J,Dickson D,Rieder M,Steward D,Edmonds J.Results of inpatient pediatric resuscitation.Crit Care Med.1986;14:469471
  7. Peberdy MA,Ornato JP,Larkin GL, et al.Survival from in‐hospital cardiac arrest during nights and weekends.JAMA.2008;299:785792.
  8. Jones D,Bates S,Warrillow S, et al.Circadian pattern of activation of the medical emergency team in a teaching hospital.Crit Care.2005;9:R303R306.
  9. Topjian AA,Berg RA,Nadkarni VM.Pediatric cardiopulmonary resuscitation: advances in science, techniques, and outcomes.Pediatrics.2008;122;10861098
  10. Donoghue AJ,Nadkarni VM,Elliott M, et al.Effect of hospital characteristics on outcomes from pediatric cardiopulmonary resuscitation: a report from the National Registry of Cardiopulmonary Resuscitation.Pediatrics.2006;118:9951001.
  11. Duncan H,Hutchinson J,Parshuram CS.The Pediatric Early Warning System score: a severity of illness score to predict urgent medical need in hospitalized children.J Crit Care.2006;21(3):271278.
Issue
Journal of Hospital Medicine - 6(3)
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Journal of Hospital Medicine - 6(3)
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131-135
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Retrospective review of emergency response activations during a 13‐year period at a tertiary care children's hospital
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Retrospective review of emergency response activations during a 13‐year period at a tertiary care children's hospital
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cardiac or respiratory event, emergency response team, pediatrics, rapid response team, the Children's Hospital
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Study Associates Inflammatory Bowel Disease with VTE

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Study Associates Inflammatory Bowel Disease with VTE

Hospitalists should pay attention to a new study that shows patients with inflammatory bowel disease (IBD) are at increased risk of recurrent VTE, according to a veteran hospitalist who studies the topic. Until research advances to the point it can identify weighted risk, however, it’s difficult to emphasize the results too much, he adds.

Still, Alpesh Amin, MD, MBA, SFHM, FACP, professor and chairman of the Department of Medicine and executive director of the HM program at the University of California at Irvine, says the new research solidifies the idea that HM groups should know whether a patient has IBD when doing a risk assessment.

"Now the question is, 'Which risk factors are most significant?'" Dr. Amin says. "More information needs to come to help define that."

The 14-center cohort study found that the probability of recurrence five years after discontinuation of anticoagulation therapy was higher among patients with IBD than patients without IBD (33.4%; 95% confidence interval [CI]: 21.8–45.0 vs. 21.7%; 95% CI: 18.8–24.6; P=0.01) (Gastroenterology. 2010;139(3):779-787). In addition, after adjustment for potential confounders, IBD also rates as an independent risk factor of recurrence (hazard ratio=2.5; 95% CI: 1.4–4.2; P=0.001).

Dr. Amin would like to see data that delineate the risk differential between hospitalized patients with IBD and hospitalized patients admitted for acute flare-ups of their IBD. For example, an IBD patient admitted with bloody diarrhea is usually steered away from anticoagulants for fear of increased bleeding. In some of those cases, hospitalists may instead use an inferior vena cava (IVC) filter. Those devices recently drew attention after an Archives of Internal Medicine report (PDF) and an FDA advisory questioned their long-term safety implications.

"We don't have strong evidence whether having acute flare-ups makes the risk worse or not," Dr. Amin says. "We need to figure out how to deal with that issue."

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Hospitalists should pay attention to a new study that shows patients with inflammatory bowel disease (IBD) are at increased risk of recurrent VTE, according to a veteran hospitalist who studies the topic. Until research advances to the point it can identify weighted risk, however, it’s difficult to emphasize the results too much, he adds.

Still, Alpesh Amin, MD, MBA, SFHM, FACP, professor and chairman of the Department of Medicine and executive director of the HM program at the University of California at Irvine, says the new research solidifies the idea that HM groups should know whether a patient has IBD when doing a risk assessment.

"Now the question is, 'Which risk factors are most significant?'" Dr. Amin says. "More information needs to come to help define that."

The 14-center cohort study found that the probability of recurrence five years after discontinuation of anticoagulation therapy was higher among patients with IBD than patients without IBD (33.4%; 95% confidence interval [CI]: 21.8–45.0 vs. 21.7%; 95% CI: 18.8–24.6; P=0.01) (Gastroenterology. 2010;139(3):779-787). In addition, after adjustment for potential confounders, IBD also rates as an independent risk factor of recurrence (hazard ratio=2.5; 95% CI: 1.4–4.2; P=0.001).

Dr. Amin would like to see data that delineate the risk differential between hospitalized patients with IBD and hospitalized patients admitted for acute flare-ups of their IBD. For example, an IBD patient admitted with bloody diarrhea is usually steered away from anticoagulants for fear of increased bleeding. In some of those cases, hospitalists may instead use an inferior vena cava (IVC) filter. Those devices recently drew attention after an Archives of Internal Medicine report (PDF) and an FDA advisory questioned their long-term safety implications.

"We don't have strong evidence whether having acute flare-ups makes the risk worse or not," Dr. Amin says. "We need to figure out how to deal with that issue."

Hospitalists should pay attention to a new study that shows patients with inflammatory bowel disease (IBD) are at increased risk of recurrent VTE, according to a veteran hospitalist who studies the topic. Until research advances to the point it can identify weighted risk, however, it’s difficult to emphasize the results too much, he adds.

Still, Alpesh Amin, MD, MBA, SFHM, FACP, professor and chairman of the Department of Medicine and executive director of the HM program at the University of California at Irvine, says the new research solidifies the idea that HM groups should know whether a patient has IBD when doing a risk assessment.

"Now the question is, 'Which risk factors are most significant?'" Dr. Amin says. "More information needs to come to help define that."

The 14-center cohort study found that the probability of recurrence five years after discontinuation of anticoagulation therapy was higher among patients with IBD than patients without IBD (33.4%; 95% confidence interval [CI]: 21.8–45.0 vs. 21.7%; 95% CI: 18.8–24.6; P=0.01) (Gastroenterology. 2010;139(3):779-787). In addition, after adjustment for potential confounders, IBD also rates as an independent risk factor of recurrence (hazard ratio=2.5; 95% CI: 1.4–4.2; P=0.001).

Dr. Amin would like to see data that delineate the risk differential between hospitalized patients with IBD and hospitalized patients admitted for acute flare-ups of their IBD. For example, an IBD patient admitted with bloody diarrhea is usually steered away from anticoagulants for fear of increased bleeding. In some of those cases, hospitalists may instead use an inferior vena cava (IVC) filter. Those devices recently drew attention after an Archives of Internal Medicine report (PDF) and an FDA advisory questioned their long-term safety implications.

"We don't have strong evidence whether having acute flare-ups makes the risk worse or not," Dr. Amin says. "We need to figure out how to deal with that issue."

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The Hospitalist - 2010(11)
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The Hospitalist - 2010(11)
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Study Associates Inflammatory Bowel Disease with VTE
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Study Associates Inflammatory Bowel Disease with VTE
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