Adding antiandrogen treatment to salvage radiotherapy markedly improves long-term survival and disease-specific mortality, reduces the rate of distant metastases, and decreases the incidence of further recurrences in men who have an initial biochemical recurrence of prostate cancer, according to a report in the New England Journal of Medicine.

These are the long-term findings of a prospective multicenter trial begun in 1998, which compared standard radiotherapy plus daily oral bicalutamide against radiotherapy plus placebo. The survival benefit became evident only during the second decade after treatment, said William U. Shipley, MD, of Massachusetts General Hospital and Harvard Medical School, Boston, and his associates.

During the course of this study, bicalutamide was superseded by injectable GnRH agonists as the add-on antiandrogen treatment of choice, “but the hypothesis tested in this trial remains very relevant,” they noted. “Our trial presents proof of principle that the addition of hormone-based therapy to salvage radiation therapy is associated with significant and clinically important lower rates of prostate-cancer metastases and death.”

The study involved 760 men (median age, 65 years) who had undergone radical prostatectomy with lymphadenectomy for T2 or T3 prostate cancer without nodal involvement. A median of 2 years after surgery, all of them showed rising PSA levels (biochemical recurrence) but no metastases. They were randomly assigned in a double-blind fashion to receive salvage radiotherapy plus a 2-year course of bicalutamide (384 patients) or matching placebo (376 patients) and were followed for a median of 13 years.

The primary endpoint – the rate of overall survival at 12 years – was 76.3% in the bicalutamide group and 71.3% in the placebo group, for a hazard ratio of 0.77. An estimated 20 patients would need to be treated with bicalutamide to avoid one death over a 12-year period, the investigators said (N Engl J Med. 2017 Feb 2 [doi: 10.1056/NEJMoa1607529]).

The rates of prostate cancer–specific death were 5.8% and 13.4%, respectively (HR, 0.49). The cumulative incidences of distant metastases were 14.5% vs. 23.0% (HR, 0.63), and those of biochemical recurrence were 44.0% vs. 67.9% (HR, 0.48). Post hoc subgroup analyses showed that the survival benefit was greatest among men who had the most aggressive cancers, such as those with the highest PSA levels, the highest Gleason scores, and positive rather than negative surgical margins.

Compared with placebo, bicalutamide did not exacerbate early or late bladder, bowel, hematologic, or hepatic effects of radiotherapy, and it was not associated with any increase in cardiac death, Dr. Shipley and his associates said.

The National Cancer Institute and AstraZeneca supported the trial. Dr. Shipley reported previously holding stock in PFS Genomics; his associates reported ties to numerous industry sources.

Adding antiandrogen treatment to salvage radiotherapy markedly improves long-term survival and disease-specific mortality, reduces the rate of distant metastases, and decreases the incidence of further recurrences in men who have an initial biochemical recurrence of prostate cancer, according to a report in the New England Journal of Medicine.

These are the long-term findings of a prospective multicenter trial begun in 1998, which compared standard radiotherapy plus daily oral bicalutamide against radiotherapy plus placebo. The survival benefit became evident only during the second decade after treatment, said William U. Shipley, MD, of Massachusetts General Hospital and Harvard Medical School, Boston, and his associates.

During the course of this study, bicalutamide was superseded by injectable GnRH agonists as the add-on antiandrogen treatment of choice, “but the hypothesis tested in this trial remains very relevant,” they noted. “Our trial presents proof of principle that the addition of hormone-based therapy to salvage radiation therapy is associated with significant and clinically important lower rates of prostate-cancer metastases and death.”

The study involved 760 men (median age, 65 years) who had undergone radical prostatectomy with lymphadenectomy for T2 or T3 prostate cancer without nodal involvement. A median of 2 years after surgery, all of them showed rising PSA levels (biochemical recurrence) but no metastases. They were randomly assigned in a double-blind fashion to receive salvage radiotherapy plus a 2-year course of bicalutamide (384 patients) or matching placebo (376 patients) and were followed for a median of 13 years.

The primary endpoint – the rate of overall survival at 12 years – was 76.3% in the bicalutamide group and 71.3% in the placebo group, for a hazard ratio of 0.77. An estimated 20 patients would need to be treated with bicalutamide to avoid one death over a 12-year period, the investigators said (N Engl J Med. 2017 Feb 2 [doi: 10.1056/NEJMoa1607529]).

The rates of prostate cancer–specific death were 5.8% and 13.4%, respectively (HR, 0.49). The cumulative incidences of distant metastases were 14.5% vs. 23.0% (HR, 0.63), and those of biochemical recurrence were 44.0% vs. 67.9% (HR, 0.48). Post hoc subgroup analyses showed that the survival benefit was greatest among men who had the most aggressive cancers, such as those with the highest PSA levels, the highest Gleason scores, and positive rather than negative surgical margins.

Compared with placebo, bicalutamide did not exacerbate early or late bladder, bowel, hematologic, or hepatic effects of radiotherapy, and it was not associated with any increase in cardiac death, Dr. Shipley and his associates said.

The National Cancer Institute and AstraZeneca supported the trial. Dr. Shipley reported previously holding stock in PFS Genomics; his associates reported ties to numerous industry sources.

Adding antiandrogen treatment to salvage radiotherapy markedly improves long-term survival and disease-specific mortality, reduces the rate of distant metastases, and decreases the incidence of further recurrences in men who have an initial biochemical recurrence of prostate cancer, according to a report in the New England Journal of Medicine.

These are the long-term findings of a prospective multicenter trial begun in 1998, which compared standard radiotherapy plus daily oral bicalutamide against radiotherapy plus placebo. The survival benefit became evident only during the second decade after treatment, said William U. Shipley, MD, of Massachusetts General Hospital and Harvard Medical School, Boston, and his associates.

During the course of this study, bicalutamide was superseded by injectable GnRH agonists as the add-on antiandrogen treatment of choice, “but the hypothesis tested in this trial remains very relevant,” they noted. “Our trial presents proof of principle that the addition of hormone-based therapy to salvage radiation therapy is associated with significant and clinically important lower rates of prostate-cancer metastases and death.”

The study involved 760 men (median age, 65 years) who had undergone radical prostatectomy with lymphadenectomy for T2 or T3 prostate cancer without nodal involvement. A median of 2 years after surgery, all of them showed rising PSA levels (biochemical recurrence) but no metastases. They were randomly assigned in a double-blind fashion to receive salvage radiotherapy plus a 2-year course of bicalutamide (384 patients) or matching placebo (376 patients) and were followed for a median of 13 years.

The primary endpoint – the rate of overall survival at 12 years – was 76.3% in the bicalutamide group and 71.3% in the placebo group, for a hazard ratio of 0.77. An estimated 20 patients would need to be treated with bicalutamide to avoid one death over a 12-year period, the investigators said (N Engl J Med. 2017 Feb 2 [doi: 10.1056/NEJMoa1607529]).

The rates of prostate cancer–specific death were 5.8% and 13.4%, respectively (HR, 0.49). The cumulative incidences of distant metastases were 14.5% vs. 23.0% (HR, 0.63), and those of biochemical recurrence were 44.0% vs. 67.9% (HR, 0.48). Post hoc subgroup analyses showed that the survival benefit was greatest among men who had the most aggressive cancers, such as those with the highest PSA levels, the highest Gleason scores, and positive rather than negative surgical margins.

Compared with placebo, bicalutamide did not exacerbate early or late bladder, bowel, hematologic, or hepatic effects of radiotherapy, and it was not associated with any increase in cardiac death, Dr. Shipley and his associates said.

The National Cancer Institute and AstraZeneca supported the trial. Dr. Shipley reported previously holding stock in PFS Genomics; his associates reported ties to numerous industry sources.

Shivani Ghoshal, MD; and Lawrence J. Hirsch, MD

Department of Neurology

Yale University School of Medicine

New Haven, Connecticut

Disclosures:

Dr. Hirsch reports research support to Yale University for investigator-initiated studies from Eisai Inc, Lundbeck, Sunovion Pharmaceuticals Inc, and Upsher-Smith Laboratories, Inc, all of whom market or plant to market medications for epilepsy/seizures. He also reports consultation frees for advising from Marinus Pharmaceuticals, Inc, Sun Pharmaceutical Industries Ltd., Sunovion Pharmaceuticals Inc,and Upsher-Smith Laboratories, Inc., all of whom market or plan to market medications for epilepsy/seizures.

Introduction

Status epilepticus (SE) is a common neurological emergency that requires prompt recognition, management, and work-up. Just over one-third of SE cases are refractory to appropriate first- and second-line treatment.¹ A portion of these refractory cases occur in healthy patients with no prior significant medical disease or history of epilepsy. Despite standard initial evaluations including imaging and lumbar puncture, their etiology remains unclear after the first couple days.

This review focuses on this last understudied group of patients, who have the condition known as NORSE: cryptogenic new-onset refractory status epilepticus. We will focus on practical approaches to work-up and management for the >3000 patients with this syndrome in the United States each year.

Case

A 22-year-old right-handed male high school teacher with no significant past medical or seizure history presented to another hospital following a convulsive seizure, as well as 5 focal seizures. On initial exam, he was afebrile, lethargic, disoriented, and had anomia, but normal cranial nerve, motor and reflex examinations. In the week preceding his presentation, the patient had headaches, intermittent fever, nausea, and vomiting.

Magnetic resonance imaging (MRI) brain with and without contrast was unremarkable. Cerebrospinal fluid findings (CSF) studies showed a glucose of 74 mg/dL (normal), protein of 24 mg/dL (normal), no xanthochromia, 1 red blood cell, and 13 white blood cells, all lymphocytes. Acyclovir was started and phenytoin was loaded. He continued to have focal seizures with impaired awareness despite addition of levetiracetam and valproate, and was transferred to our center, where continuous electroencephalogram (EEG) monitoring was begun.

The patient rapidly developed refractory nonconvulsive status epilepticus. He was intubated and started on a midazolam infusion. He continued to have intermittent seizures, almost all nonconvulsive, despite high dose midazolam (up to 2.5 mg/kg/h). Propofol infusion was added and led to seizure control, but seizures returned during the propofol taper. Treatments utilized over the next 3 to 4 weeks included pentobarbital and ketamine infusions, steroids, antibiotics, and later phenobarbital to aid with weaning off pentobarbital.

The patient achieved seizure control after 33 days, and remained in the ICU for 66 days. His course was complicated by severe acidosis and rhabdomyolysis during his high-dose midazolam and ketamine infusions (resembling propofol-infusion syndrome, but with no recent use of propofol), a collapsed lung, and a brief cardiac arrest. His pancreatic enzymes were elevated on admission, and remained so. His spine MRI showed extensive abnormal signal. He underwent tracheostomy and percutaneous gastrostomy placement, and was discharged to an acute rehabilitation facility fully alert.

Multiple lab investigations, including a work-up for autoimmune and paraneoplastic encephalitis, were all negative, except serologies for mycoplasma pneumonia returned IgM+/IgG+, later becoming IgM-/IgG+, suggesting recent infection. He completed a course of doxycycline. His pancreatitis and myelitis were felt to be secondary to mycoplasma.

At one-year follow-up, he completely returned to his cognitive and behavioral baseline, including his upbeat, charismatic personality. At 4 years out, he remains with normal cognition, a moderate spastic paraparesis, and only rare, brief focal seizures (about 1 per year). Three years after NORSE, he was accepted into graduate school at multiple institutions, including an Ivy League school. He does voluntary motivational speaking as well.

Definitions^1-3

1. Status epilepticus (SE) – Any 1 of the following:
   1. Convulsive seizure with impaired consciousness lasting >5 minutes

   2. ≥2 seizures without full recovery in between

   3. Nonconvulsive or electrographic seizure activity lasting 10 minutes⁴

   4. Electrographic seizure activity occupying >50% of any hour.

2. Refractory status epilepticus (RSE): persistent SE that fails to respond to at least 2 appropriate parenteral medications.
3. NORSE: no prior epilepsy, and new onset of RSE without an obvious cause after the first 48 hours of evaluation (adequate time to rule out strokes, brain masses, drug overdoses, and common viral encephalitides such as herpes simplex virus-1).1,2

Related syndromes:

There are multiple related names and syndromes, most commonly FIRES (febrile infection-related epilepsy syndrome).⁵ This typically refers to children with a recent febrile illness (within 2 weeks), followed by NORSE, and most commonly followed by chronic epilepsy. We view FIRES as a subcategory of NORSE.

Epidemiology:

We estimate the following annual incidences in the United States, though these are likely to be underestimates:

Status epilepticus: ~45,000 cases.

Calculation: ~14/100,000 per year,⁶ with US population = 325 million

            Refractory SE (any etiology): 37% of SE³ = ~17,000

            NORSE (cryptogenic RSE): 130/675 RSE cases in Gaspard et al² = 19% of RSE

= ~3200 cases in the US each year.

NORSE in the literature:

Much of what is known regarding NORSE comes from retrospective case studies.^2,7-10 The largest included 130 patients2; of these patients, 60% presented with some prodrome up to 2 weeks prior to admission, with confusion in 45% and fever in 34%. MRI brain scans were normal in 38% of cases. In the remaining cases, the abnormalities were most often seen on fluid-attenuated inversion recovery images, within the limbic or neocortical areas. 65% of patients had CSF pleocytosis, usually mild (median 5 lymphocytes), though this did not necessarily indicate an infectious or inflammatory cause. CSF abnormalities occurred as frequently in cryptogenic cases as those with causes eventually identified (Table). Retrospective reviews have shown SE itself can be associated with a CSF pleocytosis of 6 to 28 lymphocytes/mL in up to 30% of patients, despite negative laboratory and radiologic testing for established causes.¹¹

Table. NORSE Diagnostic Checklist

Within first 24 hours:

• Initiate institution status epilepticus protocol
• Obtain thorough history, especially regarding immunosuppression, medications and supplements, recent travel to endemic areas, accidental or occupational exposure to animals, insects, pathogens, drugs or toxins
• Consider treatment for possible HSV encephalitis
• Triage for appropriate cardiopulmonary support
• MRI brain with and without contrast; consider MRA and MRV head
• Initiate continuous EEG, regardless of cessation of convulsive activity
• Serologic/imaging tests (see below)

At 48 hours:

• Assess returned testing, initiate appropriate treatments
• If patient continues to have refractory status epilepticus or coma, transfer to higher level of care for appropriate further treatment of NORSE at a center with experience in these cases, including continuous video/EEG monitoring.

At 72 hours:

• Consider initiation of 5-day course of high dose parental corticosteroids. Transfer to higher level of care for consideration of IVIG, plasmapheresis, or further immunomodulatory therapy if no clear diagnosis, if still having seizures, if no continuous EEG monitoring available, or if still comatose.

*This is not a complete list of tests to be done, but is a sample of suggested tests. Table adapted from http://www.norseinstitute.org/ , with permission. Please see that website for the full table, as well as other helpful tables including a sample status epilepticus protocol, zoonotic/geographic tips, diagnostic clues to specific organisms or syndromes, and list of medications, drugs and toxins that can cause status epilepticus.

Recommendations for work-up

Each patient with cryptogenic refractory status should undergo rigorous systemic and CSF infectious work-ups for viral, bacterial, and atypical agents. Systemic metabolic and general autoimmune panels should be sent, as well as further work-up for autoimmune or paraneoplastic causes. Of the 130 NORSE patients from the Gaspard et al 2015 study, 48% of patients were ultimately found to have autoimmune or paraneoplastic encephalitis.² Among these, anti-NMDA antibodies (12%) and anti-voltage-gated potassium channel antibodies (6%) were the most frequent. 52% of patients from this study remained cryptogenic despite extensive investigation.

Other retrospective case studies have similarly noted a percentage of NORSE patients with an underlying autoimmune or paraneoplastic etiology.^7-10

We have included a sample checklist of recommended (for most patients) and optional (to be used in specific settings) testing for NORSE patients (Table); a more thorough list that will be updated periodically can be found at www.norseinstitute.org.

Management

The general management of SE has been recently and extensively reviewed elsewhere.^1,3,12 With regard to NORSE in particular, the body of literature is small, but findings are suggestive of the importance of early immunotherapy, even in patients without clearly identified antibodies.2 Li et al describe successful cessation of seizures by using plasma exchange in a small number of NORSE cases refractory to multiple anticonvulsants and general anesthetics.¹⁰ Khawaja et al reported improved outcomes in patients treated with immune therapies (intravenous steroids, immune globulins, plasmapheresis, or a combination).⁹ It is possible that patients with NORSE that remain cryptogenic have an underlying occult auto-immune or other immunologic condition (not yet defined); in addition, it could be that inflammatory mechanisms are occurring at the seizure focus due to ongoing seizures, creating further epileptogenicity.^12,13 Until further evidence accumulates, we recommend fairly early use of immune therapies in most or all cases of NORSE.

Outcomes

Most NORSE cases progress to super-refractory status epilepticus (SRSE), meaning persistent seizures after >24 hours of treatment, usually including anesthetics. SRSE has a mortality rate of 35% and high morbidity—in part due to the systemic effects of RSE, and in part due to prolonged ICU stays, anti-seizure and anesthetic medications.^1,12,13 In one study, all patients with prolonged SRSE developed measurable brain atrophy, with atrophy more notable in younger patients with prolonged hospitalization and longer duration of anesthetic treatment.¹⁴

However, not all patients with NORSE have a poor outcome, as demonstrated in our case. In 2013, Kilbride et al reviewed the outcomes of 63 patients in prolonged SRSE (>7 days of RSE) of any etiology.⁸ Of the 63 patients included, two-thirds survived to discharge. Of the survivors, 22% achieved good recovery (modified Rankin score 0-3) in follow-up, outcomes ranging from no disability to moderate disability. In the recent Gaspard multicenter review of patients with NORSE, 62% of the 130 patients had severe disability at hospital discharge (mRS score of 4-6), but many patients improved on longer-term follow up. Among the surviving patients, 72% had a good outcome at a median of 9 months, and 41% (26/63) had no significant disability (mRS 0-1).² The 2016 retrospective study by Hocker et al showed no correlation of development of cerebral atrophy with functional outcome¹⁴; thus, cerebral atrophy should not be used as a prognosticating factor for functional recovery. While NORSE has significant morbidity and mortality, good outcome remains possible, as shown in our case, where despite 1 month of iatrogenic coma, 2 months in the ICU, and many medical complications, he returned to baseline cognitively. We believe good outcome remains possible in many (but certainly not all) patients, especially with early recognition, aggressive treatment of seizures, and probably with use of immune therapy regardless of diagnostic testing results, at least while we await further and better investigations.

Conclusion

NORSE patients are previously healthy patients who present in refractory status epilepticus with unknown etiology despite appropriate initial investigation, including imaging and lumbar puncture. There is increasing evidence that a large proportion of these cases have a component of autoimmune encephalitis. Increased awareness of NORSE is imperative for determining the prevalence, etiologies, and best treatments for NORSE patients. There are ongoing prospective, multicenter studies investigating the role of the immune system, infections, and genetics, and tracking treatments and outcomes.

For further information or to help with further efforts to increase awareness and research into NORSE:

Websites:

• http://www.norseinstitute.org/ [primary website dedicated to NORSE]
• http://www.epilepsy.com/article/2015/10/norse-new-onset-refractory-status-epilepticus  [summary and podcast]
• https://rarediseases.org/rare-diseases/new-onset-refractory-status-epilepticus-norse/ [brief summary of NORSE]

Suggested reviews/key papers:

• Status epilepticus treatment: refs 1, 3 and 12.
• Autoimmune epilepsy and NORSE: refs 2 and 15.

1. Grover EH, Nazzal Y, Hirsch LJ. Treatment of Convulsive Status Epilepticus. Curr Treat Options Neurol. 2016;18(3):11.

2. Gaspard N, Foreman BP, Alvarez V, Cabrera Kang C et al. New-onset Refractory Status Epilepticus: Etiology, Clinical Features, and Outcome. Neurology. 2015;85(18):1604-13.

3. Hocker SE. Status Epilepticus. Continuum (Minneap Minn). 2015 Oct;21(5):1362-83.

4. Trinka E, Cock H, Hesdorffer D, Rossetti AO, Scheffer IE, Shinnar S, Shorvon, S, Lowenstein DH. A definition and classification of status epilepticus—Report of the ILAE Task Force on Classification of Status Epilepticus. Epilepsia. 2015;56(10):1515-23

5. Van Baalen A, Vezzani A, Hausler M, Kluger G. Febrile Infection-Related Epilepsy Syndrome: Clinical Review and Hypotheses of Epileptogenesis. Neuropediatrics. 2016[Epub ahead of print]

6. Betjemann JP, Josephson SA, Lowenstein DH, Burke JF. Trends in Status Epilepticus-Related Hospitalizations and Mortality: Redefined in US Practice Over Time. JAMA Neurol. 2015;72(6):650-5.

7. Wilder-Smith EPV, Lim ECH, Teoh HL, Sharma VK, Tan JJH, Chan BPL, et al. The NORSE (new-onset refractory status epilepticus) syndrome: defining a disease entity. Ann Acad Med Singap. 2005 ;34(7):417-20.

8. Kilbride RD, Reynolds AS, Szaflarski JP, Hirsch LJ. Clinical outcomes following prolonged refractory status epilepticus (PRSE). Neurocrit Care. 2013;18(3):374-85.8.

9. Khawaja AM, DeWolfe JL, Miller DW, Szaflarski JP. New-onset refractory status epilepticus (NORSE) – The potential role for immunotherapy. Epilepsy Behav. 2015; 47:17-23.

10. Li J, Saldivar C, Maganti RK. Plasma Exchange in Cryptogenic New Onset Refractory Status Epilepticus. Seizure. 2013;22(1):70-3.

11. Barry E, Hauser WA. Pleocytosis after status epilepticus. Arch Neurol. 1994 Feb;51(2):190-3.

12. Trinka E, Brigo F, Shorvon S. Recent Advances in Status Epilepticus. Curr Opin Neurol. 2016;29(2):189-98.

13. Hocker, S. Systemic Complications of Status Epilepticus – An Update. Epilepsy Behav. 2015;49:83-7.

14. Hocker S, Nagarajan E, Rabinstein AA, Hanson D, Britton W. Progressive Brain Atrophy in Super-refractory Status Epilepticus. JAMA Neurol. 2016.

.15. Gaspard N. Autoimmune Epilepsy. Continuum (Minneap Minn). 2016;22(1 Epilepsy):227-45.

Shivani Ghoshal, MD; and Lawrence J. Hirsch, MD

Department of Neurology

Yale University School of Medicine

New Haven, Connecticut

Disclosures:

Dr. Hirsch reports research support to Yale University for investigator-initiated studies from Eisai Inc, Lundbeck, Sunovion Pharmaceuticals Inc, and Upsher-Smith Laboratories, Inc, all of whom market or plant to market medications for epilepsy/seizures. He also reports consultation frees for advising from Marinus Pharmaceuticals, Inc, Sun Pharmaceutical Industries Ltd., Sunovion Pharmaceuticals Inc,and Upsher-Smith Laboratories, Inc., all of whom market or plan to market medications for epilepsy/seizures.

Introduction

Status epilepticus (SE) is a common neurological emergency that requires prompt recognition, management, and work-up. Just over one-third of SE cases are refractory to appropriate first- and second-line treatment.¹ A portion of these refractory cases occur in healthy patients with no prior significant medical disease or history of epilepsy. Despite standard initial evaluations including imaging and lumbar puncture, their etiology remains unclear after the first couple days.

This review focuses on this last understudied group of patients, who have the condition known as NORSE: cryptogenic new-onset refractory status epilepticus. We will focus on practical approaches to work-up and management for the >3000 patients with this syndrome in the United States each year.

Case

A 22-year-old right-handed male high school teacher with no significant past medical or seizure history presented to another hospital following a convulsive seizure, as well as 5 focal seizures. On initial exam, he was afebrile, lethargic, disoriented, and had anomia, but normal cranial nerve, motor and reflex examinations. In the week preceding his presentation, the patient had headaches, intermittent fever, nausea, and vomiting.

Magnetic resonance imaging (MRI) brain with and without contrast was unremarkable. Cerebrospinal fluid findings (CSF) studies showed a glucose of 74 mg/dL (normal), protein of 24 mg/dL (normal), no xanthochromia, 1 red blood cell, and 13 white blood cells, all lymphocytes. Acyclovir was started and phenytoin was loaded. He continued to have focal seizures with impaired awareness despite addition of levetiracetam and valproate, and was transferred to our center, where continuous electroencephalogram (EEG) monitoring was begun.

The patient rapidly developed refractory nonconvulsive status epilepticus. He was intubated and started on a midazolam infusion. He continued to have intermittent seizures, almost all nonconvulsive, despite high dose midazolam (up to 2.5 mg/kg/h). Propofol infusion was added and led to seizure control, but seizures returned during the propofol taper. Treatments utilized over the next 3 to 4 weeks included pentobarbital and ketamine infusions, steroids, antibiotics, and later phenobarbital to aid with weaning off pentobarbital.

The patient achieved seizure control after 33 days, and remained in the ICU for 66 days. His course was complicated by severe acidosis and rhabdomyolysis during his high-dose midazolam and ketamine infusions (resembling propofol-infusion syndrome, but with no recent use of propofol), a collapsed lung, and a brief cardiac arrest. His pancreatic enzymes were elevated on admission, and remained so. His spine MRI showed extensive abnormal signal. He underwent tracheostomy and percutaneous gastrostomy placement, and was discharged to an acute rehabilitation facility fully alert.

Multiple lab investigations, including a work-up for autoimmune and paraneoplastic encephalitis, were all negative, except serologies for mycoplasma pneumonia returned IgM+/IgG+, later becoming IgM-/IgG+, suggesting recent infection. He completed a course of doxycycline. His pancreatitis and myelitis were felt to be secondary to mycoplasma.

At one-year follow-up, he completely returned to his cognitive and behavioral baseline, including his upbeat, charismatic personality. At 4 years out, he remains with normal cognition, a moderate spastic paraparesis, and only rare, brief focal seizures (about 1 per year). Three years after NORSE, he was accepted into graduate school at multiple institutions, including an Ivy League school. He does voluntary motivational speaking as well.

Definitions^1-3

1. Status epilepticus (SE) – Any 1 of the following:
   1. Convulsive seizure with impaired consciousness lasting >5 minutes

   2. ≥2 seizures without full recovery in between

   3. Nonconvulsive or electrographic seizure activity lasting 10 minutes⁴

   4. Electrographic seizure activity occupying >50% of any hour.

2. Refractory status epilepticus (RSE): persistent SE that fails to respond to at least 2 appropriate parenteral medications.
3. NORSE: no prior epilepsy, and new onset of RSE without an obvious cause after the first 48 hours of evaluation (adequate time to rule out strokes, brain masses, drug overdoses, and common viral encephalitides such as herpes simplex virus-1).1,2

Related syndromes:

There are multiple related names and syndromes, most commonly FIRES (febrile infection-related epilepsy syndrome).⁵ This typically refers to children with a recent febrile illness (within 2 weeks), followed by NORSE, and most commonly followed by chronic epilepsy. We view FIRES as a subcategory of NORSE.

Epidemiology:

We estimate the following annual incidences in the United States, though these are likely to be underestimates:

Status epilepticus: ~45,000 cases.

Calculation: ~14/100,000 per year,⁶ with US population = 325 million

            Refractory SE (any etiology): 37% of SE³ = ~17,000

            NORSE (cryptogenic RSE): 130/675 RSE cases in Gaspard et al² = 19% of RSE

= ~3200 cases in the US each year.

NORSE in the literature:

Much of what is known regarding NORSE comes from retrospective case studies.^2,7-10 The largest included 130 patients2; of these patients, 60% presented with some prodrome up to 2 weeks prior to admission, with confusion in 45% and fever in 34%. MRI brain scans were normal in 38% of cases. In the remaining cases, the abnormalities were most often seen on fluid-attenuated inversion recovery images, within the limbic or neocortical areas. 65% of patients had CSF pleocytosis, usually mild (median 5 lymphocytes), though this did not necessarily indicate an infectious or inflammatory cause. CSF abnormalities occurred as frequently in cryptogenic cases as those with causes eventually identified (Table). Retrospective reviews have shown SE itself can be associated with a CSF pleocytosis of 6 to 28 lymphocytes/mL in up to 30% of patients, despite negative laboratory and radiologic testing for established causes.¹¹

Table. NORSE Diagnostic Checklist

Within first 24 hours:

• Initiate institution status epilepticus protocol
• Obtain thorough history, especially regarding immunosuppression, medications and supplements, recent travel to endemic areas, accidental or occupational exposure to animals, insects, pathogens, drugs or toxins
• Consider treatment for possible HSV encephalitis
• Triage for appropriate cardiopulmonary support
• MRI brain with and without contrast; consider MRA and MRV head
• Initiate continuous EEG, regardless of cessation of convulsive activity
• Serologic/imaging tests (see below)

At 48 hours:

• Assess returned testing, initiate appropriate treatments
• If patient continues to have refractory status epilepticus or coma, transfer to higher level of care for appropriate further treatment of NORSE at a center with experience in these cases, including continuous video/EEG monitoring.

At 72 hours:

• Consider initiation of 5-day course of high dose parental corticosteroids. Transfer to higher level of care for consideration of IVIG, plasmapheresis, or further immunomodulatory therapy if no clear diagnosis, if still having seizures, if no continuous EEG monitoring available, or if still comatose.

*This is not a complete list of tests to be done, but is a sample of suggested tests. Table adapted from http://www.norseinstitute.org/ , with permission. Please see that website for the full table, as well as other helpful tables including a sample status epilepticus protocol, zoonotic/geographic tips, diagnostic clues to specific organisms or syndromes, and list of medications, drugs and toxins that can cause status epilepticus.

Recommendations for work-up

Each patient with cryptogenic refractory status should undergo rigorous systemic and CSF infectious work-ups for viral, bacterial, and atypical agents. Systemic metabolic and general autoimmune panels should be sent, as well as further work-up for autoimmune or paraneoplastic causes. Of the 130 NORSE patients from the Gaspard et al 2015 study, 48% of patients were ultimately found to have autoimmune or paraneoplastic encephalitis.² Among these, anti-NMDA antibodies (12%) and anti-voltage-gated potassium channel antibodies (6%) were the most frequent. 52% of patients from this study remained cryptogenic despite extensive investigation.

Other retrospective case studies have similarly noted a percentage of NORSE patients with an underlying autoimmune or paraneoplastic etiology.^7-10

We have included a sample checklist of recommended (for most patients) and optional (to be used in specific settings) testing for NORSE patients (Table); a more thorough list that will be updated periodically can be found at www.norseinstitute.org.

Management

The general management of SE has been recently and extensively reviewed elsewhere.^1,3,12 With regard to NORSE in particular, the body of literature is small, but findings are suggestive of the importance of early immunotherapy, even in patients without clearly identified antibodies.2 Li et al describe successful cessation of seizures by using plasma exchange in a small number of NORSE cases refractory to multiple anticonvulsants and general anesthetics.¹⁰ Khawaja et al reported improved outcomes in patients treated with immune therapies (intravenous steroids, immune globulins, plasmapheresis, or a combination).⁹ It is possible that patients with NORSE that remain cryptogenic have an underlying occult auto-immune or other immunologic condition (not yet defined); in addition, it could be that inflammatory mechanisms are occurring at the seizure focus due to ongoing seizures, creating further epileptogenicity.^12,13 Until further evidence accumulates, we recommend fairly early use of immune therapies in most or all cases of NORSE.

Outcomes

Most NORSE cases progress to super-refractory status epilepticus (SRSE), meaning persistent seizures after >24 hours of treatment, usually including anesthetics. SRSE has a mortality rate of 35% and high morbidity—in part due to the systemic effects of RSE, and in part due to prolonged ICU stays, anti-seizure and anesthetic medications.^1,12,13 In one study, all patients with prolonged SRSE developed measurable brain atrophy, with atrophy more notable in younger patients with prolonged hospitalization and longer duration of anesthetic treatment.¹⁴

However, not all patients with NORSE have a poor outcome, as demonstrated in our case. In 2013, Kilbride et al reviewed the outcomes of 63 patients in prolonged SRSE (>7 days of RSE) of any etiology.⁸ Of the 63 patients included, two-thirds survived to discharge. Of the survivors, 22% achieved good recovery (modified Rankin score 0-3) in follow-up, outcomes ranging from no disability to moderate disability. In the recent Gaspard multicenter review of patients with NORSE, 62% of the 130 patients had severe disability at hospital discharge (mRS score of 4-6), but many patients improved on longer-term follow up. Among the surviving patients, 72% had a good outcome at a median of 9 months, and 41% (26/63) had no significant disability (mRS 0-1).² The 2016 retrospective study by Hocker et al showed no correlation of development of cerebral atrophy with functional outcome¹⁴; thus, cerebral atrophy should not be used as a prognosticating factor for functional recovery. While NORSE has significant morbidity and mortality, good outcome remains possible, as shown in our case, where despite 1 month of iatrogenic coma, 2 months in the ICU, and many medical complications, he returned to baseline cognitively. We believe good outcome remains possible in many (but certainly not all) patients, especially with early recognition, aggressive treatment of seizures, and probably with use of immune therapy regardless of diagnostic testing results, at least while we await further and better investigations.

Conclusion

NORSE patients are previously healthy patients who present in refractory status epilepticus with unknown etiology despite appropriate initial investigation, including imaging and lumbar puncture. There is increasing evidence that a large proportion of these cases have a component of autoimmune encephalitis. Increased awareness of NORSE is imperative for determining the prevalence, etiologies, and best treatments for NORSE patients. There are ongoing prospective, multicenter studies investigating the role of the immune system, infections, and genetics, and tracking treatments and outcomes.

For further information or to help with further efforts to increase awareness and research into NORSE:

Websites:

• http://www.norseinstitute.org/ [primary website dedicated to NORSE]
• http://www.epilepsy.com/article/2015/10/norse-new-onset-refractory-status-epilepticus  [summary and podcast]
• https://rarediseases.org/rare-diseases/new-onset-refractory-status-epilepticus-norse/ [brief summary of NORSE]

Suggested reviews/key papers:

• Status epilepticus treatment: refs 1, 3 and 12.
• Autoimmune epilepsy and NORSE: refs 2 and 15.

1. Grover EH, Nazzal Y, Hirsch LJ. Treatment of Convulsive Status Epilepticus. Curr Treat Options Neurol. 2016;18(3):11.

2. Gaspard N, Foreman BP, Alvarez V, Cabrera Kang C et al. New-onset Refractory Status Epilepticus: Etiology, Clinical Features, and Outcome. Neurology. 2015;85(18):1604-13.

3. Hocker SE. Status Epilepticus. Continuum (Minneap Minn). 2015 Oct;21(5):1362-83.

4. Trinka E, Cock H, Hesdorffer D, Rossetti AO, Scheffer IE, Shinnar S, Shorvon, S, Lowenstein DH. A definition and classification of status epilepticus—Report of the ILAE Task Force on Classification of Status Epilepticus. Epilepsia. 2015;56(10):1515-23

5. Van Baalen A, Vezzani A, Hausler M, Kluger G. Febrile Infection-Related Epilepsy Syndrome: Clinical Review and Hypotheses of Epileptogenesis. Neuropediatrics. 2016[Epub ahead of print]

6. Betjemann JP, Josephson SA, Lowenstein DH, Burke JF. Trends in Status Epilepticus-Related Hospitalizations and Mortality: Redefined in US Practice Over Time. JAMA Neurol. 2015;72(6):650-5.

7. Wilder-Smith EPV, Lim ECH, Teoh HL, Sharma VK, Tan JJH, Chan BPL, et al. The NORSE (new-onset refractory status epilepticus) syndrome: defining a disease entity. Ann Acad Med Singap. 2005 ;34(7):417-20.

8. Kilbride RD, Reynolds AS, Szaflarski JP, Hirsch LJ. Clinical outcomes following prolonged refractory status epilepticus (PRSE). Neurocrit Care. 2013;18(3):374-85.8.

9. Khawaja AM, DeWolfe JL, Miller DW, Szaflarski JP. New-onset refractory status epilepticus (NORSE) – The potential role for immunotherapy. Epilepsy Behav. 2015; 47:17-23.

10. Li J, Saldivar C, Maganti RK. Plasma Exchange in Cryptogenic New Onset Refractory Status Epilepticus. Seizure. 2013;22(1):70-3.

11. Barry E, Hauser WA. Pleocytosis after status epilepticus. Arch Neurol. 1994 Feb;51(2):190-3.

12. Trinka E, Brigo F, Shorvon S. Recent Advances in Status Epilepticus. Curr Opin Neurol. 2016;29(2):189-98.

13. Hocker, S. Systemic Complications of Status Epilepticus – An Update. Epilepsy Behav. 2015;49:83-7.

14. Hocker S, Nagarajan E, Rabinstein AA, Hanson D, Britton W. Progressive Brain Atrophy in Super-refractory Status Epilepticus. JAMA Neurol. 2016.

.15. Gaspard N. Autoimmune Epilepsy. Continuum (Minneap Minn). 2016;22(1 Epilepsy):227-45.

Shivani Ghoshal, MD; and Lawrence J. Hirsch, MD

Department of Neurology

Yale University School of Medicine

New Haven, Connecticut

Disclosures:

Dr. Hirsch reports research support to Yale University for investigator-initiated studies from Eisai Inc, Lundbeck, Sunovion Pharmaceuticals Inc, and Upsher-Smith Laboratories, Inc, all of whom market or plant to market medications for epilepsy/seizures. He also reports consultation frees for advising from Marinus Pharmaceuticals, Inc, Sun Pharmaceutical Industries Ltd., Sunovion Pharmaceuticals Inc,and Upsher-Smith Laboratories, Inc., all of whom market or plan to market medications for epilepsy/seizures.

Introduction

Status epilepticus (SE) is a common neurological emergency that requires prompt recognition, management, and work-up. Just over one-third of SE cases are refractory to appropriate first- and second-line treatment.¹ A portion of these refractory cases occur in healthy patients with no prior significant medical disease or history of epilepsy. Despite standard initial evaluations including imaging and lumbar puncture, their etiology remains unclear after the first couple days.

This review focuses on this last understudied group of patients, who have the condition known as NORSE: cryptogenic new-onset refractory status epilepticus. We will focus on practical approaches to work-up and management for the >3000 patients with this syndrome in the United States each year.

Case

A 22-year-old right-handed male high school teacher with no significant past medical or seizure history presented to another hospital following a convulsive seizure, as well as 5 focal seizures. On initial exam, he was afebrile, lethargic, disoriented, and had anomia, but normal cranial nerve, motor and reflex examinations. In the week preceding his presentation, the patient had headaches, intermittent fever, nausea, and vomiting.

Magnetic resonance imaging (MRI) brain with and without contrast was unremarkable. Cerebrospinal fluid findings (CSF) studies showed a glucose of 74 mg/dL (normal), protein of 24 mg/dL (normal), no xanthochromia, 1 red blood cell, and 13 white blood cells, all lymphocytes. Acyclovir was started and phenytoin was loaded. He continued to have focal seizures with impaired awareness despite addition of levetiracetam and valproate, and was transferred to our center, where continuous electroencephalogram (EEG) monitoring was begun.

The patient rapidly developed refractory nonconvulsive status epilepticus. He was intubated and started on a midazolam infusion. He continued to have intermittent seizures, almost all nonconvulsive, despite high dose midazolam (up to 2.5 mg/kg/h). Propofol infusion was added and led to seizure control, but seizures returned during the propofol taper. Treatments utilized over the next 3 to 4 weeks included pentobarbital and ketamine infusions, steroids, antibiotics, and later phenobarbital to aid with weaning off pentobarbital.

The patient achieved seizure control after 33 days, and remained in the ICU for 66 days. His course was complicated by severe acidosis and rhabdomyolysis during his high-dose midazolam and ketamine infusions (resembling propofol-infusion syndrome, but with no recent use of propofol), a collapsed lung, and a brief cardiac arrest. His pancreatic enzymes were elevated on admission, and remained so. His spine MRI showed extensive abnormal signal. He underwent tracheostomy and percutaneous gastrostomy placement, and was discharged to an acute rehabilitation facility fully alert.

Multiple lab investigations, including a work-up for autoimmune and paraneoplastic encephalitis, were all negative, except serologies for mycoplasma pneumonia returned IgM+/IgG+, later becoming IgM-/IgG+, suggesting recent infection. He completed a course of doxycycline. His pancreatitis and myelitis were felt to be secondary to mycoplasma.

At one-year follow-up, he completely returned to his cognitive and behavioral baseline, including his upbeat, charismatic personality. At 4 years out, he remains with normal cognition, a moderate spastic paraparesis, and only rare, brief focal seizures (about 1 per year). Three years after NORSE, he was accepted into graduate school at multiple institutions, including an Ivy League school. He does voluntary motivational speaking as well.

Definitions^1-3

1. Status epilepticus (SE) – Any 1 of the following:
   1. Convulsive seizure with impaired consciousness lasting >5 minutes

   2. ≥2 seizures without full recovery in between

   3. Nonconvulsive or electrographic seizure activity lasting 10 minutes⁴

   4. Electrographic seizure activity occupying >50% of any hour.

2. Refractory status epilepticus (RSE): persistent SE that fails to respond to at least 2 appropriate parenteral medications.
3. NORSE: no prior epilepsy, and new onset of RSE without an obvious cause after the first 48 hours of evaluation (adequate time to rule out strokes, brain masses, drug overdoses, and common viral encephalitides such as herpes simplex virus-1).1,2

Related syndromes:

There are multiple related names and syndromes, most commonly FIRES (febrile infection-related epilepsy syndrome).⁵ This typically refers to children with a recent febrile illness (within 2 weeks), followed by NORSE, and most commonly followed by chronic epilepsy. We view FIRES as a subcategory of NORSE.

Epidemiology:

We estimate the following annual incidences in the United States, though these are likely to be underestimates:

Status epilepticus: ~45,000 cases.

Calculation: ~14/100,000 per year,⁶ with US population = 325 million

            Refractory SE (any etiology): 37% of SE³ = ~17,000

            NORSE (cryptogenic RSE): 130/675 RSE cases in Gaspard et al² = 19% of RSE

= ~3200 cases in the US each year.

NORSE in the literature:

Much of what is known regarding NORSE comes from retrospective case studies.^2,7-10 The largest included 130 patients2; of these patients, 60% presented with some prodrome up to 2 weeks prior to admission, with confusion in 45% and fever in 34%. MRI brain scans were normal in 38% of cases. In the remaining cases, the abnormalities were most often seen on fluid-attenuated inversion recovery images, within the limbic or neocortical areas. 65% of patients had CSF pleocytosis, usually mild (median 5 lymphocytes), though this did not necessarily indicate an infectious or inflammatory cause. CSF abnormalities occurred as frequently in cryptogenic cases as those with causes eventually identified (Table). Retrospective reviews have shown SE itself can be associated with a CSF pleocytosis of 6 to 28 lymphocytes/mL in up to 30% of patients, despite negative laboratory and radiologic testing for established causes.¹¹

Table. NORSE Diagnostic Checklist

Within first 24 hours:

• Initiate institution status epilepticus protocol
• Obtain thorough history, especially regarding immunosuppression, medications and supplements, recent travel to endemic areas, accidental or occupational exposure to animals, insects, pathogens, drugs or toxins
• Consider treatment for possible HSV encephalitis
• Triage for appropriate cardiopulmonary support
• MRI brain with and without contrast; consider MRA and MRV head
• Initiate continuous EEG, regardless of cessation of convulsive activity
• Serologic/imaging tests (see below)

At 48 hours:

• Assess returned testing, initiate appropriate treatments
• If patient continues to have refractory status epilepticus or coma, transfer to higher level of care for appropriate further treatment of NORSE at a center with experience in these cases, including continuous video/EEG monitoring.

At 72 hours:

• Consider initiation of 5-day course of high dose parental corticosteroids. Transfer to higher level of care for consideration of IVIG, plasmapheresis, or further immunomodulatory therapy if no clear diagnosis, if still having seizures, if no continuous EEG monitoring available, or if still comatose.

*This is not a complete list of tests to be done, but is a sample of suggested tests. Table adapted from http://www.norseinstitute.org/ , with permission. Please see that website for the full table, as well as other helpful tables including a sample status epilepticus protocol, zoonotic/geographic tips, diagnostic clues to specific organisms or syndromes, and list of medications, drugs and toxins that can cause status epilepticus.

Recommendations for work-up

Each patient with cryptogenic refractory status should undergo rigorous systemic and CSF infectious work-ups for viral, bacterial, and atypical agents. Systemic metabolic and general autoimmune panels should be sent, as well as further work-up for autoimmune or paraneoplastic causes. Of the 130 NORSE patients from the Gaspard et al 2015 study, 48% of patients were ultimately found to have autoimmune or paraneoplastic encephalitis.² Among these, anti-NMDA antibodies (12%) and anti-voltage-gated potassium channel antibodies (6%) were the most frequent. 52% of patients from this study remained cryptogenic despite extensive investigation.

Other retrospective case studies have similarly noted a percentage of NORSE patients with an underlying autoimmune or paraneoplastic etiology.^7-10

We have included a sample checklist of recommended (for most patients) and optional (to be used in specific settings) testing for NORSE patients (Table); a more thorough list that will be updated periodically can be found at www.norseinstitute.org.

Management

The general management of SE has been recently and extensively reviewed elsewhere.^1,3,12 With regard to NORSE in particular, the body of literature is small, but findings are suggestive of the importance of early immunotherapy, even in patients without clearly identified antibodies.2 Li et al describe successful cessation of seizures by using plasma exchange in a small number of NORSE cases refractory to multiple anticonvulsants and general anesthetics.¹⁰ Khawaja et al reported improved outcomes in patients treated with immune therapies (intravenous steroids, immune globulins, plasmapheresis, or a combination).⁹ It is possible that patients with NORSE that remain cryptogenic have an underlying occult auto-immune or other immunologic condition (not yet defined); in addition, it could be that inflammatory mechanisms are occurring at the seizure focus due to ongoing seizures, creating further epileptogenicity.^12,13 Until further evidence accumulates, we recommend fairly early use of immune therapies in most or all cases of NORSE.

Outcomes

Most NORSE cases progress to super-refractory status epilepticus (SRSE), meaning persistent seizures after >24 hours of treatment, usually including anesthetics. SRSE has a mortality rate of 35% and high morbidity—in part due to the systemic effects of RSE, and in part due to prolonged ICU stays, anti-seizure and anesthetic medications.^1,12,13 In one study, all patients with prolonged SRSE developed measurable brain atrophy, with atrophy more notable in younger patients with prolonged hospitalization and longer duration of anesthetic treatment.¹⁴

However, not all patients with NORSE have a poor outcome, as demonstrated in our case. In 2013, Kilbride et al reviewed the outcomes of 63 patients in prolonged SRSE (>7 days of RSE) of any etiology.⁸ Of the 63 patients included, two-thirds survived to discharge. Of the survivors, 22% achieved good recovery (modified Rankin score 0-3) in follow-up, outcomes ranging from no disability to moderate disability. In the recent Gaspard multicenter review of patients with NORSE, 62% of the 130 patients had severe disability at hospital discharge (mRS score of 4-6), but many patients improved on longer-term follow up. Among the surviving patients, 72% had a good outcome at a median of 9 months, and 41% (26/63) had no significant disability (mRS 0-1).² The 2016 retrospective study by Hocker et al showed no correlation of development of cerebral atrophy with functional outcome¹⁴; thus, cerebral atrophy should not be used as a prognosticating factor for functional recovery. While NORSE has significant morbidity and mortality, good outcome remains possible, as shown in our case, where despite 1 month of iatrogenic coma, 2 months in the ICU, and many medical complications, he returned to baseline cognitively. We believe good outcome remains possible in many (but certainly not all) patients, especially with early recognition, aggressive treatment of seizures, and probably with use of immune therapy regardless of diagnostic testing results, at least while we await further and better investigations.

Conclusion

NORSE patients are previously healthy patients who present in refractory status epilepticus with unknown etiology despite appropriate initial investigation, including imaging and lumbar puncture. There is increasing evidence that a large proportion of these cases have a component of autoimmune encephalitis. Increased awareness of NORSE is imperative for determining the prevalence, etiologies, and best treatments for NORSE patients. There are ongoing prospective, multicenter studies investigating the role of the immune system, infections, and genetics, and tracking treatments and outcomes.

For further information or to help with further efforts to increase awareness and research into NORSE:

Websites:

• http://www.norseinstitute.org/ [primary website dedicated to NORSE]
• http://www.epilepsy.com/article/2015/10/norse-new-onset-refractory-status-epilepticus  [summary and podcast]
• https://rarediseases.org/rare-diseases/new-onset-refractory-status-epilepticus-norse/ [brief summary of NORSE]

Suggested reviews/key papers:

• Status epilepticus treatment: refs 1, 3 and 12.
• Autoimmune epilepsy and NORSE: refs 2 and 15.

1. Grover EH, Nazzal Y, Hirsch LJ. Treatment of Convulsive Status Epilepticus. Curr Treat Options Neurol. 2016;18(3):11.

2. Gaspard N, Foreman BP, Alvarez V, Cabrera Kang C et al. New-onset Refractory Status Epilepticus: Etiology, Clinical Features, and Outcome. Neurology. 2015;85(18):1604-13.

3. Hocker SE. Status Epilepticus. Continuum (Minneap Minn). 2015 Oct;21(5):1362-83.

4. Trinka E, Cock H, Hesdorffer D, Rossetti AO, Scheffer IE, Shinnar S, Shorvon, S, Lowenstein DH. A definition and classification of status epilepticus—Report of the ILAE Task Force on Classification of Status Epilepticus. Epilepsia. 2015;56(10):1515-23

5. Van Baalen A, Vezzani A, Hausler M, Kluger G. Febrile Infection-Related Epilepsy Syndrome: Clinical Review and Hypotheses of Epileptogenesis. Neuropediatrics. 2016[Epub ahead of print]

6. Betjemann JP, Josephson SA, Lowenstein DH, Burke JF. Trends in Status Epilepticus-Related Hospitalizations and Mortality: Redefined in US Practice Over Time. JAMA Neurol. 2015;72(6):650-5.

7. Wilder-Smith EPV, Lim ECH, Teoh HL, Sharma VK, Tan JJH, Chan BPL, et al. The NORSE (new-onset refractory status epilepticus) syndrome: defining a disease entity. Ann Acad Med Singap. 2005 ;34(7):417-20.

8. Kilbride RD, Reynolds AS, Szaflarski JP, Hirsch LJ. Clinical outcomes following prolonged refractory status epilepticus (PRSE). Neurocrit Care. 2013;18(3):374-85.8.

9. Khawaja AM, DeWolfe JL, Miller DW, Szaflarski JP. New-onset refractory status epilepticus (NORSE) – The potential role for immunotherapy. Epilepsy Behav. 2015; 47:17-23.

10. Li J, Saldivar C, Maganti RK. Plasma Exchange in Cryptogenic New Onset Refractory Status Epilepticus. Seizure. 2013;22(1):70-3.

11. Barry E, Hauser WA. Pleocytosis after status epilepticus. Arch Neurol. 1994 Feb;51(2):190-3.

12. Trinka E, Brigo F, Shorvon S. Recent Advances in Status Epilepticus. Curr Opin Neurol. 2016;29(2):189-98.

13. Hocker, S. Systemic Complications of Status Epilepticus – An Update. Epilepsy Behav. 2015;49:83-7.

14. Hocker S, Nagarajan E, Rabinstein AA, Hanson D, Britton W. Progressive Brain Atrophy in Super-refractory Status Epilepticus. JAMA Neurol. 2016.

.15. Gaspard N. Autoimmune Epilepsy. Continuum (Minneap Minn). 2016;22(1 Epilepsy):227-45.

Electronic medical records have the potential to improve quality of care among patients with epilepsy. With that goal in mind, Jaishree Narayanan et al have created a digital toolkit that allows providers to capture structured clinical data at the point of care. The software facilitates writing notes and inputting test scores in several domains, including Generalized Anxiety Disorder-7, Neurological Disorders Depression Inventory for Epilepsy, the Montreal Cognitive Assessment/Short Test of Mental Status, and the Medical Research Council Prognostic Index.

Narayanan J, Dobrin S, Choi  J, et al. Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy. Epilepsia. 2017;58(1):68-76.

Electronic medical records have the potential to improve quality of care among patients with epilepsy. With that goal in mind, Jaishree Narayanan et al have created a digital toolkit that allows providers to capture structured clinical data at the point of care. The software facilitates writing notes and inputting test scores in several domains, including Generalized Anxiety Disorder-7, Neurological Disorders Depression Inventory for Epilepsy, the Montreal Cognitive Assessment/Short Test of Mental Status, and the Medical Research Council Prognostic Index.

Narayanan J, Dobrin S, Choi  J, et al. Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy. Epilepsia. 2017;58(1):68-76.

Electronic medical records have the potential to improve quality of care among patients with epilepsy. With that goal in mind, Jaishree Narayanan et al have created a digital toolkit that allows providers to capture structured clinical data at the point of care. The software facilitates writing notes and inputting test scores in several domains, including Generalized Anxiety Disorder-7, Neurological Disorders Depression Inventory for Epilepsy, the Montreal Cognitive Assessment/Short Test of Mental Status, and the Medical Research Council Prognostic Index.

Narayanan J, Dobrin S, Choi  J, et al. Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy. Epilepsia. 2017;58(1):68-76.

To determine the value of pulse oximetry in detecting seizures, Goldenholz et al evaluated 193 seizures among 45 patients who were being monitored with video EEG, SpO₂, and EKG. Their analysis found that SpO₂ thresholds of 80%-86% were able to detect 63%-73% of generalized convulsions and 20%-28% of focal seizures. While the researchers concluded that continuous SpO₂ monitoring requires a tradeoff between false alarms and accurate detection of seizures, they believe an alarm setting of 86% is likely to be worthwhile in patients who experience fewer false alarms and a setting of 80% would be better for those who have more false alarms.

Goldenholz DM, Kuhn A, Austermuehle A, et al. Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy. Epilepsia. 2017;58(1):77-84.

To determine the value of pulse oximetry in detecting seizures, Goldenholz et al evaluated 193 seizures among 45 patients who were being monitored with video EEG, SpO₂, and EKG. Their analysis found that SpO₂ thresholds of 80%-86% were able to detect 63%-73% of generalized convulsions and 20%-28% of focal seizures. While the researchers concluded that continuous SpO₂ monitoring requires a tradeoff between false alarms and accurate detection of seizures, they believe an alarm setting of 86% is likely to be worthwhile in patients who experience fewer false alarms and a setting of 80% would be better for those who have more false alarms.

Goldenholz DM, Kuhn A, Austermuehle A, et al. Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy. Epilepsia. 2017;58(1):77-84.

To determine the value of pulse oximetry in detecting seizures, Goldenholz et al evaluated 193 seizures among 45 patients who were being monitored with video EEG, SpO₂, and EKG. Their analysis found that SpO₂ thresholds of 80%-86% were able to detect 63%-73% of generalized convulsions and 20%-28% of focal seizures. While the researchers concluded that continuous SpO₂ monitoring requires a tradeoff between false alarms and accurate detection of seizures, they believe an alarm setting of 86% is likely to be worthwhile in patients who experience fewer false alarms and a setting of 80% would be better for those who have more false alarms.

Goldenholz DM, Kuhn A, Austermuehle A, et al. Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy. Epilepsia. 2017;58(1):77-84.

WAILEA, HAWAII – More than half of the patients with seborrheic keratosis had more than 16 lesions, in a prospective study of 406 adults at 10 dermatology practices.

The results were presented in a poster at the Hawaii Dermatology Seminar provided by Global Academy for Medical Education/Skin Disease Education Foundation.

The study population was 62% female, with an average age of 58 years. Patients were seen at 10 community dermatology clinics across the United States, where they completed questionnaires about their seborrheic keratosis.

Of those surveyed, 54% had more than 16 lesions, while 18% had 5 or fewer; another 18% had 6-10 lesions, and 10% had 11-15 lesions. The mean number of lesions by location ranged from 3.6 on the face to 26 on the upper body. Approximately 31% of patients reported picking at their lesions to cause them to fall off.

Most of those surveyed said they were either interested or somewhat interested in an office-based treatment. Female patients and patients with lesions on the head and neck were more likely to report interest in an office-based treatment.

The findings were limited by the inclusion only of patients who presented to private dermatology clinics and therefore may not generalize to the overall population of seborrheic keratosis patients.

The author of the study was James Del Rosso, MD, of Touro University, Nevada.

The study was supported by Aclaris Therapeutics. Dr. Del Rosso has received consulting fees from Aclaris.

SDEF and this news organization are owned by the same parent company.

WAILEA, HAWAII – More than half of the patients with seborrheic keratosis had more than 16 lesions, in a prospective study of 406 adults at 10 dermatology practices.

The results were presented in a poster at the Hawaii Dermatology Seminar provided by Global Academy for Medical Education/Skin Disease Education Foundation.

The study population was 62% female, with an average age of 58 years. Patients were seen at 10 community dermatology clinics across the United States, where they completed questionnaires about their seborrheic keratosis.

Of those surveyed, 54% had more than 16 lesions, while 18% had 5 or fewer; another 18% had 6-10 lesions, and 10% had 11-15 lesions. The mean number of lesions by location ranged from 3.6 on the face to 26 on the upper body. Approximately 31% of patients reported picking at their lesions to cause them to fall off.

Most of those surveyed said they were either interested or somewhat interested in an office-based treatment. Female patients and patients with lesions on the head and neck were more likely to report interest in an office-based treatment.

The findings were limited by the inclusion only of patients who presented to private dermatology clinics and therefore may not generalize to the overall population of seborrheic keratosis patients.

The author of the study was James Del Rosso, MD, of Touro University, Nevada.

The study was supported by Aclaris Therapeutics. Dr. Del Rosso has received consulting fees from Aclaris.

SDEF and this news organization are owned by the same parent company.

WAILEA, HAWAII – More than half of the patients with seborrheic keratosis had more than 16 lesions, in a prospective study of 406 adults at 10 dermatology practices.

The results were presented in a poster at the Hawaii Dermatology Seminar provided by Global Academy for Medical Education/Skin Disease Education Foundation.

The study population was 62% female, with an average age of 58 years. Patients were seen at 10 community dermatology clinics across the United States, where they completed questionnaires about their seborrheic keratosis.

Of those surveyed, 54% had more than 16 lesions, while 18% had 5 or fewer; another 18% had 6-10 lesions, and 10% had 11-15 lesions. The mean number of lesions by location ranged from 3.6 on the face to 26 on the upper body. Approximately 31% of patients reported picking at their lesions to cause them to fall off.

Most of those surveyed said they were either interested or somewhat interested in an office-based treatment. Female patients and patients with lesions on the head and neck were more likely to report interest in an office-based treatment.

The findings were limited by the inclusion only of patients who presented to private dermatology clinics and therefore may not generalize to the overall population of seborrheic keratosis patients.

The author of the study was James Del Rosso, MD, of Touro University, Nevada.

The study was supported by Aclaris Therapeutics. Dr. Del Rosso has received consulting fees from Aclaris.

SDEF and this news organization are owned by the same parent company.

Geriatric syndromes are multifactorial health conditions that affect older people and include dementia, delirium, impaired mobility, falls, frailty, poor nutrition, weight loss, incontinence, and difficulties with activities of daily living.¹ These syndromes are highly prevalent among older patients admitted to acute-care hospitals^2,3 and often add complexity to the clinical status of hospitalized older adults with multiple comorbid conditions.⁴ In the English National Health Service (NHS), the proportion of older people admitted to acute-care hospitals with geriatric syndromes has increased dramatically.⁵

The recognition and management of geriatric syndromes by hospitalists requires specific knowledge and skill sets.⁶ However, geriatricians are a scarce resource in many settings, including the NHS. A challenge for service evaluation and research is the generally poor capture of information about geriatric syndromes compared to specific comorbidities in discharge summaries and hospital coding.⁷ Steps are being taken in the NHS to address this issue, and in 2013 our center started the routine collection of data on clinical frailty, history of dementia (HoD) and acute confusional state (ACS) in all patients 75 years or older admitted nonelectively to the hospital.⁸The presence of geriatric syndromes in older inpatients is an important driver of adverse outcomes, particularly length of stay (LOS) and admission to institutional care.⁹ However, acute illness severity (AIS) is also an important determinant of poor outcomes in the inpatient population and may drive disproportionate changes in health status in the most vulnerable.¹⁰ Research studies with geriatric syndromes in acute settings have not been able to simultaneously consider AIS.¹¹ In addition, comorbidity is not always associated with an increased number of geriatric syndromes.¹²

We aimed to study the association of geriatric syndromes such as frailty, HoD and ACS that are measured in routine clinical care with hospital outcomes (prolonged LOS, inpatient mortality, delayed discharge, institutionalization, and 30-day readmission), while controlling for demographics (age, gender), AIS, comorbidity, and discharging specialty (general medicine, geriatric medicine, surgery).

Study Design and Setting

This retrospective observational study was conducted in a large tertiary university hospital in England with 1000 acute beds receiving more than 102,000 visits to the emergency department (ED) and admitting over 73,000 patients per year; among the latter, more than 12,000 are 75 years and older.

Sample

We analyzed all first nonelective inpatient episodes (ie, from ED admission to discharge) of people 75 years and older (all specialties) between the October 26, 2014 and the October 26, 2015. Data were obtained via the hospital’s information systems following the implementation of a new electronic patient record on October 26, 2014.

Patients’ Characteristics

The following anonymized variables were extracted:

• Age and gender
• AIS information is routinely collected in our ED using a Modified Early Warning Score (ED-MEWS). The components and scoring of ED-MEWS are shown in Table 1. Where more than 1 ED-MEWS was collected, the highest was used in the analyses.
• Charlson Comorbidity Index (CCI, without age adjustment).¹³ The CCI is based on the discharge diagnoses, as coded according to WHO International Classification of Diseases, v 10 (ICD-10). The CCI was calculated retrospectively and would have not been available to clinicians early during the patients’ admission.
• Clinical Frailty Scale (CFS). The scoring of CFS is based on a global assessment of patients’ comorbidity symptoms, and their level of physical activity and dependency on activities of daily living, estimated to reflect the status immediately before the onset of the acute illness leading to hospitalization. The possible scores are: 1 (very fit), 2 (well), 3 (managing well), 4 (vulnerable), 5 (mildly frail), 6 (moderately frail), 7 (severely frail), 8 (very severely frail), and 9 (terminally ill) ().¹⁴ The use of the CFS in admissions of people 75 years and older was introduced in our center in 2013 under a local Commissioning for Quality and Innovation (CQUIN) scheme.⁸ The CQUIN required that all patients 75 years and older admitted to the hospital, via the ED, be screened for frailty using the CFS within 72 hours of admission. The admitting doctor usually scores the CFS on the electronic admission record, but it can also be completed by ED nurses or by nursing or therapy staff from the trust-wide Specialist Advice for the Frail Elderly team. Training on CFS scoring is provided to staff at a hiring orientation and at regular educational meetings. Permission to use CFS for clinical purposes was obtained from the principal investigator at Geriatric Medicine Research, Dalhousie University, Halifax, Canada.
• Cognitive variables were collected early during the admission in patients 75 years and older, thanks to a parallel local CQUIN scheme. The cognitive CQUIN variables are screening variables, not gold standard. The admission clerking is designed to clinically classify patients within 72 hours of admission into the following 3 mutually exclusive categories:

○ Known HoD (in the database: no = 0; yes = 1)

○ ACS, without HoD (in the database: no = 0; yes = 1)

○ Neither HoD nor ACS

• The cognitive CQUIN assessment does not intend to diagnose dementia in those who are not known to have it, but tries to separate the dementias that general practitioners (GPs) know from hospital-identified acute cognitive concerns that GPs may need to assess or investigate after discharge. The latter may include delirium and/or undiagnosed dementia.
• In our routine hospital practice, the initial cognitive assessment is performed by a clinician in the following fashion: if the patient is known to have dementia (ie, based on clinical history and/or chart review), the clinician selects the “known history of dementia” option in the admission navigator, and no further cognitive screening is conducted. If the patient has no known dementia, the clinician administers the 4-item Abbreviated Mental Test (AMT4): (1) age, (2) date of birth, (3) place, and (4) year, with impaired cognition indicated by an AMT4 of less than 4 and triggering the selection of “ACS without known HoD” option. If the AMT4 is normal, the clinician selects the “neither HoD nor ACS” option.
• Due to the service evaluation nature of our work, these measures could not be assessed for reliability within the electronic medical records system (eg, regarding sensitivity and specificity against a gold standard or inter-rater reliability).
• Discharged from geriatric medicine (no = 0; yes = 1). Every year, our hospital admits over 12,000 patients 75 years and older, of which 25% are managed by the Department of Medicine for the Elderly (DME). The DME specialist bed base consists of 5 core wards, which specialize in ward-based comprehensive geriatric assessment (CGA) and are supported by dedicated nursing, physiotherapy, occupational therapy, and social work teams, as well as by readily available input from speech and language therapy, clinical nutrition, psychogeriatric, pharmacy and palliative care teams. Formal multidisciplinary team meetings occur at least twice weekly. A sixth specialist DME ward with a more acute perspective has been operational for 7 years; this ward was renamed the Frailty and Acute Medicine for the Elderly (FAME) ward in 2014 and has daily multidisciplinary team meetings. Although admission to FAME is through the ED, admission to core DME wards can occur from FAME (ie, within-DME transfer), via the ED, or from other inpatient specialty areas if older patients are perceived to be in high need of CGA after screening by the Specialist Advice for the Frail Elderly team. An audit in our center showed that up to 20% of patients discharged by DME were not initially admitted by DME, underscoring the significant role of core specialist DME wards in absorbing complex cases, especially from the general medical wards.⁸
• Discharged from general medicine (no = 0; yes = 1). In our setting, virtually all patients discharged by general medicine were first admitted by general medicine.⁸
• Discharged by a surgical specialty (no = 0; yes = 1)

Hospital Outcomes

The following anonymized variables were identified:

• LOS (days). Prolonged LOS was defined as 10 or more days (no = 0; yes = 1)
• Inpatient mortality (no = 0; yes = 1)
• Delayed discharge (no = 0; yes = 1). This was defined as the total LOS being at least 1 day longer than the LOS up to the last recorded clinically fit date. This date is used in NHS hospitals to indicate that the acute medical episode has finished and discharge-planning arrangements (often via social care providers) can commence.
• Institutionalization (no = 0; yes = 1). This was defined as the discharge destination being a care home, when a care home was not the usual place of residence.
• 30-day readmission (no = 0; yes = 1)

Statistical Analyses

Anonymized data were analyzed with IBM SPSS Statistics (v 22, Armonk, New York) software. Descriptive statistics were given as count (with percentage) or mean (with standard deviation.

To avoid potential problems with multicollinearity in the multivariate regression models, the correlations among the predictor variables were checked using a correlation matrix of 2-sided Spearman’s rho correlation coefficients. Correlations of 0.50 or more were considered large.^15,16

Because all outcomes in the study were binary, multivariate binary logistic regression models were computed. In these models, the odds ratio (OR) reflects the effect size of each predictor; 95% confidence intervals (CI) were requested for each OR. Predictors with P < 0.01 were considered as statistically significant. The classification performance of each logistic regression model was assessed calculating its area under the curve (AUC). 

Sensitivity analyses were conducted after imputing missing data (SPSS multiple imputation procedure) and after fitting interaction terms between geriatric syndromes and discharge by geriatric medicine.

The initial database contained 12,282 nonelective admission and discharge episodes (all specialties) of patients 75 years and older between October 26, 2014 and October 26, 2015. Among those, 8202 (66.8%) were first episodes. Table 2 shows the sample descriptives, and Table 3 shows the breakdown of geriatric syndromes (single and multiple) in the total sample (n = 8282), including missing frailty data.

In the correlation matrix of 2-sided Spearman’s rho correlation coefficients, no correlations with large-effect size were found to suggest issues with multicollinearity; the largest correlation coefficients were between age and CFS (rho = 0.35), HoD and CFS (rho = 0.32), and CCI and CFS (rho = 0.26).

The results of the multivariate regression models are shown in Table 4. The best performing models were the ones for inpatient mortality (AUC = 0.80), followed by institutionalization (AUC = 0.76), and prolonged LOS (AUC = 0.71). After full adjustment, clinical frailty was an independent predictor of prolonged LOS, inpatient mortality, delayed discharge, and institutionalization. HoD was an independent predictor of prolonged LOS, delayed discharge, and institutionalization; and ACS was an independent predictor of prolonged LOS, delayed discharge, institutionalization, and 30-day readmission (Table 4). Results did not significantly change in sensitivity analyses conducted after multiple imputation of missing data and after inclusion of interaction terms (see Supplemental Table 1 and Supplemental Table 2).

Our aim was to study the association of geriatric syndromes (measured in routine clinical care) with hospital outcomes. We found that geriatric syndromes such as clinical frailty, HoD, and ACS were strong independent predictors. Concerning prolonged LOS, delayed discharge, and institutionalization, geriatric syndromes had ORs that were greater than those of traditionally measured factors such as demographics, comorbidity and acute illness severity. Our findings add to the body of knowledge in this area because we accounted for the latter effects. Our experience shows that metrics on geriatric syndromes can be successfully collected in the routine hospital setting and add clear value to the prediction of operational outcomes. This may encourage other hospitals to do the same.

Our findings are consistent with suggestions that accounting for chronic conditions alone may be less informative than also accounting for the co-occurrence of geriatric syndromes.¹⁷ The focus of CFS is on the pre-admission level of physical activity and dependency on activities of daily living, and poorer scores may confer vulnerability to adverse outcomes due to reduced physiological reserve and ability to withstand acute stressors.¹⁸ Other studies have also found CFS to be a good predictor of inpatient outcomes,^19-22 and it has been recommended as a possible means to identify vulnerable older adults in acute-care settings.²³

Acknowledgments

The authors wish to thank all members of the acute teams in our hospital, without which this initiative would have not been possible. Licensed access to the NHS Foundation Trust’s information systems is also gratefully acknowledged.

Disclosure

The authors report no financial conflicts of interest.

Geriatric syndromes are multifactorial health conditions that affect older people and include dementia, delirium, impaired mobility, falls, frailty, poor nutrition, weight loss, incontinence, and difficulties with activities of daily living.¹ These syndromes are highly prevalent among older patients admitted to acute-care hospitals^2,3 and often add complexity to the clinical status of hospitalized older adults with multiple comorbid conditions.⁴ In the English National Health Service (NHS), the proportion of older people admitted to acute-care hospitals with geriatric syndromes has increased dramatically.⁵

The recognition and management of geriatric syndromes by hospitalists requires specific knowledge and skill sets.⁶ However, geriatricians are a scarce resource in many settings, including the NHS. A challenge for service evaluation and research is the generally poor capture of information about geriatric syndromes compared to specific comorbidities in discharge summaries and hospital coding.⁷ Steps are being taken in the NHS to address this issue, and in 2013 our center started the routine collection of data on clinical frailty, history of dementia (HoD) and acute confusional state (ACS) in all patients 75 years or older admitted nonelectively to the hospital.⁸The presence of geriatric syndromes in older inpatients is an important driver of adverse outcomes, particularly length of stay (LOS) and admission to institutional care.⁹ However, acute illness severity (AIS) is also an important determinant of poor outcomes in the inpatient population and may drive disproportionate changes in health status in the most vulnerable.¹⁰ Research studies with geriatric syndromes in acute settings have not been able to simultaneously consider AIS.¹¹ In addition, comorbidity is not always associated with an increased number of geriatric syndromes.¹²

We aimed to study the association of geriatric syndromes such as frailty, HoD and ACS that are measured in routine clinical care with hospital outcomes (prolonged LOS, inpatient mortality, delayed discharge, institutionalization, and 30-day readmission), while controlling for demographics (age, gender), AIS, comorbidity, and discharging specialty (general medicine, geriatric medicine, surgery).

Study Design and Setting

This retrospective observational study was conducted in a large tertiary university hospital in England with 1000 acute beds receiving more than 102,000 visits to the emergency department (ED) and admitting over 73,000 patients per year; among the latter, more than 12,000 are 75 years and older.

Sample

We analyzed all first nonelective inpatient episodes (ie, from ED admission to discharge) of people 75 years and older (all specialties) between the October 26, 2014 and the October 26, 2015. Data were obtained via the hospital’s information systems following the implementation of a new electronic patient record on October 26, 2014.

Patients’ Characteristics

The following anonymized variables were extracted:

• Age and gender
• AIS information is routinely collected in our ED using a Modified Early Warning Score (ED-MEWS). The components and scoring of ED-MEWS are shown in Table 1. Where more than 1 ED-MEWS was collected, the highest was used in the analyses.
• Charlson Comorbidity Index (CCI, without age adjustment).¹³ The CCI is based on the discharge diagnoses, as coded according to WHO International Classification of Diseases, v 10 (ICD-10). The CCI was calculated retrospectively and would have not been available to clinicians early during the patients’ admission.
• Clinical Frailty Scale (CFS). The scoring of CFS is based on a global assessment of patients’ comorbidity symptoms, and their level of physical activity and dependency on activities of daily living, estimated to reflect the status immediately before the onset of the acute illness leading to hospitalization. The possible scores are: 1 (very fit), 2 (well), 3 (managing well), 4 (vulnerable), 5 (mildly frail), 6 (moderately frail), 7 (severely frail), 8 (very severely frail), and 9 (terminally ill) ().¹⁴ The use of the CFS in admissions of people 75 years and older was introduced in our center in 2013 under a local Commissioning for Quality and Innovation (CQUIN) scheme.⁸ The CQUIN required that all patients 75 years and older admitted to the hospital, via the ED, be screened for frailty using the CFS within 72 hours of admission. The admitting doctor usually scores the CFS on the electronic admission record, but it can also be completed by ED nurses or by nursing or therapy staff from the trust-wide Specialist Advice for the Frail Elderly team. Training on CFS scoring is provided to staff at a hiring orientation and at regular educational meetings. Permission to use CFS for clinical purposes was obtained from the principal investigator at Geriatric Medicine Research, Dalhousie University, Halifax, Canada.
• Cognitive variables were collected early during the admission in patients 75 years and older, thanks to a parallel local CQUIN scheme. The cognitive CQUIN variables are screening variables, not gold standard. The admission clerking is designed to clinically classify patients within 72 hours of admission into the following 3 mutually exclusive categories:

○ Known HoD (in the database: no = 0; yes = 1)

○ ACS, without HoD (in the database: no = 0; yes = 1)

○ Neither HoD nor ACS

• The cognitive CQUIN assessment does not intend to diagnose dementia in those who are not known to have it, but tries to separate the dementias that general practitioners (GPs) know from hospital-identified acute cognitive concerns that GPs may need to assess or investigate after discharge. The latter may include delirium and/or undiagnosed dementia.
• In our routine hospital practice, the initial cognitive assessment is performed by a clinician in the following fashion: if the patient is known to have dementia (ie, based on clinical history and/or chart review), the clinician selects the “known history of dementia” option in the admission navigator, and no further cognitive screening is conducted. If the patient has no known dementia, the clinician administers the 4-item Abbreviated Mental Test (AMT4): (1) age, (2) date of birth, (3) place, and (4) year, with impaired cognition indicated by an AMT4 of less than 4 and triggering the selection of “ACS without known HoD” option. If the AMT4 is normal, the clinician selects the “neither HoD nor ACS” option.
• Due to the service evaluation nature of our work, these measures could not be assessed for reliability within the electronic medical records system (eg, regarding sensitivity and specificity against a gold standard or inter-rater reliability).
• Discharged from geriatric medicine (no = 0; yes = 1). Every year, our hospital admits over 12,000 patients 75 years and older, of which 25% are managed by the Department of Medicine for the Elderly (DME). The DME specialist bed base consists of 5 core wards, which specialize in ward-based comprehensive geriatric assessment (CGA) and are supported by dedicated nursing, physiotherapy, occupational therapy, and social work teams, as well as by readily available input from speech and language therapy, clinical nutrition, psychogeriatric, pharmacy and palliative care teams. Formal multidisciplinary team meetings occur at least twice weekly. A sixth specialist DME ward with a more acute perspective has been operational for 7 years; this ward was renamed the Frailty and Acute Medicine for the Elderly (FAME) ward in 2014 and has daily multidisciplinary team meetings. Although admission to FAME is through the ED, admission to core DME wards can occur from FAME (ie, within-DME transfer), via the ED, or from other inpatient specialty areas if older patients are perceived to be in high need of CGA after screening by the Specialist Advice for the Frail Elderly team. An audit in our center showed that up to 20% of patients discharged by DME were not initially admitted by DME, underscoring the significant role of core specialist DME wards in absorbing complex cases, especially from the general medical wards.⁸
• Discharged from general medicine (no = 0; yes = 1). In our setting, virtually all patients discharged by general medicine were first admitted by general medicine.⁸
• Discharged by a surgical specialty (no = 0; yes = 1)

Hospital Outcomes

The following anonymized variables were identified:

• LOS (days). Prolonged LOS was defined as 10 or more days (no = 0; yes = 1)
• Inpatient mortality (no = 0; yes = 1)
• Delayed discharge (no = 0; yes = 1). This was defined as the total LOS being at least 1 day longer than the LOS up to the last recorded clinically fit date. This date is used in NHS hospitals to indicate that the acute medical episode has finished and discharge-planning arrangements (often via social care providers) can commence.
• Institutionalization (no = 0; yes = 1). This was defined as the discharge destination being a care home, when a care home was not the usual place of residence.
• 30-day readmission (no = 0; yes = 1)

Statistical Analyses

Anonymized data were analyzed with IBM SPSS Statistics (v 22, Armonk, New York) software. Descriptive statistics were given as count (with percentage) or mean (with standard deviation.

To avoid potential problems with multicollinearity in the multivariate regression models, the correlations among the predictor variables were checked using a correlation matrix of 2-sided Spearman’s rho correlation coefficients. Correlations of 0.50 or more were considered large.^15,16

Because all outcomes in the study were binary, multivariate binary logistic regression models were computed. In these models, the odds ratio (OR) reflects the effect size of each predictor; 95% confidence intervals (CI) were requested for each OR. Predictors with P < 0.01 were considered as statistically significant. The classification performance of each logistic regression model was assessed calculating its area under the curve (AUC). 

Sensitivity analyses were conducted after imputing missing data (SPSS multiple imputation procedure) and after fitting interaction terms between geriatric syndromes and discharge by geriatric medicine.

The initial database contained 12,282 nonelective admission and discharge episodes (all specialties) of patients 75 years and older between October 26, 2014 and October 26, 2015. Among those, 8202 (66.8%) were first episodes. Table 2 shows the sample descriptives, and Table 3 shows the breakdown of geriatric syndromes (single and multiple) in the total sample (n = 8282), including missing frailty data.

In the correlation matrix of 2-sided Spearman’s rho correlation coefficients, no correlations with large-effect size were found to suggest issues with multicollinearity; the largest correlation coefficients were between age and CFS (rho = 0.35), HoD and CFS (rho = 0.32), and CCI and CFS (rho = 0.26).

The results of the multivariate regression models are shown in Table 4. The best performing models were the ones for inpatient mortality (AUC = 0.80), followed by institutionalization (AUC = 0.76), and prolonged LOS (AUC = 0.71). After full adjustment, clinical frailty was an independent predictor of prolonged LOS, inpatient mortality, delayed discharge, and institutionalization. HoD was an independent predictor of prolonged LOS, delayed discharge, and institutionalization; and ACS was an independent predictor of prolonged LOS, delayed discharge, institutionalization, and 30-day readmission (Table 4). Results did not significantly change in sensitivity analyses conducted after multiple imputation of missing data and after inclusion of interaction terms (see Supplemental Table 1 and Supplemental Table 2).

Our aim was to study the association of geriatric syndromes (measured in routine clinical care) with hospital outcomes. We found that geriatric syndromes such as clinical frailty, HoD, and ACS were strong independent predictors. Concerning prolonged LOS, delayed discharge, and institutionalization, geriatric syndromes had ORs that were greater than those of traditionally measured factors such as demographics, comorbidity and acute illness severity. Our findings add to the body of knowledge in this area because we accounted for the latter effects. Our experience shows that metrics on geriatric syndromes can be successfully collected in the routine hospital setting and add clear value to the prediction of operational outcomes. This may encourage other hospitals to do the same.

Our findings are consistent with suggestions that accounting for chronic conditions alone may be less informative than also accounting for the co-occurrence of geriatric syndromes.¹⁷ The focus of CFS is on the pre-admission level of physical activity and dependency on activities of daily living, and poorer scores may confer vulnerability to adverse outcomes due to reduced physiological reserve and ability to withstand acute stressors.¹⁸ Other studies have also found CFS to be a good predictor of inpatient outcomes,^19-22 and it has been recommended as a possible means to identify vulnerable older adults in acute-care settings.²³

Acknowledgments

The authors wish to thank all members of the acute teams in our hospital, without which this initiative would have not been possible. Licensed access to the NHS Foundation Trust’s information systems is also gratefully acknowledged.

Disclosure

The authors report no financial conflicts of interest.

Geriatric syndromes are multifactorial health conditions that affect older people and include dementia, delirium, impaired mobility, falls, frailty, poor nutrition, weight loss, incontinence, and difficulties with activities of daily living.¹ These syndromes are highly prevalent among older patients admitted to acute-care hospitals^2,3 and often add complexity to the clinical status of hospitalized older adults with multiple comorbid conditions.⁴ In the English National Health Service (NHS), the proportion of older people admitted to acute-care hospitals with geriatric syndromes has increased dramatically.⁵

The recognition and management of geriatric syndromes by hospitalists requires specific knowledge and skill sets.⁶ However, geriatricians are a scarce resource in many settings, including the NHS. A challenge for service evaluation and research is the generally poor capture of information about geriatric syndromes compared to specific comorbidities in discharge summaries and hospital coding.⁷ Steps are being taken in the NHS to address this issue, and in 2013 our center started the routine collection of data on clinical frailty, history of dementia (HoD) and acute confusional state (ACS) in all patients 75 years or older admitted nonelectively to the hospital.⁸The presence of geriatric syndromes in older inpatients is an important driver of adverse outcomes, particularly length of stay (LOS) and admission to institutional care.⁹ However, acute illness severity (AIS) is also an important determinant of poor outcomes in the inpatient population and may drive disproportionate changes in health status in the most vulnerable.¹⁰ Research studies with geriatric syndromes in acute settings have not been able to simultaneously consider AIS.¹¹ In addition, comorbidity is not always associated with an increased number of geriatric syndromes.¹²

We aimed to study the association of geriatric syndromes such as frailty, HoD and ACS that are measured in routine clinical care with hospital outcomes (prolonged LOS, inpatient mortality, delayed discharge, institutionalization, and 30-day readmission), while controlling for demographics (age, gender), AIS, comorbidity, and discharging specialty (general medicine, geriatric medicine, surgery).

Study Design and Setting

This retrospective observational study was conducted in a large tertiary university hospital in England with 1000 acute beds receiving more than 102,000 visits to the emergency department (ED) and admitting over 73,000 patients per year; among the latter, more than 12,000 are 75 years and older.

Sample

We analyzed all first nonelective inpatient episodes (ie, from ED admission to discharge) of people 75 years and older (all specialties) between the October 26, 2014 and the October 26, 2015. Data were obtained via the hospital’s information systems following the implementation of a new electronic patient record on October 26, 2014.

Patients’ Characteristics

The following anonymized variables were extracted:

• Age and gender
• AIS information is routinely collected in our ED using a Modified Early Warning Score (ED-MEWS). The components and scoring of ED-MEWS are shown in Table 1. Where more than 1 ED-MEWS was collected, the highest was used in the analyses.
• Charlson Comorbidity Index (CCI, without age adjustment).¹³ The CCI is based on the discharge diagnoses, as coded according to WHO International Classification of Diseases, v 10 (ICD-10). The CCI was calculated retrospectively and would have not been available to clinicians early during the patients’ admission.
• Clinical Frailty Scale (CFS). The scoring of CFS is based on a global assessment of patients’ comorbidity symptoms, and their level of physical activity and dependency on activities of daily living, estimated to reflect the status immediately before the onset of the acute illness leading to hospitalization. The possible scores are: 1 (very fit), 2 (well), 3 (managing well), 4 (vulnerable), 5 (mildly frail), 6 (moderately frail), 7 (severely frail), 8 (very severely frail), and 9 (terminally ill) ().¹⁴ The use of the CFS in admissions of people 75 years and older was introduced in our center in 2013 under a local Commissioning for Quality and Innovation (CQUIN) scheme.⁸ The CQUIN required that all patients 75 years and older admitted to the hospital, via the ED, be screened for frailty using the CFS within 72 hours of admission. The admitting doctor usually scores the CFS on the electronic admission record, but it can also be completed by ED nurses or by nursing or therapy staff from the trust-wide Specialist Advice for the Frail Elderly team. Training on CFS scoring is provided to staff at a hiring orientation and at regular educational meetings. Permission to use CFS for clinical purposes was obtained from the principal investigator at Geriatric Medicine Research, Dalhousie University, Halifax, Canada.
• Cognitive variables were collected early during the admission in patients 75 years and older, thanks to a parallel local CQUIN scheme. The cognitive CQUIN variables are screening variables, not gold standard. The admission clerking is designed to clinically classify patients within 72 hours of admission into the following 3 mutually exclusive categories:

○ Known HoD (in the database: no = 0; yes = 1)

○ ACS, without HoD (in the database: no = 0; yes = 1)

○ Neither HoD nor ACS

• The cognitive CQUIN assessment does not intend to diagnose dementia in those who are not known to have it, but tries to separate the dementias that general practitioners (GPs) know from hospital-identified acute cognitive concerns that GPs may need to assess or investigate after discharge. The latter may include delirium and/or undiagnosed dementia.
• In our routine hospital practice, the initial cognitive assessment is performed by a clinician in the following fashion: if the patient is known to have dementia (ie, based on clinical history and/or chart review), the clinician selects the “known history of dementia” option in the admission navigator, and no further cognitive screening is conducted. If the patient has no known dementia, the clinician administers the 4-item Abbreviated Mental Test (AMT4): (1) age, (2) date of birth, (3) place, and (4) year, with impaired cognition indicated by an AMT4 of less than 4 and triggering the selection of “ACS without known HoD” option. If the AMT4 is normal, the clinician selects the “neither HoD nor ACS” option.
• Due to the service evaluation nature of our work, these measures could not be assessed for reliability within the electronic medical records system (eg, regarding sensitivity and specificity against a gold standard or inter-rater reliability).
• Discharged from geriatric medicine (no = 0; yes = 1). Every year, our hospital admits over 12,000 patients 75 years and older, of which 25% are managed by the Department of Medicine for the Elderly (DME). The DME specialist bed base consists of 5 core wards, which specialize in ward-based comprehensive geriatric assessment (CGA) and are supported by dedicated nursing, physiotherapy, occupational therapy, and social work teams, as well as by readily available input from speech and language therapy, clinical nutrition, psychogeriatric, pharmacy and palliative care teams. Formal multidisciplinary team meetings occur at least twice weekly. A sixth specialist DME ward with a more acute perspective has been operational for 7 years; this ward was renamed the Frailty and Acute Medicine for the Elderly (FAME) ward in 2014 and has daily multidisciplinary team meetings. Although admission to FAME is through the ED, admission to core DME wards can occur from FAME (ie, within-DME transfer), via the ED, or from other inpatient specialty areas if older patients are perceived to be in high need of CGA after screening by the Specialist Advice for the Frail Elderly team. An audit in our center showed that up to 20% of patients discharged by DME were not initially admitted by DME, underscoring the significant role of core specialist DME wards in absorbing complex cases, especially from the general medical wards.⁸
• Discharged from general medicine (no = 0; yes = 1). In our setting, virtually all patients discharged by general medicine were first admitted by general medicine.⁸
• Discharged by a surgical specialty (no = 0; yes = 1)

Hospital Outcomes

The following anonymized variables were identified:

• LOS (days). Prolonged LOS was defined as 10 or more days (no = 0; yes = 1)
• Inpatient mortality (no = 0; yes = 1)
• Delayed discharge (no = 0; yes = 1). This was defined as the total LOS being at least 1 day longer than the LOS up to the last recorded clinically fit date. This date is used in NHS hospitals to indicate that the acute medical episode has finished and discharge-planning arrangements (often via social care providers) can commence.
• Institutionalization (no = 0; yes = 1). This was defined as the discharge destination being a care home, when a care home was not the usual place of residence.
• 30-day readmission (no = 0; yes = 1)

Statistical Analyses

Anonymized data were analyzed with IBM SPSS Statistics (v 22, Armonk, New York) software. Descriptive statistics were given as count (with percentage) or mean (with standard deviation.

To avoid potential problems with multicollinearity in the multivariate regression models, the correlations among the predictor variables were checked using a correlation matrix of 2-sided Spearman’s rho correlation coefficients. Correlations of 0.50 or more were considered large.^15,16

Because all outcomes in the study were binary, multivariate binary logistic regression models were computed. In these models, the odds ratio (OR) reflects the effect size of each predictor; 95% confidence intervals (CI) were requested for each OR. Predictors with P < 0.01 were considered as statistically significant. The classification performance of each logistic regression model was assessed calculating its area under the curve (AUC). 

Sensitivity analyses were conducted after imputing missing data (SPSS multiple imputation procedure) and after fitting interaction terms between geriatric syndromes and discharge by geriatric medicine.

The initial database contained 12,282 nonelective admission and discharge episodes (all specialties) of patients 75 years and older between October 26, 2014 and October 26, 2015. Among those, 8202 (66.8%) were first episodes. Table 2 shows the sample descriptives, and Table 3 shows the breakdown of geriatric syndromes (single and multiple) in the total sample (n = 8282), including missing frailty data.

In the correlation matrix of 2-sided Spearman’s rho correlation coefficients, no correlations with large-effect size were found to suggest issues with multicollinearity; the largest correlation coefficients were between age and CFS (rho = 0.35), HoD and CFS (rho = 0.32), and CCI and CFS (rho = 0.26).

The results of the multivariate regression models are shown in Table 4. The best performing models were the ones for inpatient mortality (AUC = 0.80), followed by institutionalization (AUC = 0.76), and prolonged LOS (AUC = 0.71). After full adjustment, clinical frailty was an independent predictor of prolonged LOS, inpatient mortality, delayed discharge, and institutionalization. HoD was an independent predictor of prolonged LOS, delayed discharge, and institutionalization; and ACS was an independent predictor of prolonged LOS, delayed discharge, institutionalization, and 30-day readmission (Table 4). Results did not significantly change in sensitivity analyses conducted after multiple imputation of missing data and after inclusion of interaction terms (see Supplemental Table 1 and Supplemental Table 2).

Our aim was to study the association of geriatric syndromes (measured in routine clinical care) with hospital outcomes. We found that geriatric syndromes such as clinical frailty, HoD, and ACS were strong independent predictors. Concerning prolonged LOS, delayed discharge, and institutionalization, geriatric syndromes had ORs that were greater than those of traditionally measured factors such as demographics, comorbidity and acute illness severity. Our findings add to the body of knowledge in this area because we accounted for the latter effects. Our experience shows that metrics on geriatric syndromes can be successfully collected in the routine hospital setting and add clear value to the prediction of operational outcomes. This may encourage other hospitals to do the same.

Our findings are consistent with suggestions that accounting for chronic conditions alone may be less informative than also accounting for the co-occurrence of geriatric syndromes.¹⁷ The focus of CFS is on the pre-admission level of physical activity and dependency on activities of daily living, and poorer scores may confer vulnerability to adverse outcomes due to reduced physiological reserve and ability to withstand acute stressors.¹⁸ Other studies have also found CFS to be a good predictor of inpatient outcomes,^19-22 and it has been recommended as a possible means to identify vulnerable older adults in acute-care settings.²³

Acknowledgments

The authors wish to thank all members of the acute teams in our hospital, without which this initiative would have not been possible. Licensed access to the NHS Foundation Trust’s information systems is also gratefully acknowledged.

Disclosure

The authors report no financial conflicts of interest.

Interest in mHealth—the use of mobile communication devices for clinical and public health—has exploded among clinicians and researchers for its potential to efficiently improve patient health. Recent studies have used mHealth’s asynchronous receptive and expressive communication functions in interventions targeted to managing care transitions and hospital readmissions.^1-3 We also recently published on improved readmission risk assessments using postdischarge measures of patient reported outcomes, which could be collected through mobile devices. ⁴ But persistent disparities in access to⁵ and engagement with⁶ smartphones may threaten validity and equity when mHealth strategies do not fully address its own limitations.

Disparities introduced by uneven access to technology are well known, but the rapid, albeit belated, adoption of mobile devices by racial minority groups in the United States has allowed authors of recent thoughtful publications to recast mHealth as itself offering solutions to the disparities’ problem.^7,8 Others have cautioned the emergence of disparities along domains other than race, such as low literacy and limited English proficiency (LEP).⁹ In this paper, we assessed the impact of inadequate reading health literacy (IRHL) and LEP on factors related to access and engagement with mHealth. We conducted our study among urban low-income adults in whom IRHL and LEP are common.

We surveyed patients in a large public safety-net health system serving 132 municipalities, including the city of Chicago, in northeastern Illinois. In 2015, nearly 90% of patients were racial-ethnic minorities with more than one-third insured by Medicaid and another one-third uninsured. We sampled adult inpatients and outpatients separately by nonselectively approaching patients in November 2015 to complete an in-person questionnaire in a 464-bed hospital and in 2 primary-care clinics. All inpatients occupied a nonisolation room in a general medical-surgical ward that had been sampled for data collection for that day in 9-day cycles with 8 other similar units. All outpatients in the clinic waiting areas were approached on consecutive days until a predetermined recruitment target was met. Each participant was surveyed once in his/her preferred language (English or Spanish), was 18 years and older, consented verbally, and received no compensation. Sample size provided 80% power to detect a device ownership rate of 50% in an evenly allocated low literacy population compared to a reference rate of 66% assuming a 2-sided α of 0.05 using the Fisher exact test.

The 18-item questionnaire was informed by constructs addressed in the 2015 Pew Research Center smartphone survey.¹⁰ However, in addition to device ownership, we inquired about device capabilities, service-plan details, service interruptions due to difficulty paying bills in the previous year, home-Internet access, an active e-mail account, and self-assigned demographics. Self-reported reading health literacy,¹¹ more directly measured than e-health literacy, was screened using a parsimonious instrument validated as a dichotomized measure.¹² Instruments in English and Spanish were tested for appropriate and comprehensible word choices and syntax through pilot testing. We inferred LEP among patients preferring to complete the survey in Spanish based on our familiarity with the population. We defined any Internet access as having a mobile data-service plan or having home-Internet access. In addition, we inquired about primary insurance provider and offered Medicaid patients an informational brochure about the federal Lifeline Program (https://www.fcc.gov/lifeline) that subsidizes text-messaging-enabled cellular telephone service for low-income patients. Notably, we assessed engagement by asking about the extent of patients’ interest in “new ways of communicating with your doctor, clinic, or pharmacy using” text, e-mail, or mobile apps with a 5-level response scale ranging from “not at all interested” to “very interested”.

Participant characteristics were confirmed to be similar to the Cook County Health and Hospitals System patient population in 2015 with regards to age, gender, and race/ethnicity. We calculated unadjusted and adjusted odds ratios for IRHL and LEP’s association with each dependent measure of access (to smartphone, Internet, or e-mail) and engagement (using text messaging, e-mail, or mobile apps) controlling for age, gender, primary payer, recruitment location, IRHL, and LEP. Because we oversampled inpatients, we estimated sampling-weight-adjusted proportions and 95% confidence intervals (CI) of the entire CCHHS patient population with access to smartphone, data/text plan, non-prepaid plan, and service interruptions using STATA v13 (StataCorp LP, College Station Texas). The project received a waiver upon review by the local Institutional Review Board.

Participation rate was 65% (302/464). Differences in patients by site are shown in Table 1. IRHL was more frequent and LEP less frequent among hospitalized patients. As shown in Table 2, patients with IRHL were less likely to have any Internet access, to have an active e-mail account, and to be interested in using e-mail for healthcare communications. Patients with LEP were less likely than English speakers to be interested in using mobile apps. Inpatients were less likely than outpatients to be interested in text messaging for healthcare communications.

The estimated proportion (95% CI) of the health system’s patients owning a text-enabled mobile device was 87% (75%-94%) and an Internet-enabled mobile device was 64% (47%-78%). The proportion with no data service interruptions in the previous year was 40% (31%-50%).

In this cross-section of urban low-income adult patients, IRHL and LEP were factors associated with potential disparities introduced by mHealth. Even as access to smartphones becomes ubiquitous, lagging access to Internet and e-mail among low literacy patients, and low levels of technology engagement for healthcare communications among patients with IRHL or LEP, underscore concerns about equity in health systems’ adoption of mHealth strategies. Hospitalized patients were found to have diminished engagement with mHealth independent of IRHL and LEP.

Regarding engagement, significantly fewer patients with IRHL or LEP were interested in using technology for healthcare communications. Our finding suggests that health disparities already associated with these conditions¹³ may not be reduced by mobile device outreach alone and may even be worsened by it. Touch screens, audio-enabled questionnaires, and language translation engines are innovations that may be helpful to mitigate IRHL and LEP, but evidence is scarce. Privacy and security concerns, and lack of experience with technology, may also lower engagement. A contemporaneous study found lower apps’ usage among Latinos, also suggesting that language concordance between apps, their source, and targeted users is important.¹⁴ Low-tech solutions involving mobile telephone or even lower tech in-person communications targeted to the estimated 26% of the US population with low literacy¹⁵ and 20% with LEP¹⁶ may be practical stopgap measures. Even as disparities in access to technology across race-ethnicity are diminishing,¹⁰ equity across poverty levels, low levels of education, cultural norms, and disabilities may be more challenging to overcome. Our assessment indicates that large exclusions of a safety-net population in 2015 are a legitimate concern in communication strategies that rely too heavily on mHealth. These findings underscore the CONSORT-EHEALTH recommendation that investigators report web-based recruitment strategies and data-collection methods comprehensively.¹⁷

Regarding access, our estimates suggest that historical disparities in smartphone ownership are diminishing, but access to Internet capabilities may still be lower among the urban poor compared to the nation as a whole. The Pew Research Center found that 64% of Americans owned a smartphone in 2015 (respondents defined smartphone).¹⁰ In comparison, 87% (95% CI, 75%, 94%) of our study participants owned a text-enabled mobile device and 64% (47%, 78%) owned an Internet-enabled mobile device. However, the 40% (31%, 50%) of our safety-net population with an uninterrupted data plan over the previous year may be lower than the 50% of Americans reporting uninterrupted data plans over their lifetime.¹⁰ The impact of expense-related data plan interruptions is magnified by the 40% of our study population—compared to 15% of Americans—who are dependent on mobile devices for Internet access.¹⁰ The association between Internet connectivity and literacy evokes multiple bidirectional pathways yet to be elucidated. But if mHealth can reduce health disparities, closing the gap in device ownership is only a partial accomplishment, and future work also needs to expand Internet connectivity to allow literacy-enhancing and literacy-naïve technologies to flourish.

This study has limitations. Our study population was a consecutive sample and participation rate was less than 100%. However, we recruited participants into the study the way we may also have approached patients to introduce mHealth options in our clinical settings. Our sampling method proved adequate for our primary goal to explain differences in technology access and engagement using regression analysis. Although our patient population may not directly generalize to many healthcare systems, including other safety-net systems serving regions with variable technology uptake,¹⁸ our findings reflect the capacities and the preferences of the most disadvantaged segments of urban populations. We systematically excluded LEP non-Spanish speakers, but they consisted of less than 5% of inpatients and no outpatients. We did not assess current technology use. Finally, as discussed earlier, access and use of new technologies change rapidly and frequent updates are necessary.

mHealth is a promising tool because it may increase healthcare access, improve care quality, and promote research. All these potential benefits will be obtained with accompanying efforts to reduce healthcare disparities, especially where some technologies themselves are exclusionary.⁹ As research of mHealth methods grows, support for patients with IRHL and LEP are still necessary to simultaneously advance our shared goal for equity.

Disclosures

The authors report no financial conflicts of interest.

Interest in mHealth—the use of mobile communication devices for clinical and public health—has exploded among clinicians and researchers for its potential to efficiently improve patient health. Recent studies have used mHealth’s asynchronous receptive and expressive communication functions in interventions targeted to managing care transitions and hospital readmissions.^1-3 We also recently published on improved readmission risk assessments using postdischarge measures of patient reported outcomes, which could be collected through mobile devices. ⁴ But persistent disparities in access to⁵ and engagement with⁶ smartphones may threaten validity and equity when mHealth strategies do not fully address its own limitations.

Disparities introduced by uneven access to technology are well known, but the rapid, albeit belated, adoption of mobile devices by racial minority groups in the United States has allowed authors of recent thoughtful publications to recast mHealth as itself offering solutions to the disparities’ problem.^7,8 Others have cautioned the emergence of disparities along domains other than race, such as low literacy and limited English proficiency (LEP).⁹ In this paper, we assessed the impact of inadequate reading health literacy (IRHL) and LEP on factors related to access and engagement with mHealth. We conducted our study among urban low-income adults in whom IRHL and LEP are common.

We surveyed patients in a large public safety-net health system serving 132 municipalities, including the city of Chicago, in northeastern Illinois. In 2015, nearly 90% of patients were racial-ethnic minorities with more than one-third insured by Medicaid and another one-third uninsured. We sampled adult inpatients and outpatients separately by nonselectively approaching patients in November 2015 to complete an in-person questionnaire in a 464-bed hospital and in 2 primary-care clinics. All inpatients occupied a nonisolation room in a general medical-surgical ward that had been sampled for data collection for that day in 9-day cycles with 8 other similar units. All outpatients in the clinic waiting areas were approached on consecutive days until a predetermined recruitment target was met. Each participant was surveyed once in his/her preferred language (English or Spanish), was 18 years and older, consented verbally, and received no compensation. Sample size provided 80% power to detect a device ownership rate of 50% in an evenly allocated low literacy population compared to a reference rate of 66% assuming a 2-sided α of 0.05 using the Fisher exact test.

The 18-item questionnaire was informed by constructs addressed in the 2015 Pew Research Center smartphone survey.¹⁰ However, in addition to device ownership, we inquired about device capabilities, service-plan details, service interruptions due to difficulty paying bills in the previous year, home-Internet access, an active e-mail account, and self-assigned demographics. Self-reported reading health literacy,¹¹ more directly measured than e-health literacy, was screened using a parsimonious instrument validated as a dichotomized measure.¹² Instruments in English and Spanish were tested for appropriate and comprehensible word choices and syntax through pilot testing. We inferred LEP among patients preferring to complete the survey in Spanish based on our familiarity with the population. We defined any Internet access as having a mobile data-service plan or having home-Internet access. In addition, we inquired about primary insurance provider and offered Medicaid patients an informational brochure about the federal Lifeline Program (https://www.fcc.gov/lifeline) that subsidizes text-messaging-enabled cellular telephone service for low-income patients. Notably, we assessed engagement by asking about the extent of patients’ interest in “new ways of communicating with your doctor, clinic, or pharmacy using” text, e-mail, or mobile apps with a 5-level response scale ranging from “not at all interested” to “very interested”.

Participant characteristics were confirmed to be similar to the Cook County Health and Hospitals System patient population in 2015 with regards to age, gender, and race/ethnicity. We calculated unadjusted and adjusted odds ratios for IRHL and LEP’s association with each dependent measure of access (to smartphone, Internet, or e-mail) and engagement (using text messaging, e-mail, or mobile apps) controlling for age, gender, primary payer, recruitment location, IRHL, and LEP. Because we oversampled inpatients, we estimated sampling-weight-adjusted proportions and 95% confidence intervals (CI) of the entire CCHHS patient population with access to smartphone, data/text plan, non-prepaid plan, and service interruptions using STATA v13 (StataCorp LP, College Station Texas). The project received a waiver upon review by the local Institutional Review Board.

Participation rate was 65% (302/464). Differences in patients by site are shown in Table 1. IRHL was more frequent and LEP less frequent among hospitalized patients. As shown in Table 2, patients with IRHL were less likely to have any Internet access, to have an active e-mail account, and to be interested in using e-mail for healthcare communications. Patients with LEP were less likely than English speakers to be interested in using mobile apps. Inpatients were less likely than outpatients to be interested in text messaging for healthcare communications.

The estimated proportion (95% CI) of the health system’s patients owning a text-enabled mobile device was 87% (75%-94%) and an Internet-enabled mobile device was 64% (47%-78%). The proportion with no data service interruptions in the previous year was 40% (31%-50%).

In this cross-section of urban low-income adult patients, IRHL and LEP were factors associated with potential disparities introduced by mHealth. Even as access to smartphones becomes ubiquitous, lagging access to Internet and e-mail among low literacy patients, and low levels of technology engagement for healthcare communications among patients with IRHL or LEP, underscore concerns about equity in health systems’ adoption of mHealth strategies. Hospitalized patients were found to have diminished engagement with mHealth independent of IRHL and LEP.

Regarding engagement, significantly fewer patients with IRHL or LEP were interested in using technology for healthcare communications. Our finding suggests that health disparities already associated with these conditions¹³ may not be reduced by mobile device outreach alone and may even be worsened by it. Touch screens, audio-enabled questionnaires, and language translation engines are innovations that may be helpful to mitigate IRHL and LEP, but evidence is scarce. Privacy and security concerns, and lack of experience with technology, may also lower engagement. A contemporaneous study found lower apps’ usage among Latinos, also suggesting that language concordance between apps, their source, and targeted users is important.¹⁴ Low-tech solutions involving mobile telephone or even lower tech in-person communications targeted to the estimated 26% of the US population with low literacy¹⁵ and 20% with LEP¹⁶ may be practical stopgap measures. Even as disparities in access to technology across race-ethnicity are diminishing,¹⁰ equity across poverty levels, low levels of education, cultural norms, and disabilities may be more challenging to overcome. Our assessment indicates that large exclusions of a safety-net population in 2015 are a legitimate concern in communication strategies that rely too heavily on mHealth. These findings underscore the CONSORT-EHEALTH recommendation that investigators report web-based recruitment strategies and data-collection methods comprehensively.¹⁷

Regarding access, our estimates suggest that historical disparities in smartphone ownership are diminishing, but access to Internet capabilities may still be lower among the urban poor compared to the nation as a whole. The Pew Research Center found that 64% of Americans owned a smartphone in 2015 (respondents defined smartphone).¹⁰ In comparison, 87% (95% CI, 75%, 94%) of our study participants owned a text-enabled mobile device and 64% (47%, 78%) owned an Internet-enabled mobile device. However, the 40% (31%, 50%) of our safety-net population with an uninterrupted data plan over the previous year may be lower than the 50% of Americans reporting uninterrupted data plans over their lifetime.¹⁰ The impact of expense-related data plan interruptions is magnified by the 40% of our study population—compared to 15% of Americans—who are dependent on mobile devices for Internet access.¹⁰ The association between Internet connectivity and literacy evokes multiple bidirectional pathways yet to be elucidated. But if mHealth can reduce health disparities, closing the gap in device ownership is only a partial accomplishment, and future work also needs to expand Internet connectivity to allow literacy-enhancing and literacy-naïve technologies to flourish.

This study has limitations. Our study population was a consecutive sample and participation rate was less than 100%. However, we recruited participants into the study the way we may also have approached patients to introduce mHealth options in our clinical settings. Our sampling method proved adequate for our primary goal to explain differences in technology access and engagement using regression analysis. Although our patient population may not directly generalize to many healthcare systems, including other safety-net systems serving regions with variable technology uptake,¹⁸ our findings reflect the capacities and the preferences of the most disadvantaged segments of urban populations. We systematically excluded LEP non-Spanish speakers, but they consisted of less than 5% of inpatients and no outpatients. We did not assess current technology use. Finally, as discussed earlier, access and use of new technologies change rapidly and frequent updates are necessary.

mHealth is a promising tool because it may increase healthcare access, improve care quality, and promote research. All these potential benefits will be obtained with accompanying efforts to reduce healthcare disparities, especially where some technologies themselves are exclusionary.⁹ As research of mHealth methods grows, support for patients with IRHL and LEP are still necessary to simultaneously advance our shared goal for equity.

Disclosures

The authors report no financial conflicts of interest.

Interest in mHealth—the use of mobile communication devices for clinical and public health—has exploded among clinicians and researchers for its potential to efficiently improve patient health. Recent studies have used mHealth’s asynchronous receptive and expressive communication functions in interventions targeted to managing care transitions and hospital readmissions.^1-3 We also recently published on improved readmission risk assessments using postdischarge measures of patient reported outcomes, which could be collected through mobile devices. ⁴ But persistent disparities in access to⁵ and engagement with⁶ smartphones may threaten validity and equity when mHealth strategies do not fully address its own limitations.

Disparities introduced by uneven access to technology are well known, but the rapid, albeit belated, adoption of mobile devices by racial minority groups in the United States has allowed authors of recent thoughtful publications to recast mHealth as itself offering solutions to the disparities’ problem.^7,8 Others have cautioned the emergence of disparities along domains other than race, such as low literacy and limited English proficiency (LEP).⁹ In this paper, we assessed the impact of inadequate reading health literacy (IRHL) and LEP on factors related to access and engagement with mHealth. We conducted our study among urban low-income adults in whom IRHL and LEP are common.

We surveyed patients in a large public safety-net health system serving 132 municipalities, including the city of Chicago, in northeastern Illinois. In 2015, nearly 90% of patients were racial-ethnic minorities with more than one-third insured by Medicaid and another one-third uninsured. We sampled adult inpatients and outpatients separately by nonselectively approaching patients in November 2015 to complete an in-person questionnaire in a 464-bed hospital and in 2 primary-care clinics. All inpatients occupied a nonisolation room in a general medical-surgical ward that had been sampled for data collection for that day in 9-day cycles with 8 other similar units. All outpatients in the clinic waiting areas were approached on consecutive days until a predetermined recruitment target was met. Each participant was surveyed once in his/her preferred language (English or Spanish), was 18 years and older, consented verbally, and received no compensation. Sample size provided 80% power to detect a device ownership rate of 50% in an evenly allocated low literacy population compared to a reference rate of 66% assuming a 2-sided α of 0.05 using the Fisher exact test.

The 18-item questionnaire was informed by constructs addressed in the 2015 Pew Research Center smartphone survey.¹⁰ However, in addition to device ownership, we inquired about device capabilities, service-plan details, service interruptions due to difficulty paying bills in the previous year, home-Internet access, an active e-mail account, and self-assigned demographics. Self-reported reading health literacy,¹¹ more directly measured than e-health literacy, was screened using a parsimonious instrument validated as a dichotomized measure.¹² Instruments in English and Spanish were tested for appropriate and comprehensible word choices and syntax through pilot testing. We inferred LEP among patients preferring to complete the survey in Spanish based on our familiarity with the population. We defined any Internet access as having a mobile data-service plan or having home-Internet access. In addition, we inquired about primary insurance provider and offered Medicaid patients an informational brochure about the federal Lifeline Program (https://www.fcc.gov/lifeline) that subsidizes text-messaging-enabled cellular telephone service for low-income patients. Notably, we assessed engagement by asking about the extent of patients’ interest in “new ways of communicating with your doctor, clinic, or pharmacy using” text, e-mail, or mobile apps with a 5-level response scale ranging from “not at all interested” to “very interested”.

Participant characteristics were confirmed to be similar to the Cook County Health and Hospitals System patient population in 2015 with regards to age, gender, and race/ethnicity. We calculated unadjusted and adjusted odds ratios for IRHL and LEP’s association with each dependent measure of access (to smartphone, Internet, or e-mail) and engagement (using text messaging, e-mail, or mobile apps) controlling for age, gender, primary payer, recruitment location, IRHL, and LEP. Because we oversampled inpatients, we estimated sampling-weight-adjusted proportions and 95% confidence intervals (CI) of the entire CCHHS patient population with access to smartphone, data/text plan, non-prepaid plan, and service interruptions using STATA v13 (StataCorp LP, College Station Texas). The project received a waiver upon review by the local Institutional Review Board.

Participation rate was 65% (302/464). Differences in patients by site are shown in Table 1. IRHL was more frequent and LEP less frequent among hospitalized patients. As shown in Table 2, patients with IRHL were less likely to have any Internet access, to have an active e-mail account, and to be interested in using e-mail for healthcare communications. Patients with LEP were less likely than English speakers to be interested in using mobile apps. Inpatients were less likely than outpatients to be interested in text messaging for healthcare communications.

The estimated proportion (95% CI) of the health system’s patients owning a text-enabled mobile device was 87% (75%-94%) and an Internet-enabled mobile device was 64% (47%-78%). The proportion with no data service interruptions in the previous year was 40% (31%-50%).

In this cross-section of urban low-income adult patients, IRHL and LEP were factors associated with potential disparities introduced by mHealth. Even as access to smartphones becomes ubiquitous, lagging access to Internet and e-mail among low literacy patients, and low levels of technology engagement for healthcare communications among patients with IRHL or LEP, underscore concerns about equity in health systems’ adoption of mHealth strategies. Hospitalized patients were found to have diminished engagement with mHealth independent of IRHL and LEP.

Regarding engagement, significantly fewer patients with IRHL or LEP were interested in using technology for healthcare communications. Our finding suggests that health disparities already associated with these conditions¹³ may not be reduced by mobile device outreach alone and may even be worsened by it. Touch screens, audio-enabled questionnaires, and language translation engines are innovations that may be helpful to mitigate IRHL and LEP, but evidence is scarce. Privacy and security concerns, and lack of experience with technology, may also lower engagement. A contemporaneous study found lower apps’ usage among Latinos, also suggesting that language concordance between apps, their source, and targeted users is important.¹⁴ Low-tech solutions involving mobile telephone or even lower tech in-person communications targeted to the estimated 26% of the US population with low literacy¹⁵ and 20% with LEP¹⁶ may be practical stopgap measures. Even as disparities in access to technology across race-ethnicity are diminishing,¹⁰ equity across poverty levels, low levels of education, cultural norms, and disabilities may be more challenging to overcome. Our assessment indicates that large exclusions of a safety-net population in 2015 are a legitimate concern in communication strategies that rely too heavily on mHealth. These findings underscore the CONSORT-EHEALTH recommendation that investigators report web-based recruitment strategies and data-collection methods comprehensively.¹⁷

Regarding access, our estimates suggest that historical disparities in smartphone ownership are diminishing, but access to Internet capabilities may still be lower among the urban poor compared to the nation as a whole. The Pew Research Center found that 64% of Americans owned a smartphone in 2015 (respondents defined smartphone).¹⁰ In comparison, 87% (95% CI, 75%, 94%) of our study participants owned a text-enabled mobile device and 64% (47%, 78%) owned an Internet-enabled mobile device. However, the 40% (31%, 50%) of our safety-net population with an uninterrupted data plan over the previous year may be lower than the 50% of Americans reporting uninterrupted data plans over their lifetime.¹⁰ The impact of expense-related data plan interruptions is magnified by the 40% of our study population—compared to 15% of Americans—who are dependent on mobile devices for Internet access.¹⁰ The association between Internet connectivity and literacy evokes multiple bidirectional pathways yet to be elucidated. But if mHealth can reduce health disparities, closing the gap in device ownership is only a partial accomplishment, and future work also needs to expand Internet connectivity to allow literacy-enhancing and literacy-naïve technologies to flourish.

This study has limitations. Our study population was a consecutive sample and participation rate was less than 100%. However, we recruited participants into the study the way we may also have approached patients to introduce mHealth options in our clinical settings. Our sampling method proved adequate for our primary goal to explain differences in technology access and engagement using regression analysis. Although our patient population may not directly generalize to many healthcare systems, including other safety-net systems serving regions with variable technology uptake,¹⁸ our findings reflect the capacities and the preferences of the most disadvantaged segments of urban populations. We systematically excluded LEP non-Spanish speakers, but they consisted of less than 5% of inpatients and no outpatients. We did not assess current technology use. Finally, as discussed earlier, access and use of new technologies change rapidly and frequent updates are necessary.

mHealth is a promising tool because it may increase healthcare access, improve care quality, and promote research. All these potential benefits will be obtained with accompanying efforts to reduce healthcare disparities, especially where some technologies themselves are exclusionary.⁹ As research of mHealth methods grows, support for patients with IRHL and LEP are still necessary to simultaneously advance our shared goal for equity.

Disclosures

The authors report no financial conflicts of interest.

Each year, approximately one-third of all hospitalized medical and surgical patients in the United States (about 12 million patients) are exposed to heparin products for the prevention or treatment of thromboembolism.¹ Although generally safe, heparin can trigger an immune response in which platelet factor 4–heparin complexes set off an antibody-mediated cascade that can result in heparin-induced thrombocytopenia (HIT) and paradoxical arterial and venous thromboses, or heparin-induced thrombocytopenia with thrombosis (HITT). The incidence of HIT appears to be significantly higher with the more immunogenic unfractionated heparin (UFH) (2%-3% if treated for ≥5 days) than with low-molecular-weight heparin (LMWH) (0.2%-0.6%)² and is significantly higher in postoperative patients (1%-5%) than in medical patients.³ Older patients and female patients, especially those who undergo surgery, are thought to be at higher risk.⁴ Progression from HIT to HITT can occur in up to 50% of surgical patients,⁵ and HITT can significantly increase mortality.⁴

In the United States, LMWH use has increased 5-fold since 2000—an increase attributed to the 2010 release of generic enoxaparin.⁶ As US hospitals switch from UFH to LMWH with its significantly lower risk of HIT, up-to-date HIT incidence data may help physicians and payers better understand the impact of the disorder on mortality and hospital length of stay (LOS) for medical patients and subsets of surgical patients and subsequently direct screening efforts to those at highest risk. Therefore, in the present study, we used national data to determine the latest incidence and economic implications of HIT overall and for high-risk surgical groups.

In this study, we analyzed data from the Nationwide Inpatient Sample (NIS) database, part of the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality (AHRQ). The period studied was 2009-2011. We used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 289.84, introduced in 2009, to identify patients who were at least 18 years old and had a primary or secondary diagnosis of HIT. Validated Clinical Classifications Software (CCS) was used to identify those who underwent cardiac, vascular, or orthopedic surgery, and ICD-9-CM codes for various thromboses were used to identify those with HITT (Supplemental Figure, Supplemental Table 1). Baseline patient and hospital characteristics were compared using the Pearson’s Chi-square test for categorical variables and the Student t test for continuous variables (2-sided P < 0.05 for statistical significance) (Table 1). We calculated the incidence of HIT overall and for the 3 surgical subgroups and compared the cohorts on their mean hospital LOS, mean hospital charge, and in-hospital mortality (Table 2).

Statistical analysis was performed with Stata Version 13.1 (Stata Corp, College Station, TX). Survey commands were used to account for the complex survey design in NIS. Reading Health System’s Institutional Review Board determined that our study protocol was exempt.

RESULTS

Of 98,636,364 hospitalizations, 72,515 (0.07%) involved HIT. There were no significant differences in the annual incidence of HIT during the study period (0.06% in 2009, 0.05% in 2010, 0.06% in 2011).

Patients with HIT were older than patients without HIT (mean age, 65.3 vs 57.3 years; P < 0.001). HIT was slightly more common in men overall (OR, 1.48; 95% CI, 1.46-1.51), but subgroup analyses revealed women had higher rates of HIT after cardiac surgery (OR, 1.41; 95% CI, 1.26-1.58) and vascular surgery (OR, 1.42; 95% CI, 1.29-1.57), though not after orthopedic surgery (OR, 1.06; 95% CI, 0.89-1.26). The majority of HIT cases were in urban teaching hospitals (56.23%) and in large hospitals, those with at least 325 beds (69.26%). There was no difference in mean age between patients with HITT and patients with HIT without thrombosis (65.46 vs 65.14 years; P = 0.32). Although the incidence of HITT did not differ by hospital location or teaching status, HITT cases were more common in hospitals with at least 325 beds (71.81%).

Regarding HIT, the death rate was 4-fold higher for patients with the disorder (9.63%) than for those without it (2.19%); hospital LOS and costs were significantly higher, too (Table 2). In addition, in-hospital mortality was higher (P < 0.001) for patients with HITT (12.28%) than for patients with HIT without thrombosis (8.24%); HITT patients’ hospital LOS and costs were higher as well. In patients who had cardiac, vascular, or orthopedic surgery, development of HIT was also associated with significantly higher in-hospital mortality, mean hospital LOS, and mean hospital charge. In patients with HITT, deep vein thrombosis (DVT) and pulmonary embolism represented the majority of reported cases (Supplemental Table 2). However, in patients who had cardiac surgery, acute arterial thromboses of coronary and cerebral vessels were more common.

DISCUSSION

In this national database survey, the overall incidence of HIT during the study period 2009-2011 was 0.07%, or 1 in 1350 hospitalized patients. Although earlier studies reported rates as high as 5% for high-risk subgroups of surgical patients,⁷ our data are more in line with more recently reported rates: about 0.02% for hospital admissions⁸ and from less than 0.1% to 0.4% for patients who received heparin.⁹ Older studies, which predominantly involved postoperative patients and were conducted when UFH often was the first-line heparin product used, may account for higher rates relative to ours. Of the 3 types of surgeries we evaluated, cardiac surgery had the highest HIT rate (0.5%), consistent with other studies.⁴ The higher HIT/HITT rates found for larger urban hospitals in our study might be attributable to increased awareness and testing, availability of hematology consultation, and higher risk of heparin use in this setting, where patients are sicker and cases and procedures more complicated.

Age was an important determinant of HIT risk in our study and in similar large-database series.⁴ Whether increased UFH use in the elderly (because of age or kidney disease) was a causative factor in this finding is unknown. In our study, although men and women had a nearly equal incidence of HIT, women had a significantly higher risk of HIT after both cardiac surgery and vascular surgery. Immune-mediated mechanisms that are more common in females may play a causative role in these settings.¹⁰

Our study results showed HIT associated with increased hospital LOS and an almost 4-fold increase in inpatient mortality and costs. The increased economic burden in HIT cases may be driven by the diagnostic work-up cost and expensive alternative anticoagulation.^11,12 Similarly, compared with HIT without thrombosis, HITT was associated with significantly increased hospital LOS (3.7 days), total hospital charge ($64,279) and mortality (49% increase, to 12.2% from 8.2%), consistent with prior studies.¹³ In addition, 34.1% (24,704) of our HIT patients developed at least 1 thrombotic complication, with venous thromboses more common than arterial thromboses, as previously reported.¹³ Lower extremity DVT was the most common thrombosis in orthopedic and vascular surgery. However, in cardiac surgery, acute coronary occlusion was the most common thrombotic complication. We postulate that the difference stems from the increased propensity of HIT-related thrombosis to occur in areas of vascular injury.¹⁴

The strengths of our study include its large size, which increases the generalizability of its results and avoids the biases inherent in small, single-center studies. As with any administrative dataset, the NIS may include coding errors related to underdiagnosis and overdiagnosis (eg, a HIT/HITT diagnosis carried forward from prior episodes). In our study, we inferred the HITT diagnosis in HIT cases with a vascular complication, but we could have missed HIT cases that had not been coded for vascular complications, and we could have overassociated vascular complications that had predated HIT and been treated with heparin. Although HIT and HITT were associated with worse clinical outcomes and increased hospital LOS, it is possible patients who were hospitalized longer had more opportunities for heparin use, and this exposure led to HIT or HITT. The lack of details regarding prior heparin use, including type of heparin (UFH or LMWH), prevented us from inferring the actual risks of individual heparin products.

In conclusion, in cardiac, vascular, and orthopedic surgery, HIT and especially HITT can significantly increase hospital LOS, inpatient costs, and mortality. Lower extremity DVT and acute coronary artery occlusion are the most common thrombotic complications in these cases. HIT screening strategies that incorporate platelet counts are recommended only in patients at highest risk (>1%), according to the most recent American College of Chest Physicians guidelines, but this recommendation was made on the basis of the high cost of alternative anticoagulants. Given our more recent data regarding the very high costs of HIT and especially HITT, screening strategies with platelet counts may prove more cost-effective. Recent genome-wide studies that found higher rates of HIT in patients with T-cell death–associated gene 8 (TDAG8) may help explain sex differences in postoperative patients and identify patients at highest risk so alternative anticoagulants can be used.¹⁵

 

 

Disclosures

This study was funded by the Reading Health System (grant RHS0010). Dr. Bhatt is supported by the Physician-Scientist Training Program (grant 2015-2016), College of Medicine, University of Nebraska Medical Center. The other authors report no financial conflicts of interest.

 

Each year, approximately one-third of all hospitalized medical and surgical patients in the United States (about 12 million patients) are exposed to heparin products for the prevention or treatment of thromboembolism.¹ Although generally safe, heparin can trigger an immune response in which platelet factor 4–heparin complexes set off an antibody-mediated cascade that can result in heparin-induced thrombocytopenia (HIT) and paradoxical arterial and venous thromboses, or heparin-induced thrombocytopenia with thrombosis (HITT). The incidence of HIT appears to be significantly higher with the more immunogenic unfractionated heparin (UFH) (2%-3% if treated for ≥5 days) than with low-molecular-weight heparin (LMWH) (0.2%-0.6%)² and is significantly higher in postoperative patients (1%-5%) than in medical patients.³ Older patients and female patients, especially those who undergo surgery, are thought to be at higher risk.⁴ Progression from HIT to HITT can occur in up to 50% of surgical patients,⁵ and HITT can significantly increase mortality.⁴

In the United States, LMWH use has increased 5-fold since 2000—an increase attributed to the 2010 release of generic enoxaparin.⁶ As US hospitals switch from UFH to LMWH with its significantly lower risk of HIT, up-to-date HIT incidence data may help physicians and payers better understand the impact of the disorder on mortality and hospital length of stay (LOS) for medical patients and subsets of surgical patients and subsequently direct screening efforts to those at highest risk. Therefore, in the present study, we used national data to determine the latest incidence and economic implications of HIT overall and for high-risk surgical groups.

In this study, we analyzed data from the Nationwide Inpatient Sample (NIS) database, part of the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality (AHRQ). The period studied was 2009-2011. We used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 289.84, introduced in 2009, to identify patients who were at least 18 years old and had a primary or secondary diagnosis of HIT. Validated Clinical Classifications Software (CCS) was used to identify those who underwent cardiac, vascular, or orthopedic surgery, and ICD-9-CM codes for various thromboses were used to identify those with HITT (Supplemental Figure, Supplemental Table 1). Baseline patient and hospital characteristics were compared using the Pearson’s Chi-square test for categorical variables and the Student t test for continuous variables (2-sided P < 0.05 for statistical significance) (Table 1). We calculated the incidence of HIT overall and for the 3 surgical subgroups and compared the cohorts on their mean hospital LOS, mean hospital charge, and in-hospital mortality (Table 2).

Statistical analysis was performed with Stata Version 13.1 (Stata Corp, College Station, TX). Survey commands were used to account for the complex survey design in NIS. Reading Health System’s Institutional Review Board determined that our study protocol was exempt.

RESULTS

Of 98,636,364 hospitalizations, 72,515 (0.07%) involved HIT. There were no significant differences in the annual incidence of HIT during the study period (0.06% in 2009, 0.05% in 2010, 0.06% in 2011).

Patients with HIT were older than patients without HIT (mean age, 65.3 vs 57.3 years; P < 0.001). HIT was slightly more common in men overall (OR, 1.48; 95% CI, 1.46-1.51), but subgroup analyses revealed women had higher rates of HIT after cardiac surgery (OR, 1.41; 95% CI, 1.26-1.58) and vascular surgery (OR, 1.42; 95% CI, 1.29-1.57), though not after orthopedic surgery (OR, 1.06; 95% CI, 0.89-1.26). The majority of HIT cases were in urban teaching hospitals (56.23%) and in large hospitals, those with at least 325 beds (69.26%). There was no difference in mean age between patients with HITT and patients with HIT without thrombosis (65.46 vs 65.14 years; P = 0.32). Although the incidence of HITT did not differ by hospital location or teaching status, HITT cases were more common in hospitals with at least 325 beds (71.81%).

Regarding HIT, the death rate was 4-fold higher for patients with the disorder (9.63%) than for those without it (2.19%); hospital LOS and costs were significantly higher, too (Table 2). In addition, in-hospital mortality was higher (P < 0.001) for patients with HITT (12.28%) than for patients with HIT without thrombosis (8.24%); HITT patients’ hospital LOS and costs were higher as well. In patients who had cardiac, vascular, or orthopedic surgery, development of HIT was also associated with significantly higher in-hospital mortality, mean hospital LOS, and mean hospital charge. In patients with HITT, deep vein thrombosis (DVT) and pulmonary embolism represented the majority of reported cases (Supplemental Table 2). However, in patients who had cardiac surgery, acute arterial thromboses of coronary and cerebral vessels were more common.

DISCUSSION

In this national database survey, the overall incidence of HIT during the study period 2009-2011 was 0.07%, or 1 in 1350 hospitalized patients. Although earlier studies reported rates as high as 5% for high-risk subgroups of surgical patients,⁷ our data are more in line with more recently reported rates: about 0.02% for hospital admissions⁸ and from less than 0.1% to 0.4% for patients who received heparin.⁹ Older studies, which predominantly involved postoperative patients and were conducted when UFH often was the first-line heparin product used, may account for higher rates relative to ours. Of the 3 types of surgeries we evaluated, cardiac surgery had the highest HIT rate (0.5%), consistent with other studies.⁴ The higher HIT/HITT rates found for larger urban hospitals in our study might be attributable to increased awareness and testing, availability of hematology consultation, and higher risk of heparin use in this setting, where patients are sicker and cases and procedures more complicated.

Age was an important determinant of HIT risk in our study and in similar large-database series.⁴ Whether increased UFH use in the elderly (because of age or kidney disease) was a causative factor in this finding is unknown. In our study, although men and women had a nearly equal incidence of HIT, women had a significantly higher risk of HIT after both cardiac surgery and vascular surgery. Immune-mediated mechanisms that are more common in females may play a causative role in these settings.¹⁰

Our study results showed HIT associated with increased hospital LOS and an almost 4-fold increase in inpatient mortality and costs. The increased economic burden in HIT cases may be driven by the diagnostic work-up cost and expensive alternative anticoagulation.^11,12 Similarly, compared with HIT without thrombosis, HITT was associated with significantly increased hospital LOS (3.7 days), total hospital charge ($64,279) and mortality (49% increase, to 12.2% from 8.2%), consistent with prior studies.¹³ In addition, 34.1% (24,704) of our HIT patients developed at least 1 thrombotic complication, with venous thromboses more common than arterial thromboses, as previously reported.¹³ Lower extremity DVT was the most common thrombosis in orthopedic and vascular surgery. However, in cardiac surgery, acute coronary occlusion was the most common thrombotic complication. We postulate that the difference stems from the increased propensity of HIT-related thrombosis to occur in areas of vascular injury.¹⁴

The strengths of our study include its large size, which increases the generalizability of its results and avoids the biases inherent in small, single-center studies. As with any administrative dataset, the NIS may include coding errors related to underdiagnosis and overdiagnosis (eg, a HIT/HITT diagnosis carried forward from prior episodes). In our study, we inferred the HITT diagnosis in HIT cases with a vascular complication, but we could have missed HIT cases that had not been coded for vascular complications, and we could have overassociated vascular complications that had predated HIT and been treated with heparin. Although HIT and HITT were associated with worse clinical outcomes and increased hospital LOS, it is possible patients who were hospitalized longer had more opportunities for heparin use, and this exposure led to HIT or HITT. The lack of details regarding prior heparin use, including type of heparin (UFH or LMWH), prevented us from inferring the actual risks of individual heparin products.

In conclusion, in cardiac, vascular, and orthopedic surgery, HIT and especially HITT can significantly increase hospital LOS, inpatient costs, and mortality. Lower extremity DVT and acute coronary artery occlusion are the most common thrombotic complications in these cases. HIT screening strategies that incorporate platelet counts are recommended only in patients at highest risk (>1%), according to the most recent American College of Chest Physicians guidelines, but this recommendation was made on the basis of the high cost of alternative anticoagulants. Given our more recent data regarding the very high costs of HIT and especially HITT, screening strategies with platelet counts may prove more cost-effective. Recent genome-wide studies that found higher rates of HIT in patients with T-cell death–associated gene 8 (TDAG8) may help explain sex differences in postoperative patients and identify patients at highest risk so alternative anticoagulants can be used.¹⁵

 

 

Disclosures

This study was funded by the Reading Health System (grant RHS0010). Dr. Bhatt is supported by the Physician-Scientist Training Program (grant 2015-2016), College of Medicine, University of Nebraska Medical Center. The other authors report no financial conflicts of interest.

 

Each year, approximately one-third of all hospitalized medical and surgical patients in the United States (about 12 million patients) are exposed to heparin products for the prevention or treatment of thromboembolism.¹ Although generally safe, heparin can trigger an immune response in which platelet factor 4–heparin complexes set off an antibody-mediated cascade that can result in heparin-induced thrombocytopenia (HIT) and paradoxical arterial and venous thromboses, or heparin-induced thrombocytopenia with thrombosis (HITT). The incidence of HIT appears to be significantly higher with the more immunogenic unfractionated heparin (UFH) (2%-3% if treated for ≥5 days) than with low-molecular-weight heparin (LMWH) (0.2%-0.6%)² and is significantly higher in postoperative patients (1%-5%) than in medical patients.³ Older patients and female patients, especially those who undergo surgery, are thought to be at higher risk.⁴ Progression from HIT to HITT can occur in up to 50% of surgical patients,⁵ and HITT can significantly increase mortality.⁴

In the United States, LMWH use has increased 5-fold since 2000—an increase attributed to the 2010 release of generic enoxaparin.⁶ As US hospitals switch from UFH to LMWH with its significantly lower risk of HIT, up-to-date HIT incidence data may help physicians and payers better understand the impact of the disorder on mortality and hospital length of stay (LOS) for medical patients and subsets of surgical patients and subsequently direct screening efforts to those at highest risk. Therefore, in the present study, we used national data to determine the latest incidence and economic implications of HIT overall and for high-risk surgical groups.

In this study, we analyzed data from the Nationwide Inpatient Sample (NIS) database, part of the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality (AHRQ). The period studied was 2009-2011. We used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 289.84, introduced in 2009, to identify patients who were at least 18 years old and had a primary or secondary diagnosis of HIT. Validated Clinical Classifications Software (CCS) was used to identify those who underwent cardiac, vascular, or orthopedic surgery, and ICD-9-CM codes for various thromboses were used to identify those with HITT (Supplemental Figure, Supplemental Table 1). Baseline patient and hospital characteristics were compared using the Pearson’s Chi-square test for categorical variables and the Student t test for continuous variables (2-sided P < 0.05 for statistical significance) (Table 1). We calculated the incidence of HIT overall and for the 3 surgical subgroups and compared the cohorts on their mean hospital LOS, mean hospital charge, and in-hospital mortality (Table 2).

Statistical analysis was performed with Stata Version 13.1 (Stata Corp, College Station, TX). Survey commands were used to account for the complex survey design in NIS. Reading Health System’s Institutional Review Board determined that our study protocol was exempt.

RESULTS

Of 98,636,364 hospitalizations, 72,515 (0.07%) involved HIT. There were no significant differences in the annual incidence of HIT during the study period (0.06% in 2009, 0.05% in 2010, 0.06% in 2011).

Patients with HIT were older than patients without HIT (mean age, 65.3 vs 57.3 years; P < 0.001). HIT was slightly more common in men overall (OR, 1.48; 95% CI, 1.46-1.51), but subgroup analyses revealed women had higher rates of HIT after cardiac surgery (OR, 1.41; 95% CI, 1.26-1.58) and vascular surgery (OR, 1.42; 95% CI, 1.29-1.57), though not after orthopedic surgery (OR, 1.06; 95% CI, 0.89-1.26). The majority of HIT cases were in urban teaching hospitals (56.23%) and in large hospitals, those with at least 325 beds (69.26%). There was no difference in mean age between patients with HITT and patients with HIT without thrombosis (65.46 vs 65.14 years; P = 0.32). Although the incidence of HITT did not differ by hospital location or teaching status, HITT cases were more common in hospitals with at least 325 beds (71.81%).

Regarding HIT, the death rate was 4-fold higher for patients with the disorder (9.63%) than for those without it (2.19%); hospital LOS and costs were significantly higher, too (Table 2). In addition, in-hospital mortality was higher (P < 0.001) for patients with HITT (12.28%) than for patients with HIT without thrombosis (8.24%); HITT patients’ hospital LOS and costs were higher as well. In patients who had cardiac, vascular, or orthopedic surgery, development of HIT was also associated with significantly higher in-hospital mortality, mean hospital LOS, and mean hospital charge. In patients with HITT, deep vein thrombosis (DVT) and pulmonary embolism represented the majority of reported cases (Supplemental Table 2). However, in patients who had cardiac surgery, acute arterial thromboses of coronary and cerebral vessels were more common.

DISCUSSION

In this national database survey, the overall incidence of HIT during the study period 2009-2011 was 0.07%, or 1 in 1350 hospitalized patients. Although earlier studies reported rates as high as 5% for high-risk subgroups of surgical patients,⁷ our data are more in line with more recently reported rates: about 0.02% for hospital admissions⁸ and from less than 0.1% to 0.4% for patients who received heparin.⁹ Older studies, which predominantly involved postoperative patients and were conducted when UFH often was the first-line heparin product used, may account for higher rates relative to ours. Of the 3 types of surgeries we evaluated, cardiac surgery had the highest HIT rate (0.5%), consistent with other studies.⁴ The higher HIT/HITT rates found for larger urban hospitals in our study might be attributable to increased awareness and testing, availability of hematology consultation, and higher risk of heparin use in this setting, where patients are sicker and cases and procedures more complicated.

Age was an important determinant of HIT risk in our study and in similar large-database series.⁴ Whether increased UFH use in the elderly (because of age or kidney disease) was a causative factor in this finding is unknown. In our study, although men and women had a nearly equal incidence of HIT, women had a significantly higher risk of HIT after both cardiac surgery and vascular surgery. Immune-mediated mechanisms that are more common in females may play a causative role in these settings.¹⁰

Our study results showed HIT associated with increased hospital LOS and an almost 4-fold increase in inpatient mortality and costs. The increased economic burden in HIT cases may be driven by the diagnostic work-up cost and expensive alternative anticoagulation.^11,12 Similarly, compared with HIT without thrombosis, HITT was associated with significantly increased hospital LOS (3.7 days), total hospital charge ($64,279) and mortality (49% increase, to 12.2% from 8.2%), consistent with prior studies.¹³ In addition, 34.1% (24,704) of our HIT patients developed at least 1 thrombotic complication, with venous thromboses more common than arterial thromboses, as previously reported.¹³ Lower extremity DVT was the most common thrombosis in orthopedic and vascular surgery. However, in cardiac surgery, acute coronary occlusion was the most common thrombotic complication. We postulate that the difference stems from the increased propensity of HIT-related thrombosis to occur in areas of vascular injury.¹⁴

The strengths of our study include its large size, which increases the generalizability of its results and avoids the biases inherent in small, single-center studies. As with any administrative dataset, the NIS may include coding errors related to underdiagnosis and overdiagnosis (eg, a HIT/HITT diagnosis carried forward from prior episodes). In our study, we inferred the HITT diagnosis in HIT cases with a vascular complication, but we could have missed HIT cases that had not been coded for vascular complications, and we could have overassociated vascular complications that had predated HIT and been treated with heparin. Although HIT and HITT were associated with worse clinical outcomes and increased hospital LOS, it is possible patients who were hospitalized longer had more opportunities for heparin use, and this exposure led to HIT or HITT. The lack of details regarding prior heparin use, including type of heparin (UFH or LMWH), prevented us from inferring the actual risks of individual heparin products.

In conclusion, in cardiac, vascular, and orthopedic surgery, HIT and especially HITT can significantly increase hospital LOS, inpatient costs, and mortality. Lower extremity DVT and acute coronary artery occlusion are the most common thrombotic complications in these cases. HIT screening strategies that incorporate platelet counts are recommended only in patients at highest risk (>1%), according to the most recent American College of Chest Physicians guidelines, but this recommendation was made on the basis of the high cost of alternative anticoagulants. Given our more recent data regarding the very high costs of HIT and especially HITT, screening strategies with platelet counts may prove more cost-effective. Recent genome-wide studies that found higher rates of HIT in patients with T-cell death–associated gene 8 (TDAG8) may help explain sex differences in postoperative patients and identify patients at highest risk so alternative anticoagulants can be used.¹⁵

 

 

Disclosures

This study was funded by the Reading Health System (grant RHS0010). Dr. Bhatt is supported by the Physician-Scientist Training Program (grant 2015-2016), College of Medicine, University of Nebraska Medical Center. The other authors report no financial conflicts of interest.