The peripheral blood absolute lymphocyte-to-monocyte ratio (ALC/AMC) was prognostic for overall survival in mantle cell lymphoma patients who have undergone induction therapy, based on a retrospective review study of 96 patients by Andre Goy, MD, of John Theurer Cancer Center, Hackensack (NJ) University, and his colleagues.

Overall survival was better when ALC/AMC was 2 or greater following induction therapy, the researchers wrote in an abstract published in conjunction with the annual meeting of the American Society of Clinical Oncology.

The finding indicates that novel maintenance programs, including targeting the microenvironment or immune response, might be appropriate when patients with mantle cell lymphoma have low ALC/AMC.

The researchers examined data for 96 consecutive mantle cell lymphoma patients. The ALC/AMC was determined from peripheral blood counts obtained approximately 30 days following completion of initial therapy or immediately prior to stem cell mobilization in patients who had first line stem cell transplants.

The ALC/AMC was less than 2 in 67 patients and was 2 or greater in 29 patients. The two patient cohorts were similar in median age, ethnicities, stage distributions, elevated beta-2-microglobulin, elevated lactate dehydrogenate, and Mantle Cell Lymphoma International Prognostic Index scores.

ALC/AMC was less than 2 in 10 of 13 transplanted patients and in 57 of 83 patients who did not undergo transplants. At a median follow-up of 43 months, the median overall survival has not been reached in either cohort.

The 5-year survival rate was 90% among patients with an ALC/AMC of 2 or greater and 68% in those with an ALC/AMC less than 2 (log-rank P less than .05).

Similar ALC/AMC 5-year survival trends were noted when subsetting to the 25 patients with high risk Mantle Cell Lymphoma International Prognostic Index scores (72% vs. 45%; P = .07).

Dr. Goy disclosed honoraria from Acerta Pharma, Celgene, Pharmacyclics, and Takeda; a consulting or advisory role with Acerta Pharma, Celgene, Infinity Pharmaceuticals, Pharmacyclics, and Takeda; and speakers’ bureaus participation for Pharmacyclics and Takeda.

Prognostic value of the absolute lymphocyte-to-monocyte (ALC/AMC) ratio on overall survival among patients with mantle cell lymphoma. Published in conjunction with the 2017 ASCO Annual Meeting. Abstract No: e19030.

[email protected]

On Twitter @maryjodales

The peripheral blood absolute lymphocyte-to-monocyte ratio (ALC/AMC) was prognostic for overall survival in mantle cell lymphoma patients who have undergone induction therapy, based on a retrospective review study of 96 patients by Andre Goy, MD, of John Theurer Cancer Center, Hackensack (NJ) University, and his colleagues.

Overall survival was better when ALC/AMC was 2 or greater following induction therapy, the researchers wrote in an abstract published in conjunction with the annual meeting of the American Society of Clinical Oncology.

The finding indicates that novel maintenance programs, including targeting the microenvironment or immune response, might be appropriate when patients with mantle cell lymphoma have low ALC/AMC.

The researchers examined data for 96 consecutive mantle cell lymphoma patients. The ALC/AMC was determined from peripheral blood counts obtained approximately 30 days following completion of initial therapy or immediately prior to stem cell mobilization in patients who had first line stem cell transplants.

The ALC/AMC was less than 2 in 67 patients and was 2 or greater in 29 patients. The two patient cohorts were similar in median age, ethnicities, stage distributions, elevated beta-2-microglobulin, elevated lactate dehydrogenate, and Mantle Cell Lymphoma International Prognostic Index scores.

ALC/AMC was less than 2 in 10 of 13 transplanted patients and in 57 of 83 patients who did not undergo transplants. At a median follow-up of 43 months, the median overall survival has not been reached in either cohort.

The 5-year survival rate was 90% among patients with an ALC/AMC of 2 or greater and 68% in those with an ALC/AMC less than 2 (log-rank P less than .05).

Similar ALC/AMC 5-year survival trends were noted when subsetting to the 25 patients with high risk Mantle Cell Lymphoma International Prognostic Index scores (72% vs. 45%; P = .07).

Dr. Goy disclosed honoraria from Acerta Pharma, Celgene, Pharmacyclics, and Takeda; a consulting or advisory role with Acerta Pharma, Celgene, Infinity Pharmaceuticals, Pharmacyclics, and Takeda; and speakers’ bureaus participation for Pharmacyclics and Takeda.

Prognostic value of the absolute lymphocyte-to-monocyte (ALC/AMC) ratio on overall survival among patients with mantle cell lymphoma. Published in conjunction with the 2017 ASCO Annual Meeting. Abstract No: e19030.

[email protected]

On Twitter @maryjodales

The peripheral blood absolute lymphocyte-to-monocyte ratio (ALC/AMC) was prognostic for overall survival in mantle cell lymphoma patients who have undergone induction therapy, based on a retrospective review study of 96 patients by Andre Goy, MD, of John Theurer Cancer Center, Hackensack (NJ) University, and his colleagues.

Overall survival was better when ALC/AMC was 2 or greater following induction therapy, the researchers wrote in an abstract published in conjunction with the annual meeting of the American Society of Clinical Oncology.

The finding indicates that novel maintenance programs, including targeting the microenvironment or immune response, might be appropriate when patients with mantle cell lymphoma have low ALC/AMC.

The researchers examined data for 96 consecutive mantle cell lymphoma patients. The ALC/AMC was determined from peripheral blood counts obtained approximately 30 days following completion of initial therapy or immediately prior to stem cell mobilization in patients who had first line stem cell transplants.

The ALC/AMC was less than 2 in 67 patients and was 2 or greater in 29 patients. The two patient cohorts were similar in median age, ethnicities, stage distributions, elevated beta-2-microglobulin, elevated lactate dehydrogenate, and Mantle Cell Lymphoma International Prognostic Index scores.

ALC/AMC was less than 2 in 10 of 13 transplanted patients and in 57 of 83 patients who did not undergo transplants. At a median follow-up of 43 months, the median overall survival has not been reached in either cohort.

The 5-year survival rate was 90% among patients with an ALC/AMC of 2 or greater and 68% in those with an ALC/AMC less than 2 (log-rank P less than .05).

Similar ALC/AMC 5-year survival trends were noted when subsetting to the 25 patients with high risk Mantle Cell Lymphoma International Prognostic Index scores (72% vs. 45%; P = .07).

Dr. Goy disclosed honoraria from Acerta Pharma, Celgene, Pharmacyclics, and Takeda; a consulting or advisory role with Acerta Pharma, Celgene, Infinity Pharmaceuticals, Pharmacyclics, and Takeda; and speakers’ bureaus participation for Pharmacyclics and Takeda.

Prognostic value of the absolute lymphocyte-to-monocyte (ALC/AMC) ratio on overall survival among patients with mantle cell lymphoma. Published in conjunction with the 2017 ASCO Annual Meeting. Abstract No: e19030.

[email protected]

On Twitter @maryjodales

A learning collaborative approach can significantly improve adherence to a treat-to-target approach in patients with rheumatoid arthritis, new research suggests.

While numerous clinical trials have shown that a strategy of treating to target achieves better outcomes, compared with usual care, there is evidence that this approach is not always practiced, wrote Daniel H. Solomon, MD, of the division of rheumatology at Brigham and Women’s Hospital, Boston, and his coauthors.

• Specifying a disease activity target.
• Recording RA disease activity, using one of four recommended measures (Disease Activity Score-28, Simplified Disease Activity Index, Clinical Disease Activity Index, or Routine Assessment of Patient Index Data 3), with results described numerically or by category (remission, low, moderate, or high).
• When a decision was being made (change in target or change in treatment), documenting shared decision making.
• Basing treatment decisions on target and disease activity measure or describing reasons why treat to target was not adhered to.

The intervention increased the treat-to-target implementation score by 46 percentage points, from 11% to 57%. In comparison, the control arm had an increase of 14 percentage points, from 11% to 25% (P = .004). It achieved a substantial and significant increase in the proportion of participants for whom a treatment target was documented, from 0.6% at baseline to 45.6% at the 9-month follow-up, compared with a 12.5-point increase in the control group. The recording of disease activity increased from 20% to 89.1% in the intervention arm, compared with an increase from 30.2% to 52.3% in the control arm. Similarly, shared decision making increased from 51.3% to 85.9% in the intervention group, compared with a rise from 24.5% to 43% in the control arm.

The study also examined a range of secondary outcomes representing the impact of the intervention on patients. They found that a positive change in treat-to-target adherence score was seen in 83.8% of patients in the intervention arm, compared with 36.8% of patients in the control arm (P = .0001), when almost no patient visits at baseline were adherent to all components of treat to target.

At the 9-month follow-up, 25.9% of visits in the intervention arm were adherent to all four components of the treat-to-target approach, compared with 5.6% in the control arm.

The authors suggested that their learning collaborative approach could be applied across a range of chronic diseases.

“In fact, our model is consistent with the goals and strategy of the Million Hearts campaign to reduce cardiovascular disease burden across the U.S. by targeting five areas of goal-based therapy.”

There were no significant differences between the intervention and control arms in the number of orders for drug-monitoring laboratory tests or radiology orders. However, patients in the intervention arm had fewer adverse events than did those in the control arm (0.26 vs. 0.43 per patient; P = .043).

The investigators noted that the study was relatively small – involving just 11 sites – and the primary outcome was a process measure that did not reflect clinical outcomes and has not been validated.

The study was supported by the National Institutes of Health. One author declared salary support through research grants to his hospital from pharmaceutical companies, while another declared research grants from AbbVie for treat-to-target research activities.

A learning collaborative approach can significantly improve adherence to a treat-to-target approach in patients with rheumatoid arthritis, new research suggests.

While numerous clinical trials have shown that a strategy of treating to target achieves better outcomes, compared with usual care, there is evidence that this approach is not always practiced, wrote Daniel H. Solomon, MD, of the division of rheumatology at Brigham and Women’s Hospital, Boston, and his coauthors.

• Specifying a disease activity target.
• Recording RA disease activity, using one of four recommended measures (Disease Activity Score-28, Simplified Disease Activity Index, Clinical Disease Activity Index, or Routine Assessment of Patient Index Data 3), with results described numerically or by category (remission, low, moderate, or high).
• When a decision was being made (change in target or change in treatment), documenting shared decision making.
• Basing treatment decisions on target and disease activity measure or describing reasons why treat to target was not adhered to.

The intervention increased the treat-to-target implementation score by 46 percentage points, from 11% to 57%. In comparison, the control arm had an increase of 14 percentage points, from 11% to 25% (P = .004). It achieved a substantial and significant increase in the proportion of participants for whom a treatment target was documented, from 0.6% at baseline to 45.6% at the 9-month follow-up, compared with a 12.5-point increase in the control group. The recording of disease activity increased from 20% to 89.1% in the intervention arm, compared with an increase from 30.2% to 52.3% in the control arm. Similarly, shared decision making increased from 51.3% to 85.9% in the intervention group, compared with a rise from 24.5% to 43% in the control arm.

The study also examined a range of secondary outcomes representing the impact of the intervention on patients. They found that a positive change in treat-to-target adherence score was seen in 83.8% of patients in the intervention arm, compared with 36.8% of patients in the control arm (P = .0001), when almost no patient visits at baseline were adherent to all components of treat to target.

At the 9-month follow-up, 25.9% of visits in the intervention arm were adherent to all four components of the treat-to-target approach, compared with 5.6% in the control arm.

The authors suggested that their learning collaborative approach could be applied across a range of chronic diseases.

“In fact, our model is consistent with the goals and strategy of the Million Hearts campaign to reduce cardiovascular disease burden across the U.S. by targeting five areas of goal-based therapy.”

There were no significant differences between the intervention and control arms in the number of orders for drug-monitoring laboratory tests or radiology orders. However, patients in the intervention arm had fewer adverse events than did those in the control arm (0.26 vs. 0.43 per patient; P = .043).

The investigators noted that the study was relatively small – involving just 11 sites – and the primary outcome was a process measure that did not reflect clinical outcomes and has not been validated.

The study was supported by the National Institutes of Health. One author declared salary support through research grants to his hospital from pharmaceutical companies, while another declared research grants from AbbVie for treat-to-target research activities.

A learning collaborative approach can significantly improve adherence to a treat-to-target approach in patients with rheumatoid arthritis, new research suggests.

While numerous clinical trials have shown that a strategy of treating to target achieves better outcomes, compared with usual care, there is evidence that this approach is not always practiced, wrote Daniel H. Solomon, MD, of the division of rheumatology at Brigham and Women’s Hospital, Boston, and his coauthors.

• Specifying a disease activity target.
• Recording RA disease activity, using one of four recommended measures (Disease Activity Score-28, Simplified Disease Activity Index, Clinical Disease Activity Index, or Routine Assessment of Patient Index Data 3), with results described numerically or by category (remission, low, moderate, or high).
• When a decision was being made (change in target or change in treatment), documenting shared decision making.
• Basing treatment decisions on target and disease activity measure or describing reasons why treat to target was not adhered to.

The intervention increased the treat-to-target implementation score by 46 percentage points, from 11% to 57%. In comparison, the control arm had an increase of 14 percentage points, from 11% to 25% (P = .004). It achieved a substantial and significant increase in the proportion of participants for whom a treatment target was documented, from 0.6% at baseline to 45.6% at the 9-month follow-up, compared with a 12.5-point increase in the control group. The recording of disease activity increased from 20% to 89.1% in the intervention arm, compared with an increase from 30.2% to 52.3% in the control arm. Similarly, shared decision making increased from 51.3% to 85.9% in the intervention group, compared with a rise from 24.5% to 43% in the control arm.

The study also examined a range of secondary outcomes representing the impact of the intervention on patients. They found that a positive change in treat-to-target adherence score was seen in 83.8% of patients in the intervention arm, compared with 36.8% of patients in the control arm (P = .0001), when almost no patient visits at baseline were adherent to all components of treat to target.

At the 9-month follow-up, 25.9% of visits in the intervention arm were adherent to all four components of the treat-to-target approach, compared with 5.6% in the control arm.

The authors suggested that their learning collaborative approach could be applied across a range of chronic diseases.

“In fact, our model is consistent with the goals and strategy of the Million Hearts campaign to reduce cardiovascular disease burden across the U.S. by targeting five areas of goal-based therapy.”

There were no significant differences between the intervention and control arms in the number of orders for drug-monitoring laboratory tests or radiology orders. However, patients in the intervention arm had fewer adverse events than did those in the control arm (0.26 vs. 0.43 per patient; P = .043).

The investigators noted that the study was relatively small – involving just 11 sites – and the primary outcome was a process measure that did not reflect clinical outcomes and has not been validated.

The study was supported by the National Institutes of Health. One author declared salary support through research grants to his hospital from pharmaceutical companies, while another declared research grants from AbbVie for treat-to-target research activities.

An elevated body mass index appears to be a risk factor for progression of smoldering multiple myeloma, according to Wilson I. Gonsalves, MD, and his colleagues at Mayo Clinic, Rochester, Minn.

The findings, based on median follow up data of 106 months from 306 patients diagnosed with smoldering multiple myeloma from 2000-2010 at the Mayo Clinic, need to be confirmed in larger studies. Nevertheless, the results imply that patient weight is a potentially modifiable risk factor for progression from smoldering disease to multiple myeloma, Dr. Gonsalves and his colleagues wrote.

[email protected]

On Twitter @maryjodales

An elevated body mass index appears to be a risk factor for progression of smoldering multiple myeloma, according to Wilson I. Gonsalves, MD, and his colleagues at Mayo Clinic, Rochester, Minn.

The findings, based on median follow up data of 106 months from 306 patients diagnosed with smoldering multiple myeloma from 2000-2010 at the Mayo Clinic, need to be confirmed in larger studies. Nevertheless, the results imply that patient weight is a potentially modifiable risk factor for progression from smoldering disease to multiple myeloma, Dr. Gonsalves and his colleagues wrote.

[email protected]

On Twitter @maryjodales

An elevated body mass index appears to be a risk factor for progression of smoldering multiple myeloma, according to Wilson I. Gonsalves, MD, and his colleagues at Mayo Clinic, Rochester, Minn.

The findings, based on median follow up data of 106 months from 306 patients diagnosed with smoldering multiple myeloma from 2000-2010 at the Mayo Clinic, need to be confirmed in larger studies. Nevertheless, the results imply that patient weight is a potentially modifiable risk factor for progression from smoldering disease to multiple myeloma, Dr. Gonsalves and his colleagues wrote.

[email protected]

On Twitter @maryjodales

Reduced opioid prescribing did not correlate with inpatient pain management scores, a study has shown.

The Centers for Medicare & Medicaid Services announced recently that, as of 2018, pain management will no longer be rated in the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, citing concerns that patient satisfaction surveys given at the time of postoperative discharge incentivizes clinicians to over-prescribe pain medication. Surgical patients are key contributors to HCAHPS scores, and opioids account for almost 40% of surgical prescriptions, according to the study.

A new study throws some shade on the CMS decision to delete pain management from the HCAHPS survey.

Ingram/thinkstock

Dr. Brummett disclosed relationships with Tonix and Neuros Medical. He also holds a patent for peripheral perineural dexmedetomidine. Mr. Syrjamaki and Dr. Dupree received support from Blue Cross Blue Shield of Michigan for their respective roles in the Michigan Value Collaborative. Dr. Waljee is an unpaid consultant for 3MHealth.

[email protected]

On Twitter @whitneymcknight

Reduced opioid prescribing did not correlate with inpatient pain management scores, a study has shown.

The Centers for Medicare & Medicaid Services announced recently that, as of 2018, pain management will no longer be rated in the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, citing concerns that patient satisfaction surveys given at the time of postoperative discharge incentivizes clinicians to over-prescribe pain medication. Surgical patients are key contributors to HCAHPS scores, and opioids account for almost 40% of surgical prescriptions, according to the study.

A new study throws some shade on the CMS decision to delete pain management from the HCAHPS survey.

Ingram/thinkstock

Dr. Brummett disclosed relationships with Tonix and Neuros Medical. He also holds a patent for peripheral perineural dexmedetomidine. Mr. Syrjamaki and Dr. Dupree received support from Blue Cross Blue Shield of Michigan for their respective roles in the Michigan Value Collaborative. Dr. Waljee is an unpaid consultant for 3MHealth.

[email protected]

On Twitter @whitneymcknight

Reduced opioid prescribing did not correlate with inpatient pain management scores, a study has shown.

The Centers for Medicare & Medicaid Services announced recently that, as of 2018, pain management will no longer be rated in the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, citing concerns that patient satisfaction surveys given at the time of postoperative discharge incentivizes clinicians to over-prescribe pain medication. Surgical patients are key contributors to HCAHPS scores, and opioids account for almost 40% of surgical prescriptions, according to the study.

A new study throws some shade on the CMS decision to delete pain management from the HCAHPS survey.

Ingram/thinkstock

Dr. Brummett disclosed relationships with Tonix and Neuros Medical. He also holds a patent for peripheral perineural dexmedetomidine. Mr. Syrjamaki and Dr. Dupree received support from Blue Cross Blue Shield of Michigan for their respective roles in the Michigan Value Collaborative. Dr. Waljee is an unpaid consultant for 3MHealth.

[email protected]

On Twitter @whitneymcknight

Chronic obstructive lung disease (COPD) is the third leading cause of death in the United States¹ and a major cause of mortality and morbidity around the world. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) released a new “2017 Report”² with modified recommendations for the diagnosis, management, and prevention of COPD. The report contains several changes that are relevant to the primary care provider that will be outlined below.

Redefining COPD

GOLD’s definition of COPD was changed in its 2017 Report: “COPD is a common, preventable, and treatable disease that is characterized by persistent respiratory symptoms and airflow limitations that are due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases.” The report emphasizes that “COPD may be punctuated by periods of acute worsening of respiratory symptoms, called exacerbations.” Note that the terms “emphysema” and “chronic bronchitis” have been removed in favor of a more comprehensive description of the pathophysiology of COPD. Importantly, the report states that cough and sputum production for at least 3 months in each of 2 consecutive years, previously accepted as diagnostic criteria, are present in only a minority of patients. It is noted that chronic respiratory symptoms may exist without spirometric changes and many patients (usually smokers) have structural evidence of COPD without airflow limitation.

Changes to COPD initial assessment

The primary criterion for diagnosis is unchanged: post-bronchodilator forced expiratory volume in 1 second (FEV₁₎/forced vital capacity (FVC) less than 0.70. Spirometry remains important to confirm the diagnosis in those with classic symptoms of dyspnea, chronic cough, and/or sputum production with a history of exposure to noxious particles or gases.

The GOLD assessment system previously incorporated spirometry and included an “ABCD” system such that patients in group A are least severe. Spirometry has been progressively deemphasized in favor of symptom-based classification and the 2017 Report, for the first time, dissociates spirometric findings from severity classification.

The new system uses symptom severity and exacerbation risk to classify COPD. Two specific standardized COPD symptom measurement tools, The Modified British Medical Research Council (mMRC) questionnaire and COPD Assessment Test (CAT), are reported by GOLD as the most widely used. Low symptom severity is considered an mMRC less than or equal to 1 or CAT less than or equal to 9, high symptom severity is considered an mMRC greater than or equal to 2 or CAT greater than or equal to 10. Low risk of exacerbation is defined as no more than one exacerbation not resulting in hospital admission in the last 12 months; high risk of exacerbation is defined as at least two exacerbations or any exacerbations resulting in hospital admission in the last 12 months. Symptom severity and exacerbation risk is divided into four quadrants:
 

• GOLD group A: Low symptom severity, low exacerbation risk.

• GOLD group B: High symptom severity, low exacerbation risk.

• GOLD group C: Low symptom severity, high exacerbation risk.

• GOLD group D: High symptom severity, high exacerbation risk.

Changes to prevention and management of stable COPD

Smoking cessation remains important in the prevention of COPD. The 2017 Report reflects the U.S. Preventive Services Task Force’s guidelines for smoking cessation: Offer nicotine replacement, cessation counseling, and pharmacotherapy (varenicline, bupropion or nortriptyline). There is insufficient evidence to support the use of e-cigarettes. Influenza and pneumococcal vaccinations are recommended. Pulmonary rehabilitation remains important.

The 2017 Report includes an expanded discussion of COPD medications. The role of short-acting bronchodilators (SABD) in COPD remains prominent. Changes include a stronger recommendation to use combination short-acting beta-agonists and short-acting muscarinic antagonists (SABA/SAMA) as these seem to be superior to SABD monotherapy in improving symptoms and FEV₁.

There were several changes to the pharmacologic treatment algorithm. For the first time, GOLD proposes escalation strategies. Preference is given to LABA/LAMA (long-acting beta-agonist/long-acting muscarinic antagonists) combinations over LABA/ICS (long-acting beta-agonist/inhaled corticosteroid) combinations as a mainstay of treatment. The rationale for this change is that LABA/LAMAs give greater bronchodilation compared with LABA/ICS, and one study showed a decreased rate of exacerbations compared to LABA/ICS in patients with a history of exacerbations. In addition, patients with COPD who receive ICS appear to have a higher risk of developing pneumonia. GOLD recommendations are:

• Group A: Start with single bronchodilator (short- or long-acting), escalate to alternative class of bronchodilator if necessary.

• Group B: Start with LABA or LAMA, escalate to LABA/LAMA if symptoms persist.

• Group C: Start with LAMA, escalate to LABA/LAMA (preferred) or LABA/ICS if exacerbations continue.

• Group D: Start with LABA/LAMA (preferred) or LAMA monotherapy, escalate to LABA/LAMA/ICS (preferred) or try LABA/ICS before escalating to LAMA/LABA/ICS if symptoms persist or exacerbations continue; roflumilast and/or a macrolide may be considered if further exacerbations occur with LABA/LAMA/ICS.
 

Bottom line

1. GOLD classification of COPD severity is now based on clinical criteria alone: symptom assessment and risk for exacerbation.

2. SABA/SAMA combination therapy seems to be superior to either SABA or SAMA alone.

3. Patients in group A (milder symptoms, low exacerbation risk) may be initiated on either short- or long-acting bronchodilator therapy.

4. Patients in group B (milder symptoms, increased exacerbation risk) should be initiated on LAMA monotherapy.

5. LABA/LAMA combination therapy seems to be superior to LABA/ICS combination therapy and should be used when long-acting bronchodilator monotherapy fails to control symptoms or reduce exacerbations.

References

1. CDC MMWR 11/23/12

2. Global Strategy for the Diagnosis, Management and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 at http://goldcopd.org (accessed 3/10/2017)
 

Dr. Skolnik is associate director of the family medicine residency program at Abington (Pa.) Memorial Hospital. Dr. Lent is chief resident in the program.

Chronic obstructive lung disease (COPD) is the third leading cause of death in the United States¹ and a major cause of mortality and morbidity around the world. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) released a new “2017 Report”² with modified recommendations for the diagnosis, management, and prevention of COPD. The report contains several changes that are relevant to the primary care provider that will be outlined below.

Redefining COPD

GOLD’s definition of COPD was changed in its 2017 Report: “COPD is a common, preventable, and treatable disease that is characterized by persistent respiratory symptoms and airflow limitations that are due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases.” The report emphasizes that “COPD may be punctuated by periods of acute worsening of respiratory symptoms, called exacerbations.” Note that the terms “emphysema” and “chronic bronchitis” have been removed in favor of a more comprehensive description of the pathophysiology of COPD. Importantly, the report states that cough and sputum production for at least 3 months in each of 2 consecutive years, previously accepted as diagnostic criteria, are present in only a minority of patients. It is noted that chronic respiratory symptoms may exist without spirometric changes and many patients (usually smokers) have structural evidence of COPD without airflow limitation.

Changes to COPD initial assessment

The primary criterion for diagnosis is unchanged: post-bronchodilator forced expiratory volume in 1 second (FEV₁₎/forced vital capacity (FVC) less than 0.70. Spirometry remains important to confirm the diagnosis in those with classic symptoms of dyspnea, chronic cough, and/or sputum production with a history of exposure to noxious particles or gases.

The GOLD assessment system previously incorporated spirometry and included an “ABCD” system such that patients in group A are least severe. Spirometry has been progressively deemphasized in favor of symptom-based classification and the 2017 Report, for the first time, dissociates spirometric findings from severity classification.

The new system uses symptom severity and exacerbation risk to classify COPD. Two specific standardized COPD symptom measurement tools, The Modified British Medical Research Council (mMRC) questionnaire and COPD Assessment Test (CAT), are reported by GOLD as the most widely used. Low symptom severity is considered an mMRC less than or equal to 1 or CAT less than or equal to 9, high symptom severity is considered an mMRC greater than or equal to 2 or CAT greater than or equal to 10. Low risk of exacerbation is defined as no more than one exacerbation not resulting in hospital admission in the last 12 months; high risk of exacerbation is defined as at least two exacerbations or any exacerbations resulting in hospital admission in the last 12 months. Symptom severity and exacerbation risk is divided into four quadrants:
 

• GOLD group A: Low symptom severity, low exacerbation risk.

• GOLD group B: High symptom severity, low exacerbation risk.

• GOLD group C: Low symptom severity, high exacerbation risk.

• GOLD group D: High symptom severity, high exacerbation risk.

Changes to prevention and management of stable COPD

Smoking cessation remains important in the prevention of COPD. The 2017 Report reflects the U.S. Preventive Services Task Force’s guidelines for smoking cessation: Offer nicotine replacement, cessation counseling, and pharmacotherapy (varenicline, bupropion or nortriptyline). There is insufficient evidence to support the use of e-cigarettes. Influenza and pneumococcal vaccinations are recommended. Pulmonary rehabilitation remains important.

The 2017 Report includes an expanded discussion of COPD medications. The role of short-acting bronchodilators (SABD) in COPD remains prominent. Changes include a stronger recommendation to use combination short-acting beta-agonists and short-acting muscarinic antagonists (SABA/SAMA) as these seem to be superior to SABD monotherapy in improving symptoms and FEV₁.

There were several changes to the pharmacologic treatment algorithm. For the first time, GOLD proposes escalation strategies. Preference is given to LABA/LAMA (long-acting beta-agonist/long-acting muscarinic antagonists) combinations over LABA/ICS (long-acting beta-agonist/inhaled corticosteroid) combinations as a mainstay of treatment. The rationale for this change is that LABA/LAMAs give greater bronchodilation compared with LABA/ICS, and one study showed a decreased rate of exacerbations compared to LABA/ICS in patients with a history of exacerbations. In addition, patients with COPD who receive ICS appear to have a higher risk of developing pneumonia. GOLD recommendations are:

• Group A: Start with single bronchodilator (short- or long-acting), escalate to alternative class of bronchodilator if necessary.

• Group B: Start with LABA or LAMA, escalate to LABA/LAMA if symptoms persist.

• Group C: Start with LAMA, escalate to LABA/LAMA (preferred) or LABA/ICS if exacerbations continue.

• Group D: Start with LABA/LAMA (preferred) or LAMA monotherapy, escalate to LABA/LAMA/ICS (preferred) or try LABA/ICS before escalating to LAMA/LABA/ICS if symptoms persist or exacerbations continue; roflumilast and/or a macrolide may be considered if further exacerbations occur with LABA/LAMA/ICS.
 

Bottom line

1. GOLD classification of COPD severity is now based on clinical criteria alone: symptom assessment and risk for exacerbation.

2. SABA/SAMA combination therapy seems to be superior to either SABA or SAMA alone.

3. Patients in group A (milder symptoms, low exacerbation risk) may be initiated on either short- or long-acting bronchodilator therapy.

4. Patients in group B (milder symptoms, increased exacerbation risk) should be initiated on LAMA monotherapy.

5. LABA/LAMA combination therapy seems to be superior to LABA/ICS combination therapy and should be used when long-acting bronchodilator monotherapy fails to control symptoms or reduce exacerbations.

References

1. CDC MMWR 11/23/12

2. Global Strategy for the Diagnosis, Management and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 at http://goldcopd.org (accessed 3/10/2017)
 

Dr. Skolnik is associate director of the family medicine residency program at Abington (Pa.) Memorial Hospital. Dr. Lent is chief resident in the program.

Chronic obstructive lung disease (COPD) is the third leading cause of death in the United States¹ and a major cause of mortality and morbidity around the world. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) released a new “2017 Report”² with modified recommendations for the diagnosis, management, and prevention of COPD. The report contains several changes that are relevant to the primary care provider that will be outlined below.

Redefining COPD

GOLD’s definition of COPD was changed in its 2017 Report: “COPD is a common, preventable, and treatable disease that is characterized by persistent respiratory symptoms and airflow limitations that are due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases.” The report emphasizes that “COPD may be punctuated by periods of acute worsening of respiratory symptoms, called exacerbations.” Note that the terms “emphysema” and “chronic bronchitis” have been removed in favor of a more comprehensive description of the pathophysiology of COPD. Importantly, the report states that cough and sputum production for at least 3 months in each of 2 consecutive years, previously accepted as diagnostic criteria, are present in only a minority of patients. It is noted that chronic respiratory symptoms may exist without spirometric changes and many patients (usually smokers) have structural evidence of COPD without airflow limitation.

Changes to COPD initial assessment

The primary criterion for diagnosis is unchanged: post-bronchodilator forced expiratory volume in 1 second (FEV₁₎/forced vital capacity (FVC) less than 0.70. Spirometry remains important to confirm the diagnosis in those with classic symptoms of dyspnea, chronic cough, and/or sputum production with a history of exposure to noxious particles or gases.

The GOLD assessment system previously incorporated spirometry and included an “ABCD” system such that patients in group A are least severe. Spirometry has been progressively deemphasized in favor of symptom-based classification and the 2017 Report, for the first time, dissociates spirometric findings from severity classification.

The new system uses symptom severity and exacerbation risk to classify COPD. Two specific standardized COPD symptom measurement tools, The Modified British Medical Research Council (mMRC) questionnaire and COPD Assessment Test (CAT), are reported by GOLD as the most widely used. Low symptom severity is considered an mMRC less than or equal to 1 or CAT less than or equal to 9, high symptom severity is considered an mMRC greater than or equal to 2 or CAT greater than or equal to 10. Low risk of exacerbation is defined as no more than one exacerbation not resulting in hospital admission in the last 12 months; high risk of exacerbation is defined as at least two exacerbations or any exacerbations resulting in hospital admission in the last 12 months. Symptom severity and exacerbation risk is divided into four quadrants:
 

• GOLD group A: Low symptom severity, low exacerbation risk.

• GOLD group B: High symptom severity, low exacerbation risk.

• GOLD group C: Low symptom severity, high exacerbation risk.

• GOLD group D: High symptom severity, high exacerbation risk.

Changes to prevention and management of stable COPD

Smoking cessation remains important in the prevention of COPD. The 2017 Report reflects the U.S. Preventive Services Task Force’s guidelines for smoking cessation: Offer nicotine replacement, cessation counseling, and pharmacotherapy (varenicline, bupropion or nortriptyline). There is insufficient evidence to support the use of e-cigarettes. Influenza and pneumococcal vaccinations are recommended. Pulmonary rehabilitation remains important.

The 2017 Report includes an expanded discussion of COPD medications. The role of short-acting bronchodilators (SABD) in COPD remains prominent. Changes include a stronger recommendation to use combination short-acting beta-agonists and short-acting muscarinic antagonists (SABA/SAMA) as these seem to be superior to SABD monotherapy in improving symptoms and FEV₁.

There were several changes to the pharmacologic treatment algorithm. For the first time, GOLD proposes escalation strategies. Preference is given to LABA/LAMA (long-acting beta-agonist/long-acting muscarinic antagonists) combinations over LABA/ICS (long-acting beta-agonist/inhaled corticosteroid) combinations as a mainstay of treatment. The rationale for this change is that LABA/LAMAs give greater bronchodilation compared with LABA/ICS, and one study showed a decreased rate of exacerbations compared to LABA/ICS in patients with a history of exacerbations. In addition, patients with COPD who receive ICS appear to have a higher risk of developing pneumonia. GOLD recommendations are:

• Group A: Start with single bronchodilator (short- or long-acting), escalate to alternative class of bronchodilator if necessary.

• Group B: Start with LABA or LAMA, escalate to LABA/LAMA if symptoms persist.

• Group C: Start with LAMA, escalate to LABA/LAMA (preferred) or LABA/ICS if exacerbations continue.

• Group D: Start with LABA/LAMA (preferred) or LAMA monotherapy, escalate to LABA/LAMA/ICS (preferred) or try LABA/ICS before escalating to LAMA/LABA/ICS if symptoms persist or exacerbations continue; roflumilast and/or a macrolide may be considered if further exacerbations occur with LABA/LAMA/ICS.
 

Bottom line

1. GOLD classification of COPD severity is now based on clinical criteria alone: symptom assessment and risk for exacerbation.

2. SABA/SAMA combination therapy seems to be superior to either SABA or SAMA alone.

3. Patients in group A (milder symptoms, low exacerbation risk) may be initiated on either short- or long-acting bronchodilator therapy.

4. Patients in group B (milder symptoms, increased exacerbation risk) should be initiated on LAMA monotherapy.

5. LABA/LAMA combination therapy seems to be superior to LABA/ICS combination therapy and should be used when long-acting bronchodilator monotherapy fails to control symptoms or reduce exacerbations.

References

1. CDC MMWR 11/23/12

2. Global Strategy for the Diagnosis, Management and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 at http://goldcopd.org (accessed 3/10/2017)
 

Dr. Skolnik is associate director of the family medicine residency program at Abington (Pa.) Memorial Hospital. Dr. Lent is chief resident in the program.

Men with ADHD had a 38% lower risk of motor vehicle crashes (MVCs) when receiving ADHD medication, compared with months off medication. Women had a 42% lower risk, according to the results of a U.S. study.

Estimates suggested that up to 22% of MVCs in patients with ADHD could have been avoided if they had received medication during the whole length of the study, reported Zheng Chang, PhD, of the Karolinska Institutet, Sweden, and his colleagues (JAMA Psychiatry. 2017 May 10. doi: 10.1001/jamapsychiatry.2017.0659)

mik38/thinkstockphotos.com

[email protected]

Men with ADHD had a 38% lower risk of motor vehicle crashes (MVCs) when receiving ADHD medication, compared with months off medication. Women had a 42% lower risk, according to the results of a U.S. study.

Estimates suggested that up to 22% of MVCs in patients with ADHD could have been avoided if they had received medication during the whole length of the study, reported Zheng Chang, PhD, of the Karolinska Institutet, Sweden, and his colleagues (JAMA Psychiatry. 2017 May 10. doi: 10.1001/jamapsychiatry.2017.0659)

mik38/thinkstockphotos.com

[email protected]

Men with ADHD had a 38% lower risk of motor vehicle crashes (MVCs) when receiving ADHD medication, compared with months off medication. Women had a 42% lower risk, according to the results of a U.S. study.

Estimates suggested that up to 22% of MVCs in patients with ADHD could have been avoided if they had received medication during the whole length of the study, reported Zheng Chang, PhD, of the Karolinska Institutet, Sweden, and his colleagues (JAMA Psychiatry. 2017 May 10. doi: 10.1001/jamapsychiatry.2017.0659)

mik38/thinkstockphotos.com

[email protected]

With improved treatment, patients with multiple myeloma are surviving long enough to develop other cancers, Jorge J. Castillo, MD, and Adam J. Olszewski, MD, reported in a poster to be presented at the annual meeting of the American Society of Clinical Oncology.

The good news is that myeloma patients, when diagnosed with a subsequent solid tumor, are just as likely to respond to treatment and do just as well as patients without myeloma, according to Dr. Castillo of Dana-Farber Cancer Institute, Boston, and Dr. Olszewski of Alpert Medical School of Brown University, Providence, R.I.

[email protected]

On Twitter @maryjodales

With improved treatment, patients with multiple myeloma are surviving long enough to develop other cancers, Jorge J. Castillo, MD, and Adam J. Olszewski, MD, reported in a poster to be presented at the annual meeting of the American Society of Clinical Oncology.

The good news is that myeloma patients, when diagnosed with a subsequent solid tumor, are just as likely to respond to treatment and do just as well as patients without myeloma, according to Dr. Castillo of Dana-Farber Cancer Institute, Boston, and Dr. Olszewski of Alpert Medical School of Brown University, Providence, R.I.

[email protected]

On Twitter @maryjodales

With improved treatment, patients with multiple myeloma are surviving long enough to develop other cancers, Jorge J. Castillo, MD, and Adam J. Olszewski, MD, reported in a poster to be presented at the annual meeting of the American Society of Clinical Oncology.

The good news is that myeloma patients, when diagnosed with a subsequent solid tumor, are just as likely to respond to treatment and do just as well as patients without myeloma, according to Dr. Castillo of Dana-Farber Cancer Institute, Boston, and Dr. Olszewski of Alpert Medical School of Brown University, Providence, R.I.

[email protected]

On Twitter @maryjodales

Presenters

Nilam Soni, MD, FHM; Thomas Conlon, MD; Ria Dancel, MD, FAAP, FHM; Daniel Schnobrich, MD

Summary

Point-of-care ultrasound (POCUS) is rapidly gaining acceptance in the medical community as a goal-directed examination that answers a specific diagnostic question or guides a bedside invasive procedure. Adoption by pediatric hospitalists is increasing, aided by multiple training pathways, opportunities for scholarship, and organization development.

The use of POCUS is increasing among nonradiologist physicians due to the expectation for perfection, desire for improved patient experience, and increased availability of ultrasound machines. POCUS is rapid and safe, and can be used serially to monitor, provide procedural guidance, and lead to initiation of appropriate therapies.

Key takeaways for HM

• Point-of-care ultrasound (POCUS) is rapidly being adopted by pediatric hospitalists.
• Pediatric applications are still being developed, but include guidance of bladder catheterization, identifying occult abscesses, diagnosis of pneumonia/associated effusions, and IV placement.
• Initial training can be provided by pediatric ED physicians/PICU physicians or an on-site commercial course can be arranged for larger groups.
• Relationships with radiologists should be established at the outset to avoid misunderstanding of POCUS.

Dr. Chang is a pediatric hospitalist at Baystate Children’s Hospital and is the pediatric editor of The Hospitalist.

Presenters

Nilam Soni, MD, FHM; Thomas Conlon, MD; Ria Dancel, MD, FAAP, FHM; Daniel Schnobrich, MD

Summary

Point-of-care ultrasound (POCUS) is rapidly gaining acceptance in the medical community as a goal-directed examination that answers a specific diagnostic question or guides a bedside invasive procedure. Adoption by pediatric hospitalists is increasing, aided by multiple training pathways, opportunities for scholarship, and organization development.

The use of POCUS is increasing among nonradiologist physicians due to the expectation for perfection, desire for improved patient experience, and increased availability of ultrasound machines. POCUS is rapid and safe, and can be used serially to monitor, provide procedural guidance, and lead to initiation of appropriate therapies.

Key takeaways for HM

• Point-of-care ultrasound (POCUS) is rapidly being adopted by pediatric hospitalists.
• Pediatric applications are still being developed, but include guidance of bladder catheterization, identifying occult abscesses, diagnosis of pneumonia/associated effusions, and IV placement.
• Initial training can be provided by pediatric ED physicians/PICU physicians or an on-site commercial course can be arranged for larger groups.
• Relationships with radiologists should be established at the outset to avoid misunderstanding of POCUS.

Dr. Chang is a pediatric hospitalist at Baystate Children’s Hospital and is the pediatric editor of The Hospitalist.

Presenters

Nilam Soni, MD, FHM; Thomas Conlon, MD; Ria Dancel, MD, FAAP, FHM; Daniel Schnobrich, MD

Summary

Point-of-care ultrasound (POCUS) is rapidly gaining acceptance in the medical community as a goal-directed examination that answers a specific diagnostic question or guides a bedside invasive procedure. Adoption by pediatric hospitalists is increasing, aided by multiple training pathways, opportunities for scholarship, and organization development.

The use of POCUS is increasing among nonradiologist physicians due to the expectation for perfection, desire for improved patient experience, and increased availability of ultrasound machines. POCUS is rapid and safe, and can be used serially to monitor, provide procedural guidance, and lead to initiation of appropriate therapies.

Key takeaways for HM

• Point-of-care ultrasound (POCUS) is rapidly being adopted by pediatric hospitalists.
• Pediatric applications are still being developed, but include guidance of bladder catheterization, identifying occult abscesses, diagnosis of pneumonia/associated effusions, and IV placement.
• Initial training can be provided by pediatric ED physicians/PICU physicians or an on-site commercial course can be arranged for larger groups.
• Relationships with radiologists should be established at the outset to avoid misunderstanding of POCUS.

Dr. Chang is a pediatric hospitalist at Baystate Children’s Hospital and is the pediatric editor of The Hospitalist.

PHILADELPHIA –  The safety of sentinel lymph node biopsy (SLNB) alone without axillary lymph node dissection (ALND) has been established for patients with cT1-2N0 cancer that are found to have one or two metastatic sentinel lymph nodes who undergo breast conservation therapy, but questions regarding the role of regional radiation have persisted. 
This issue is addressed by the results of a large, prospective, 5+ year study at Memorial Sloan Kettering Cancer Center which confirmed the safety of omitting axillary lymph node dissection and suggested that regional radiation provides minimal benefit. 

PHILADELPHIA –  The safety of sentinel lymph node biopsy (SLNB) alone without axillary lymph node dissection (ALND) has been established for patients with cT1-2N0 cancer that are found to have one or two metastatic sentinel lymph nodes who undergo breast conservation therapy, but questions regarding the role of regional radiation have persisted. 
This issue is addressed by the results of a large, prospective, 5+ year study at Memorial Sloan Kettering Cancer Center which confirmed the safety of omitting axillary lymph node dissection and suggested that regional radiation provides minimal benefit. 

PHILADELPHIA –  The safety of sentinel lymph node biopsy (SLNB) alone without axillary lymph node dissection (ALND) has been established for patients with cT1-2N0 cancer that are found to have one or two metastatic sentinel lymph nodes who undergo breast conservation therapy, but questions regarding the role of regional radiation have persisted. 
This issue is addressed by the results of a large, prospective, 5+ year study at Memorial Sloan Kettering Cancer Center which confirmed the safety of omitting axillary lymph node dissection and suggested that regional radiation provides minimal benefit. 

Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,¹ patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit² and those with acute myocardial infarction (AMI),^3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.⁶ Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,⁷ indicating ambiguity in this care process.

Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.^8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,¹¹ and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources ^12-13 and experience worse outcomes compared to nontransferred patients,¹¹ although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.^14-16

To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.¹⁷ A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.¹⁸ This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.

To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.

We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,¹⁹ which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.

Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.²⁰

Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.

Patient Characteristics

Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;²¹ presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.^11,22

Hospital Characteristics

Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.^23-24

Analysis

Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.

To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.²⁵ Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.

To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25^th and 75^th percentiles, illustrating the range of IHT rates for the middle half of hospitals.

Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).

Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).

After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).

In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.

Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,^3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.¹¹ Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.^5,26 Thus, it is reasonable to presume that many of the beneficiaries

In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.

Disclosure

Nothing to report.

Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,¹ patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit² and those with acute myocardial infarction (AMI),^3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.⁶ Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,⁷ indicating ambiguity in this care process.

Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.^8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,¹¹ and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources ^12-13 and experience worse outcomes compared to nontransferred patients,¹¹ although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.^14-16

To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.¹⁷ A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.¹⁸ This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.

To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.

We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,¹⁹ which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.

Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.²⁰

Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.

Patient Characteristics

Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;²¹ presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.^11,22

Hospital Characteristics

Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.^23-24

Analysis

Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.

To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.²⁵ Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.

To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25^th and 75^th percentiles, illustrating the range of IHT rates for the middle half of hospitals.

Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).

Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).

After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).

In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.

Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,^3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.¹¹ Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.^5,26 Thus, it is reasonable to presume that many of the beneficiaries

In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.

Disclosure

Nothing to report.

Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,¹ patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit² and those with acute myocardial infarction (AMI),^3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.⁶ Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,⁷ indicating ambiguity in this care process.

Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.^8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,¹¹ and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources ^12-13 and experience worse outcomes compared to nontransferred patients,¹¹ although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.^14-16

To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.¹⁷ A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.¹⁸ This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.

To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.

We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,¹⁹ which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.

Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.²⁰

Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.

Patient Characteristics

Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;²¹ presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.^11,22

Hospital Characteristics

Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.^23-24

Analysis

Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.

To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.²⁵ Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.

To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25^th and 75^th percentiles, illustrating the range of IHT rates for the middle half of hospitals.

Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).

Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).

After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).

In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.

Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,^3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.¹¹ Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.^5,26 Thus, it is reasonable to presume that many of the beneficiaries

In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.

Disclosure

Nothing to report.