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Quality of Care for Decompensated Heart Failure: Comparable Performance between Academic Hospitalists and Non-hospitalists

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Abstract

Background

Hospitalists improve efficiency, but little information exists regarding whether they impact quality of care.

Objective

To determine hospitalists’ effect on the quality of acute congestive heart failure care.

Design and Participants

Using data from the Multicenter Hospitalist Study, we retrospectively evaluated quality of care in patients admitted with congestive heart failure who were assigned to hospitalists (n = 120) or non-hospitalists (n = 252) among six academic hospitals.

Measurements

Quality measures included the percentage of patients who had ejection fraction (EF) measurement, received appropriate medications [i.e., angiotensin-converting enzyme inhibitor (ACE-I) or beta-blockers] at discharge, measures of care coordination (e.g., follow-up within 30 days), testing for cardiac ischemia (e.g., cardiac catheterization), as well as hospital length of stay, cost, and combined 30-day readmissions and mortality.

Results

Compared to non-hospitalist physicians, hospitalists’ patients had similar rates of EF measurement (85.3% vs. 87.5%; P = 0.57), ACE-I (91.5% vs. 88.0%; P = 0.52), or beta-blocker (46.9% vs. 42.1%; P = 0.57) prescriptions. Multivariable adjustment did not change these findings. Hospitalists’ patients had higher odds of 30-day follow-up (adjusted OR = 1.83, 95% CI, 1.44 – 2.93). There were no significant differences between the groups’ frequency of cardiac testing, length of stay, costs, or risk for readmission or death by 30-days.

Conclusion

Academic hospitalists and non-hospitalists provide similar quality of care for heart failure patients, although hospitalists are paying more attention to longitudinal care. Future efforts to improve quality of care in decompensated heart failure may require attention towards system-level factors.

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Acknowledgment

The authors would like to thank Eric Vittinghoff, PhD, for providing expert advice on the statistical methods used in this analysis. Funding for the Multicenter Hospitalist Study was supported by grant R01 HS10597 AHRQ from the Agency for Healthcare Research and Quality.

Dr. Auerbach is supported by a K08 research and training grant (K080 HS11416-02) from the Agency for Healthcare Research and Quality. Dr. Kaboli is supported by a Research Career Development Award from the Health Services Research and Development Service, Department of Veterans Affairs (RCD 03-033-1). The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Dr. Wetterneck is supported by a Clinical Research Scholars Award from the National Institutes of Health (1 K12-RR01764-01). Data were presented at the Society of General Internal Medicine Annual Meeting on 26 April 2007.

The Multicenter Hospitalist Study was registered at Clinicaltrials.gov: NCT00204048.

Conflict of Interest

None disclosed.

Author information

Authors and Affiliations

  1. Division of Hospital Medicine, University of California San Francisco, San Francisco, CA, USA

    Eduard E. Vasilevskis MD & Andrew D. Auerbach MD MPH

  2. Division of General Internal Medicine, University of California San Francisco, San Francisco, CA, USA

    Eduard E. Vasilevskis MD

  3. University of Chicago, Chicago, IL, USA

    David Meltzer MD PhD, Vineet Arora MD & James Zhang PhD

  4. Brigham and Women’s Hospital, Boston, MA, USA

    Jeffrey Schnipper MD MPH

  5. The Center for Research in the Implementation of Innovative Strategies in Practice (CRIISP) at the Iowa City VA Medical Center, Iowa City, IA, USA

    Peter Kaboli MD

  6. Carver College of Medicine, University of Iowa, Iowa City, IA, USA

    Peter Kaboli MD

  7. School of Medicine and Public Health and the Center for Quality, Improvement and Productivity, University of Wisconsin, Madison, WI, USA

    Tosha Wetterneck MD

  8. Department of Medicine, University of New Mexico, Albuquerque, NM, USA

    David Gonzales MD

Authors
  1. Eduard E. Vasilevskis MD
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  2. David Meltzer MD PhD
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  3. Jeffrey Schnipper MD MPH
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  4. Peter Kaboli MD
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  5. Tosha Wetterneck MD
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  6. David Gonzales MD
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  7. Vineet Arora MD
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  8. James Zhang PhD
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  9. Andrew D. Auerbach MD MPH
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Corresponding author

Correspondence to Eduard E. Vasilevskis MD.

Appendices

Appendix 1

Table 5

Table 5 Congestive Heart Failure International Classification of Diseases (ICD-9) Codes and Descriptions
Full size table

Appendix 2: Variables Tested for Inclusion for Multivariable Analyses

Enrollment site, six sites

Date of admission, dichotomized: 06/01/01–06/15/02 vs. 06/16/02–06/30/03

Cardiology consultation utilization*

Patient demographic and socioeconomic variables

  • Age, continuous

  • Sex

  • Race, dichotomized: white vs. non-white

  • Education, dichotomized: ≤high school vs. >high school

  • Salary, three categories: $0–25,000, 25,001–50,000, and >50,000

  • Insurance status, three categories: Medicare/private insurance, Medicaid, and uninsured

  • Residence status: private residence vs. group/nursing home

Self-reported medical history

  • Coronary artery disease, angina, or congestive heart failure

  • Chronic obstructive pulmonary disease/asthma

  • Stroke

  • Diabetes

  • Hypertension

  • Anemia

  • Cancer diagnosed within the last 3 years

  • Depression

  • Kidney disease

Administrative Charlson comorbidity score, continuous

Cardiac medications used prior to admission:

  • Digoxin

  • Diuretics

  • Angiotensin converting enzyme inhibitor or angiotensin receptor blocker

  • Beta-blocker

  • Spironolactone

  • Coumadin

  • Aspirin

  • Statin

Total number of cardiac medications, continuous

Baseline cardiac or heart failure characteristics

  • History of prior myocardial infarction

  • Hospitalized in the past 12 months for CHF

  • New York Heart Classifications, dichotomized: I and II vs. III and IV

  • Lowest documented left ventricular function, dichotomized: normal to mild vs. moderate to severe

Physiology parameters in the first 24 h of admission

  • Highest creatinine, continuous and dichotomized ≥1.5

  • Highest potassium

  • Lowest sodium

  • Lowest systolic blood pressure, continuous and dichotomized ≤90

  • Highest systolic blood pressure

  • Highest heart rate

  • Lowest hemoglobin

Physiology parameters in the final 48 h prior to discharge†

  • Highest creatinine, continuous and dichotomized ≥1.5

  • Highest potassium

  • Lowest sodium

  • Lowest systolic blood pressure, continuous and dichotomized ≤90

  • Highest systolic blood pressure

  • Highest heart rate

  • Lowest hemoglobin

Patient-identified prior utilization of a primary care provider

*Cardiology consultation: not used for multivariable model predicting cardiology consultation

†Used for multivariable models predicting the following: LV ejection fraction assessment, ACE-I/ARB at discharge, beta-blocker at discharge, 30-day scheduled follow-up

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Vasilevskis, E.E., Meltzer, D., Schnipper, J. et al. Quality of Care for Decompensated Heart Failure: Comparable Performance between Academic Hospitalists and Non-hospitalists. J GEN INTERN MED 23, 1399–1406 (2008). https://doi.org/10.1007/s11606-008-0680-3

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  • DOI: https://doi.org/10.1007/s11606-008-0680-3

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