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Diagnoses and Timing of 30-day Readmissions After Hospitalization for Heart Failure, Acute Myocardial Infarction, or Pneumonia

Overview
Journal JAMA
Specialty General Medicine
Date 2013 Jan 24
PMID 23340637
Citations 404
Authors
Affiliations
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Abstract

Importance: To better guide strategies intended to reduce high rates of 30-day readmission after hospitalization for heart failure (HF), acute myocardial infarction (MI), or pneumonia, further information is needed about readmission diagnoses, readmission timing, and the relationship of both to patient age, sex, and race.

Objective: To examine readmission diagnoses and timing among Medicare beneficiaries readmitted within 30 days after hospitalization for HF, acute MI, or pneumonia.

Design, Setting, And Patients: We analyzed 2007-2009 Medicare fee-for-service claims data to identify patterns of 30-day readmission by patient demographic characteristics and time after hospitalization for HF, acute MI, or pneumonia. Readmission diagnoses were categorized using an aggregated version of the Centers for Medicare & Medicaid Services' Condition Categories. Readmission timing was determined by day after discharge.

Main Outcome Measures: We examined the percentage of 30-day readmissions occurring on each day (0-30) after discharge; the most common readmission diagnoses occurring during cumulative periods (days 0-3, 0-7, 0-15, and 0-30) and consecutive periods (days 0-3, 4-7, 8-15, and 16-30) after hospitalization; median time to readmission for common readmission diagnoses; and the relationship between patient demographic characteristics and readmission diagnoses and timing.

Results: From 2007 through 2009, we identified 329,308 30-day readmissions after 1,330,157 HF hospitalizations (24.8% readmitted), 108,992 30-day readmissions after 548,834 acute MI hospitalizations (19.9% readmitted), and 214,239 30-day readmissions after 1,168,624 pneumonia hospitalizations (18.3% readmitted). The proportion of patients readmitted for the same condition was 35.2% after the index HF hospitalization, 10.0% after the index acute MI hospitalization, and 22.4% after the index pneumonia hospitalization. Of all readmissions within 30 days of hospitalization, the majority occurred within 15 days of hospitalization: 61.0%, HF cohort; 67.6%, acute MI cohort; and 62.6%, pneumonia cohort. The diverse spectrum of readmission diagnoses was largely similar in both cumulative and consecutive periods after discharge. Median time to 30-day readmission was 12 days for patients initially hospitalized for HF, 10 days for patients initially hospitalized for acute MI, and 12 days for patients initially hospitalized for pneumonia and was comparable across common readmission diagnoses. Neither readmission diagnoses nor timing substantively varied by age, sex, or race.

Conclusion And Relevance: Among Medicare fee-for-service beneficiaries hospitalized for HF, acute MI, or pneumonia, 30-day readmissions were frequent throughout the month after hospitalization and resulted from a similar spectrum of readmission diagnoses regardless of age, sex, race, or time after discharge.

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References
1.
Chaudhry S, Mattera J, Curtis J, Spertus J, Herrin J, Lin Z . Telemonitoring in patients with heart failure. N Engl J Med. 2010; 363(24):2301-9. PMC: 3237394. DOI: 10.1056/NEJMoa1010029. View

2.
Birman-Deych E, Waterman A, Yan Y, Nilasena D, Radford M, Gage B . Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care. 2005; 43(5):480-5. DOI: 10.1097/01.mlr.0000160417.39497.a9. View

3.
Riegel B, Carlson B, Glaser D, Romero T . Randomized controlled trial of telephone case management in Hispanics of Mexican origin with heart failure. J Card Fail. 2006; 12(3):211-9. DOI: 10.1016/j.cardfail.2006.01.005. View

4.
Ashton C, Del Junco D, Souchek J, Wray N, Mansyur C . The association between the quality of inpatient care and early readmission: a meta-analysis of the evidence. Med Care. 1997; 35(10):1044-59. DOI: 10.1097/00005650-199710000-00006. View

5.
Felker G, Lee K, Bull D, Redfield M, Stevenson L, Goldsmith S . Diuretic strategies in patients with acute decompensated heart failure. N Engl J Med. 2011; 364(9):797-805. PMC: 3412356. DOI: 10.1056/NEJMoa1005419. View