Reliability of Readmission Rates as a Hospital Quality Measure in Cardiac Surgery

Center for Healthcare Outcomes and Policy, University of Michigan, Ann Arbor, Michigan
The Annals of thoracic surgery (Impact Factor: 3.85). 02/2014; 97(4). DOI: 10.1016/j.athoracsur.2013.11.048
Source: PubMed


Recent policy interventions have reduced payments to hospitals with higher-than-predicted risk-adjusted readmission rates. However, whether readmission rates reliably discriminate deficiencies in hospital quality is uncertain. We sought to determine the reliability of 30-day readmission rates after cardiac operations as a measure of hospital performance and evaluate the effect of hospital caseload on reliability.
We examined national Medicare beneficiaries undergoing coronary artery bypass graft operations for 2006 to 2008 (n = 244,874 patients, n = 1,210 hospitals). First, we performed multivariable logistic regression examining patient factors to calculate a risk-adjusted readmission rate for each hospital. We then used hierarchical modeling to estimate the reliability of this quality measure for each hospital. Finally, we determined the proportion of total variation attributable to three factors: true signal, statistical noise, and patient factors.
A median of 151 (25% to 75% interquartile range, 79 to 265) coronary artery bypasses were performed per hospital during the 3-year period. The median risk-adjusted 30-day readmission rate was 17.6% (25% to 75% interquartile range, 14.4% to 20.8%). Of the variation in readmission rates, 55% was explained by measurement noise, 4% could be attributed to patient characteristics, and the remaining 41% represented true signal in readmission rates. Only 53 hospitals (4.4%) achieved a proficient level of reliability exceeding 0.70. To achieve this reliability, 599 cases were required during the 3-year period. In 33.7% of hospitals, a moderate degree of reliability exceeding 0.5 was achieved, which required 218 cases.
The vast majority of hospitals do not achieve a minimum acceptable level of reliability for 30-day readmission rates. Despite recent enthusiasm, readmission rates are not a reliable measure of hospital quality in cardiac surgery.

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  • Surgery 07/2014; 156(5). DOI:10.1016/j.surg.2014.05.024 · 3.38 Impact Factor
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    ABSTRACT: Objectives: Reducing hospital readmissions is a national priority, with coronary artery bypass graft (CABG) surgery slated for upcoming reimbursement decisions. Clear understanding of the elements associated with readmissions is essential for developing a coherent prevention strategy. Patterns of readmission vary considerably based on diagnosis. We therefore sought to clarify the factors most clearly associated with 30-day readmission following CABG surgery in an academically affiliated community hospital network. Methods: All patients undergoing isolated CABG in an 11-hospital network from 2007 to 2011 were entered into a Society of Thoracic Surgeons (STS) compliant registry that tracks hospital readmission within 30 days of surgery. Data were split at random into training and validation groups that were used to create and validate a logistic regression model of pre-, intra-, and postoperative factors associated with readmission. Subanalyses included development of logistic models predicting readmission for the 2 largest institutions individually, and relatedness of readmission to CABG procedure. Results: The readmission rate for the entire 4861 patient group was 9.2% and varied between hospitals from 6.1% to 18.0%. Factors associated with readmission were moderate chronic obstructed pulmonary disease (odds ratio [OR], 1.81; 95% confidence interval [CI], 1.04-3.14; P = .036), cerebrovascular disease (OR, 1.56; 95% CI, 1.09-2.24; P = .016), diabetes (OR, 1.44; 95% CI, 1.08-1.93; P = .014), congestive heart failure (OR, 2.12; 95% CI, 1.23-3.66; P = .007), intra-aortic balloon pump (OR, 0.40; 95% CI, 0.19-0.83; P = .015), and use of blood products (OR, 1.76; 95% CI, 1.31-2.37; P = .0002). Although the c statistic for the training model (n = 2341) was 0.643, when applied to the validation dataset (n = 2520) the area under the receiver operating curve was reduced to 0.57. Separate analyses of factors for the 2 largest hospitals revealed marked differences, with only body mass index (OR, 1.08; 95% CI, 1.04-1.12; P = .0001) significantly associated with readmission at 1 hospital, and discharge to extended care (OR, 2.11; 95% CI, 1.02-4.33; P = .043) and renal failure (OR, 2.64; 95% CI, 1.21-5.76; P = .0149) significant at the other hospital. Most readmissions (60.8%) occurred within 10 days of discharge. Nearly one-third (31.3%) were categorized as unlikely to be CABG-related. The mean number of days from surgery to readmission was less for readmissions clearly related to CABG (15.5 ± 6.4 days), compared with those unlikely to be CABG-related (17.4 ± 7.0 days) (P = .05). Conclusions: Analysis of CABG readmission data from a network of community hospitals that vary in size and patient demographic characteristics suggests that there are many nonclinical factors influencing readmission; readmission rates and associated risk factors may vary considerably between centers; earlier readmissions are more likely to be procedure-related than patient-related; and therefore, considerable caution should be exercised in attempting to apply uniform standards or strategies to address post-CABG readmission.
    Journal of Thoracic and Cardiovascular Surgery 09/2014; 149(3). DOI:10.1016/j.jtcvs.2014.08.059 · 4.17 Impact Factor

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