Jason H. Wasfy, MD, MPhil, Kenneth Rosenfield, MD, Katya Zelevinsky, BA, Rahul Sakhuja, MD, Ann Lovett, RN, MA, John A. Spertus, MD, MPH, Neil J. Wimmer, MD, MSc, Laura Mauri, MD, MSc, Sharon-Lise T. Normand, PhD and Robert W. Yeh, MD, MSc. Circulation: Cardiovascular Quality and Outcomes. 2013; 6: 429-435 Published online before print July 2, 2013, doi: 10.1161/CIRCOUTCOMES.111.000093
Correspondence to Robert W. Yeh, MD, MSc, Cardiology Division, GRB 8–843, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114. E-mail firstname.lastname@example.org
The Excel file available for download below contains four tabs for the estimated regression coefficients, corresponding standard errors, 95% confidence intervals, and covariance matrices for the pre-PCI and discharge model set of predictors used in the paper. The estimates provided are based on the full cohort and will differ from those reported in the published paper.
The data tabs included:
- Pre-PCI Estimates
- Pre-PCI Covariance Matrix
- Discharge Model Estimates
- Discharge Model Covariance Matrix
The variables used in the paper were developed using mostly American College of Cardiology National Cardiovascular Data Registry (ACC-NCDR) CathPCI version 3 data collection instrument variable definitions. The Insurance categories were defined using the Massachusetts Center for Health Information and Analysis Acute Hospital Case Mix Databases (CHIA-AHCMD) for inpatient discharges. The ACC-NCDR data collection form and definitions are avaible at, http://cvquality.acc.org/NCDR-Home/Data-Collection/What-Each-Registry-Collects.aspx . The CHIA-AHCMD data definitions are available at http://www.chiamass.gov/case-mix-data/.
The Affordable Care Act creates financial incentives for hospitals to minimize readmissions shortly after discharge for several conditions, with percutaneous coronary intervention (PCI) to be a target in 2015. We aimed to develop and validate prediction models to assist clinicians and hospitals in identifying patients at highest risk for 30-day readmission after PCI.
Methods and Results
We identified all readmissions within 30 days of discharge after PCI in nonfederal hospitals in Massachusetts between October 1, 2005, and September 30, 2008. Within a two-thirds random sample (Developmental cohort), we developed 2 parsimonious multivariable models to predict all-cause 30-day readmission, the first incorporating only variables known before cardiac catheterization (pre-PCI model), and the second incorporating variables known at discharge (Discharge model). Models were validated within the remaining one-third sample (Validation cohort), and model discrimination and calibration were assessed. Of 36 060 PCI patients surviving to discharge, 3760 (10.4%) patients were readmitted within 30 days. Significant pre-PCI predictors of readmission included age, female sex, Medicare or State insurance, congestive heart failure, and chronic kidney disease. Post-PCI predictors of readmission included lack of β-blocker prescription at discharge, post-PCI vascular or bleeding complications, and extended length of stay. Discrimination of the pre-PCI model (C-statistic=0.68) was modestly improved by the addition of post-PCI variables in the Discharge model (C-statistic=0.69; integrated discrimination improvement, 0.009; P<0.001).
These prediction models can be used to identify patients at high risk for readmission after PCI and to target high-risk patients for interventions to prevent readmission.