A prediction model to identify patients at high risk for 30-day readmission after percutaneous coronary intervention

Citation:

Jason H Wasfy, Kenneth Rosenfield, Katya Zelevinsky, Rahul Sakhuja, Ann Lovett, John A Spertus, Neil J Wimmer, Laura Mauri, Sharon-Lise T Normand, and Robert W Yeh. 2013. “A prediction model to identify patients at high risk for 30-day readmission after percutaneous coronary intervention.” Circ Cardiovasc Qual Outcomes, 6, 4, Pp. 429-35.

Abstract:

BACKGROUND: 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). CONCLUSIONS: 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.