Predicting readmission risk following percutaneous coronary intervention at the time of admission

Abstract

Objective: To investigate whether a prediction model based on data available early in percutaneous coronary intervention (PCI) admission can predict the risk of readmission.

Background: Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after PCI early in a hospitalization would enable hospitals to enhance discharge planning.

Methods: We developed 3 different models to predict 30-day inpatient readmission to our institution for patients who underwent PCI between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from CathPCI Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent PCI between May 2013 and September 2015.

Results: Our cohort included 6717 PCI patients; 3739 in the derivation cohort and 2978 in the validation cohort. The discriminative ability of the admission model was good (C-index of 0.727). The c-indices for the discharge and cath PCI models were slightly better. (C-index of 0.751 and 0.752 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.720, 0.739 and 0.741 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.703, 0.725 and 0.719 respectively).

Conclusion: Simple models based on available demographic and clinical data may be sufficient to identify patients at highest risk of readmission following PCI early in their hospitalization.

Keywords: Percutaneous coronary intervention; Readmission; Risk prediction.