Predicting readmission risk following coronary artery bypass surgery at the time of admission.

Background: Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning.

Methods: We developed different models to predict 30-day inpatient readmission to our institution in patients who underwent CABG between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from STS 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 CABG between May 2013 and September 2015. Our cohort included 1277 CABG patients: 1159 in the derivation cohort and 1018 in the validation cohort.

Results: The discriminative ability of the admission model was reasonable (C-index of 0.673). The c-indices for the discharge and STS models were slightly better. (C-index of 0.700 and 0.714 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.641, 0.659 and 0.670 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.573, 0.605 and 0.595 respectively).

Conclusions: Risk prediction models based on data available early on admission are predictive for readmission risk. Adding registry data did not improved the performance of these models. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitalization.