Predictive Model for Heart Failure Readmission Using Nationwide Readmissions Database

Abstract

Objective: To generate a heart failure (HF) readmission prediction model using the Nationwide Readmissions Database to guide management and reduce HF readmissions. Patients and Methods: A retrospective analysis was performed for patients listed for HF admissions in the Nationwide Readmissions Database from January 1, 2010, to December 31, 2014. A Cox proportional hazards model for sample survey data for the prediction of readmission for all patients with HF was implemented using a derivation cohort (2010-2012). We generated receiver operating characteristic (ROC) curves and estimated area under the ROC curve at each time point (30, 60, 90, and 180 days) to assess the accuracy of our predictive model using the derivation cohort (2010-2012) and compared it with the validation cohort (2013-2014). A risk score was computed for the validation cohort. On the basis of the total risk score, we calculated the probability of readmission at 30, 60, 90, and 180 days. Results: Approximately 1,420,564 patients were admitted for HF, contributing to 1,817,735 total HF admissions. Of these, 665,867 patients had at least 1 readmission for HF. The 10 most common comorbidities for readmitted patients included hypertension, diabetes mellitus, renal failure, chronic pulmonary disease, deficiency anemia, fluid and electrolyte disorders, obesity, hypothyroidism, peripheral vascular disorders, and depression. The area under the ROC curve for the prediction model was 0.58 in the derivation cohort and 0.59 in the validation cohort. Conclusion: The prediction model will find clinical utility at point of care in optimizing the management of patients with HF and reducing HF readmissions.