Abstract
Background: Heart failure (HF) patients have a high readmission rate with approximately 20% of patients being readmitted within 30-days after discharge. Hospital interventions to reduce HF readmissions are resource- and effort-intensive. Widespread availability of electronic medical record data has spurred interest in using machine learning-based techniques for risk stratification of heart failure patients. The predictive performance of machine learning-based predictive models is often evaluated solely using the Area Under the Receiver Operating Characteristic (AUROC) curve. However, the AUROC is independent of prevalence therefore predictive models with the same AUROC can have differential clinical utility. Furthermore, the AUROC does not provide any insight about the presence of overfitting or decay in predictive performance of a model over time, both of which can affect its real-world performance.
Highlights
Heart failure (HF) patients have a high readmission rate with approximately 20% of patients being readmitted within 30-days after discharge
The predictive performance of machine learning-based predictive models is often evaluated solely using the Area Under the Receiver Operating Characteristic (AUROC) curve
The AUROC is independent of prevalence predictive models with the same AUROC can have differential clinical utility
Summary
Heart failure (HF) patients have a high readmission rate with approximately 20% of patients being readmitted within 30-days after discharge. Prospective Real-World Performance Evaluation of a Machine Learning Algorithm to Predict 30-Day Readmissions in Patients with Heart Failure Using Electronic Medical Record Data Sujay S Kakarmath1,2,3*, MBBS, MS; Neda Derakhshani1*, MSc; Sara B Golas1*, MA; Jennifer Felsted1,3*, PhD; Takuma Shibahara4*, PhD; Hideo Aoki4*; Mika Takata5*; Ken Naono4*, PhD; Joseph Kvedar1,2,3*, MD; Kamal Jethwani1,2,3*, MD, MPH; Stephen Agboola1,2,3*, MD, MPH
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