Machine Learning-Based 30-Day Readmission Prediction Models for Patients with Heart Failure: A Systematic Review.

Abstract

Heart failure (HF) is one of the most frequent diagnoses for 30-day readmission after hospital discharge. Nurses have role in reducing unplanned readmission and providing quality of care during HF trajectories. This systematic review assessed the quality and significant factors of machine learning (ML)-based 30-day HF readmission prediction models. Eight academic and electronic databases were searched to identify all relevant articles published between 2013 and 2023. Thirteen studies met our inclusion criteria. The sample sizes of the selected studies ranged from 1,778 to 272,778 patients, and patients' average age ranged from 70 to 81 years. Quality appraisal was performed. The most commonly used ML approaches were Random Forest and XGBoost. The 30-day HF readmission rates ranged from 1.2% to 39.4%. The area under the receiver operating characteristic curve for models predicting 30-day HF readmission were between 0.51 and 0.93. Significant predictors included sixty variables with nine categories (socio-demographics, vital signs, medical history, therapy, echocardiographic findings, prescribed medications, laboratory results, comorbidities, and hospital performance index). Future studies using ML algorithms should evaluate the predictive quality of the factors associated with 30-day HF readmission presented in this review, considering different healthcare systems and type of HF. More prospective cohort studies with combining structured and unstructured data are required to improve the quality of ML based prediction model, which may help nurses and other healthcare professionals assess early and accurate 30-day HF readmission predictions and plan individualized care after hospital discharge. This study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD 42023455584).