An EMR-wide machine learning model using the Naïve Bayes algorithm predicted 30-day hospital readmission in heart failure patients with an AUC of 0.78 and an accuracy of 83.19%.
Cohort (n=1,068)
No
Does an EMR-wide machine learning model improve the prediction of 30-day hospital readmission in heart failure patients compared to existing models?
An EMR-wide machine learning model using over 4,000 variables demonstrated encouraging predictive accuracy (AUC 0.78) for 30-day readmission in heart failure patients.
Effect estimate: AUC 0.78
Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6-0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such datadriven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.
Shameer et al. (Tue,) conducted a cohort in Heart failure (n=1,068). EMR-wide machine learning predictive model (Naïve Bayes) vs. Existing predictive models was evaluated on 30-day readmission (AUC 0.78). An EMR-wide machine learning model using the Naïve Bayes algorithm predicted 30-day hospital readmission in heart failure patients with an AUC of 0.78 and an accuracy of 83.19%.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: