PO-03-076 MACHINE LEARNING METHODS IDENTIFYING RISK FOR 30-DAY HEART FAILURE READMISSION USING DIAGNOSTICS PARAMETERS FROM IMPLANTED CARDIOVASCULAR DEVICES

Abstract

Heart failure (HF) related diagnostics parameters measured daily from implantable cardioverter defibrillators (ICD) and cardiac resynchronization therapy defibrillators (CRTD) have shown to change before, during and after HF hospitalization events. We assessed the ability to identify patients at risk of a recurrent HF admission with different machine learning techniques leveraging statistical and temporal behavior of the cardiac compass parameters prior and during HF hospitalization events. We linked Optum® deidentified Electronic Health Record dataset during the period from 2007-2021 to the Medtronic CareLinkTM data warehouse with device based continuous diagnostic monitoring data. Patients with ICD/CRTD implants with intra-thoracic impedance diagnostic feature were included for this study. Retrospective analysis was completed identifying HF admissions with diagnostic parameters from 30-days prior admission day ending on discharge day. Analyzed events included such HF admissions within the 2 to 30 days for length of stay. Admission day parameters were evaluated with the following criteria: 7-days to 30-days activity, night heart rate and day heart rate ratios, plus 3-day impedance change. In addition, diagnostic data during hospitalization was summarized into range, median, mean, and standard deviation. Length of stay was also used as an input for the models. Two groups were created based on hospitalization events with or without 30-day readmission. Dataset was split into training and validation subsets. Multiple machine learning models were trained to classify events indicating those followed by a close HF readmission. Models’ results were compared in terms of sensitivity, specificity, and false positive rates on validation set. 1745 HF hospitalization events were assessed in this study, including 486 preceding a re-admission event within 30-days post discharge. The average length of stay on this cohort was 6.2 days. Seven machine learning models were trained using 28 features derived from diagnostic data and length of stay as input. Models’ sensitivity and specificity ranged from 0.11 to 0.84 and 0.89 to 0.93 respectively. Table 1 depicts area under the curve (AUC) for each trained model. Mean model accuracy on validation data was 0.84. A machine learning derived HF diagnostic criteria evaluating diagnostic parameters on HF admission day plus while in hospital may be useful to identify patients at higher risk of 30-days HF readmission at time of discharge.Tabled 1Table 1. AUC for each Classification ModelMODEL NAMEAUCLogistic Regression0.92Decision Tree0.82RUSBoosted Trees0.94Support Vector Machine0.83Bi-layered Neural Network0.86Three-layered Neural Network0.86Optimizable Neural Network0.94 Open table in a new tab