Machine Learning Classification of Pacemaker Low-Longevity Status Using Device Interrogation Reports

Pacemaker generator replacement remains clinically important because battery depletion influences the timing of elective replacement procedures and associated procedural risk. This study investigated whether routinely available pacemaker interrogation-derived telemetry can classify devices with manufacturer-estimated low longevity status, defined as remaining device life below 12 months. A total of 39 Medtronic pacemaker interrogation snapshots were analyzed, including 11 single-chamber, 21 dual-chamber, and 7 triple-chamber CRT-P devices. Four machine learning classifiers, namely, Naïve Bayes, HistGradientBoosting, Logistic Regression, and Random Forest, were evaluated using leave-one-out cross-validation as the primary internal validation strategy, with bootstrap 95% confidence intervals calculated from aggregated out-of-fold predictions. The study is application-based rather than algorithmic, focusing on transparent comparison and interpretation of established classifiers for pacemaker-specific low-longevity classification. Logistic Regression achieved the strongest LOOCV performance, with ROC-AUC 0.941, accuracy 0.872, precision 0.958, recall 0.852, and F1-score 0.902. Random Forest also showed favorable performance, with ROC-AUC 0.855 and F1-score 0.873. Parsimonious baseline analysis showed that months since implantation plus battery voltage achieved ROC-AUC 0.883, accuracy 0.846, and F1-score 0.875, approaching the full Logistic Regression model. SHAP analysis identified months since implantation, battery voltage, and right ventricular capture threshold as the most influential predictors. These findings suggest that routine interrogation variables can classify manufacturer-estimated low-longevity status, but the incremental benefit of full multivariable machine learning over simple predictors was modest. Because the endpoint was based on manufacturer-estimated longevity rather than observed clinical battery failure or generator replacement timing, larger longitudinal and multi-manufacturer validation studies are needed before clinical application.