Heart rate is an important predictor of pacing induced cardiomyopathy in a machine learning analysis

Abstract Background 10-20% of patients who have right ventricular pacing develop pacing induced cardiomyopathy (PICM). Home monitoring (HM) could identify patients developing PICM in real-time and facilitate echocardiographic confirmation, medication optimisation and if necessary cardiac resynchronisation therapy (CRT). Purpose To identify predictors of PICM using machine learning models. Methods Consecutive cases of patients who developed PICM (12) after dual chamber pacing were identified from our implant database and matched 4:1 with propensity matched controls who underwent pacing but did not develop PICM (48) (Table 1.) HM data from the 6 months prior to echocardiographic diagnosis of PICM was analysed. PICM was defined as (1) a deterioration in left ventricular (LV) ejection fraction (EF) of 10%, (2) to an EF of 45%, (3) with right ventricular (RV) pacing 20% and (4) the exclusion of alternative causes. Three types of ML models were generated (1) random forest, (2) extreme gradient boosted (XGBoost) (3) K-nearest neighbours algorithm (KNN), with 10-fold cross-validation. Results HM data were available for 12 PICM cases (75 ± 8 years, 72% male). PICM occurred at 2.0 ± 1.2 years. There was no difference between cases and controls in atrial pacing (8±15% vs 18±22% p=0.14), ventricular pacing (81±29% vs 85±24% p=0.62) or base rate (53±5bpm vs 53±5bpm p=0.80). Of the 34 RM features analysed, mean ventricular heart rate was identified as the most important predictor of PICM; area under curve with XG Boost 0.95, KNN 0.95, and random forest 0.86. Other RM features failed to add any more predictive power to the models. Mean ventricular rate was significantly higher in cases than controls (80±6 bpm vs 69±9 bpm p0.001). Sensitivity analysis was performed to assess whether episodes of atrial fibrillation (AF) were responsible for the higher ventricular rates. After days of AF days were excluded, mean ventricular rate was still significantly higher in cases than controls (79± 6vs 69±9bpm p0.001) (Figure 1). 10/12 patients had a subsequent CRT upgrade. No consistent pattern emerged in heart rate changes in the first 3 months after CRT upgrade. Conclusion In a group of patients with normal left ventricular function at implant, remotely monitored heart rate can predict early-onset PICM. Further work is required to understand when these heart rate differences emerge, and whether they improve with positive LV remodelling.Table 1.Figure 1.