Automatic ECG-based detection of left ventricular hypertrophy and its predictive value in haemodialysis patients

Abstract

Objective. Left ventricular hypertrophy (LVH) is one of the most severe risk factors in patients with end-stage kidney disease (ESKD) regarding all-cause and cardiovascular mortality. It contributes to the risk of sudden cardiac death which accounts for approximately 25% of deaths in ESKD patients. Electrocardiography (ECG) is the least expensive way to assess whether a patient has LVH, but manual annotation is cumbersome. Thus, an automated approach has been developed to derive ECG-based LVH parameters. The aim of the current study is to compare automatic to manual measurements and to investigate their predictive value for cardiovascular and all-cause mortality. Approach. From the 12-lead 24 h ECG measurements of 301 ESKD patients undergoing haemodialysis, three different LVH parameters were calculated. Peguero-Lo Presti voltage, Cornell voltage, and Sokolow–Lyon voltage were automatically derived and compared to the manual annotations. To determine the agreement between manual and automatic measurements and their predictive value, Bland–Altman plots were created and Cox regression analysis for cardiovascular and all-cause mortality was performed. Main results. The median values for the automatic assessment were: Peguero-Lo Presti voltage 1.76 mV (IQR 1.29–2.55), Cornell voltage 1.14 mV (IQR 0.721–1.66), and Sokolow–Lyon voltage 1.66 mV (IQR 1.08–2.23). The mean differences when compared to the manual measurements were −0.027 mV (0.21 SD), 0.027 mV (0.13 SD) and −0.025 mV (0.24 SD) for Peguero-Lo Presti, Cornell, and Sokolow–Lyon voltage, respectively. The categorial LVH detection based on pre-defined thresholds differed in only 13 cases for all indices between manual and automatic assessment. Proportional hazard ratios only differed slightly in categorial LVH detection between manually and automatically determined LVH parameters; no differences could be found for continuous parameters. Significance. This study provides evidence that automatic algorithms can be as reliable in LVH parameter assessment and risk prediction as manual measurements in ESKD patients undergoing haemodialysis.