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

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.

Electrocardiographic parameters of left ventricular hypertrophy and prediction of mortality in hemodialysis patients.

BACKGROUND: In hemodialysis patients, left ventricular hypertrophy (LVH) contributes to high cardiovascular mortality. We examined cardiovascular mortality prediction by the recently proposed Peguero-Lo Presti voltage since it identifies more patients with electrocardiographic (ECG) LVH than Cornell or Sokolow-Lyon voltages. METHODS: A total of 308 patients on hemodialysis underwent 24 h ECG recordings. LVH parameters were measured before and after dialysis. The primary endpoint of cardiovascular mortality was recorded during a median 3-year follow up. Risk prediction was assessed by Cox regression, both unadjusted and adjusted for the Charlson Comorbidity Index and the Cardiovascular Mortality Risk Score. RESULTS: The Peguero-Lo Presti voltage identified with 21% the most patients with positive LVH criteria. All voltages significantly increased during dialysis. Factors such as ultrafiltration rate, Kt/V, body mass index, sex, and phosphate were the most relevant for these changes. During follow-up, 26 cardiovascular deaths occurred. Post-dialysis Peguero-Lo Presti cut-off as well as the Peguero-Lo Presti and Cornell voltages were independently associated with cardiovascular mortality in unadjusted and adjusted analysis. The Sokolow-Lyon voltage was not significantly associated with mortality. An optimal cut-off for the prediction of cardiovascular mortality was estimated at 1.38 mV for the Peguero-Lo Presti. CONCLUSIONS: The post-dialysis Peguero-Lo Presti cut-off as well as the Peguero-Lo Presti and Cornell voltages allowed independent risk prediction of cardiovascular mortality in hemodialysis patients. Measuring the ECG LVH parameters after dialysis might allow a standardized interpretation as dialysis-specific factors influence the voltages.

Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients.

Cohort studies often provide a large array of data on study participants. The techniques of statistical learning can allow an efficient way to analyze large datasets in order to uncover previously unknown, clinically relevant predictors of morbidity or mortality. We applied a combination of elastic net penalized Cox regression and stability selection with the aim of identifying novel predictors of mortality in a cohort of prevalent hemodialysis patients. In our analysis we included 475 patients from the "rISk strAtification in end-stage Renal disease" (ISAR) study, who we split into derivation and confirmation cohorts. A wide array of examinations was available for study participants, resulting in over a hundred potential predictors. In the selection approach many of the well established predictors were retrieved in the derivation cohort. Additionally, the serum levels of IL-12p70 and AST were selected as mortality predictors and confirmed in the withheld subgroup. High IL-12p70 levels were specifically prognostic of infection-related mortality. In summary, we demonstrate an approach how statistical learning can be applied to a cohort study to derive novel hypotheses in a data-driven way. Our results suggest a novel role of IL-12p70 in infection-related mortality, while AST is a promising additional biomarker in patients undergoing hemodialysis.