The prognostic predictive value of the components of the PR interval in hospitalized patients with heart failure

Abstract

Objective Previous reports on the epidemiology, influencing factors, and the prognostic value of the components of PR interval in hospitalized heart failure patients were limited. Methods This study retrospectively enrolled 1182 patients hospitalized with heart failure from 2014 to 2017. Multiple linear regression analysis was used to explore the association between the components of PR interval and the baseline parameters. The primary outcome was all-cause death or heart transplantation. Multivariable-adjusted Cox proportional hazard regression models were constructed to explore the predictive value of the components of PR interval for the primary outcome. Results In multiple linear regression analysis, higher height (for every 10 cm increase in height: regression coefficient 4.83, P < 0.001) as well as larger atrial and ventricular size were associated with larger P wave duration but not with PR segment. The primary outcome occurred in 310 patients after an average follow-up of 2.39 years. Cox regression analyses revealed that the increase in PR segment was an independent predictor of the primary outcome (every 10 ms increase: hazard ratio 1.041, 95% confidence interval [CI] 1.010–1.083, P = 0.023), whereas the P wave duration did not show significant correlation. When adding the PR segment to an initial prognostic prediction model, the likelihood ratio test and categorical net reclassification index (NRI) showed a significant improvement, but the increase in C-index was not significant. In subgroup analysis, increased PR segment was an independent predictor of the primary endpoint in patients taller than 170 cm (each 10 ms increase: hazard ratio 1.153, 95% CI 1.085–1.225, P < 0.001) but not the shorter group (P for interaction = 0.006). Conclusions In hospitalized patients with heart failure, longer PR segment was an independent predictor of the composite endpoint of all-cause death and heart transplantation, especially in the taller group, but it had limited significance in improving the prognostic risk stratification of this population.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

“…Heart failure mortality has been looked at in patients requiring different levels of care from step-down care, progressive care, and intensive care to different stages and with many different covariates [ 35 – 38 ]. Researchers have utilized methods from logistic regression to machine learning [ 39 – 44 ]. Within machine learning researchers are starting to utilize transparent methods for visualization [ 24 , 45 ].…”

“…covariates [35][36][37][38]. Researchers have utilized methods from logistic regression to machine learning [39][40][41][42][43][44]. Within machine learning researchers are starting to utilize transparent methods for visualization [24,45].…”