Predictive Model of All-Cause Death in Patients with Heart Failure using Heart Rate Variability.

Area of Science:

• Cardiology
• Medical Informatics
• Predictive Analytics

Background:

• Heart failure (HF) poses a significant mortality risk.
• Heart rate variability (HRV) analysis from Holter monitoring shows potential in predicting adverse outcomes.
• Identifying reliable predictors of all-cause death is crucial for managing HF patients.

Purpose of the Study:

• To develop a predictive model for all-cause mortality in heart failure (HF) patients.
• To utilize short-duration heart rate variability (HRV) metrics as predictors.
• To assess the combined predictive power of HRV, age, and left ventricular ejection fraction (LVEF).

Main Methods:

• Retrospective analysis of HF patients undergoing Holter monitoring.
• Evaluation of short-term HRV variables, specifically the lowest 10-minute recordings.
• Multivariate Cox regression analysis to identify independent predictors of all-cause death.

Main Results:

• The study included 116 HF patients with a 33.6% mortality rate over 2.83 years.
• Independent predictors of all-cause death identified were age > 69 years, LVEF ≤ 57%, and lowest 10-minute rMSSD ≤ 12ms.
• A predictive model incorporating these factors achieved an AUC of 0.802.

Conclusions:

• Age, LVEF, and short-term rMSSD are significant independent predictors of long-term all-cause mortality in hospitalized HF patients.
• Even brief periods of elevated sympathetic tone, reflected in HRV, can impact survival.
• The findings are particularly relevant for elderly HF patients and those with reduced ejection fraction.