Key result
Short-term HRV features predict COVID-19 ICU mortality with ~0.82 AUC.
Why the study?
There is no consensus on the optimal combination of heart rate variability features for reliable disease and mortality prediction across clinical contexts, including COVID-19.
Observational (n=82)
No
Effect estimate: AUC 0.82
Absolute Event Rate: 0.82% vs 0.63%
Short-term heart rate variability (HRV) analysis from continuous ECG monitoring during early ICU admission, combined with machine learning models, provides a feasible and noninvasive tool for mortality risk stratification in severe COVID-19 patients.
May support noninvasive HRV-based mortality stratification in COVID-19 ICU; hypothesis-generating and requires prospective validation.
Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of 0.82±0.13 was obtained with Method D versus 0.63±0.09 with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality.
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David et al. (2025) conducted an observational in COVID-19 ICU mortality (n=82). Heart rate variability (HRV) analysis using feature mean aggregation (Method D) vs. Per-window feature classification with majority voting (Method C) was evaluated on Prediction of in-hospital mortality (AUC) (AUC 0.82). Aggregating short-term heart rate variability feature means across patient observation windows predicted COVID-19 ICU mortality with an AUC of 0.82 using Linear Discriminant Analysis.
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