Early heart rate variability evaluation enables to predict ICU patients’ outcome - Université de Bretagne Occidentale
Article Dans Une Revue Scientific Reports Année : 2022

Early heart rate variability evaluation enables to predict ICU patients’ outcome

Résumé

Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such variation in survival prediction using a physiological data-warehousing program. Plethysmogram tracings (PPG) were recorded at 75 Hz from the standard monitoring system, for a 2 h period, during the 24 h following ICU admission. Physiological data recording was associated with metadata collection. HRV was derived from PPG in either the temporal and non-linear domains. 540 consecutive patients were recorded. A lower LF/ HF, SD2/SD1 ratios and Shannon entropy values on admission were associated with a higher ICU mortality. SpO2/FiO2 ratio and HRV parameters (LF/HF and Shannon entropy) were independent correlated with mortality in the multivariate analysis. Machine-learning using neural network (kNN) enabled to determine a simple decision tree combining the three best determinants (SDNN, Shannon Entropy, SD2/SD1 ratio) of a composite outcome index. HRV measured on admission enables to predict outcome in the ICU or at Day-28, independently of the admission diagnosis, treatment and mechanical ventilation requirement.
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hal-04758028 , version 1 (29-10-2024)

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Laetitia Bodenes, Quang-Thang N’guyen, Raphaël Le Mao, Nicolas Ferrière, Victoire Pateau, et al.. Early heart rate variability evaluation enables to predict ICU patients’ outcome. Scientific Reports, 2022, 12 (1), ⟨10.1038/s41598-022-06301-9⟩. ⟨hal-04758028⟩
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