Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence.

Autor: Boussen S; Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Laboratoire de Biomécanique Appliquée-Université Gustave-Eiffel, Aix-Marseille Université, UMR T24, 51 boulevard Pierre Dramard, 13015, Marseille, France. Electronic address: salah.boussen@univ-eiffel.fr., Benard-Tertrais M; Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France., Ogéa M; Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France., Malet A; Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France., Simeone P; Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Aix Marseille University, CNRS, Inst Neurosci Timone, UMR7289, Marseille, France., Antonini F; Intensive Care and Anesthesiology Department, Hôpital Nord Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France., Bruder N; Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France., Velly L; Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Aix Marseille University, CNRS, Inst Neurosci Timone, UMR7289, Marseille, France.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Feb; Vol. 169, pp. 107934. Date of Electronic Publication: 2024 Jan 03.
DOI: 10.1016/j.compbiomed.2024.107934
Abstrakt: Background: In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2.
Methods: We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability.
Measurements and Main Results: The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2.
Conclusions: The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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