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

• Published in: Computers in biology and medicine Vol. 169; p. 107934
• Main Authors: Boussen, Salah, Benard-Tertrais, Manuela, Ogéa, Mathilde, Malet, Arthur, Simeone, Pierre, Antonini, François, Bruder, Nicolas, Velly, Lionel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-02-2024; Elsevier Limited
• Subjects: Algorithms; Artificial intelligence; Automation; Blood pressure; Clinical outcomes; Complexity; Diastolic blood pressure; Disease; Electronic health records; Heart failure; Heart rate; Hemodynamic parameters; Hemodynamics; Hospitals; ICU; Inclusion; Intensive care; Intensive care units; Machine learning; Mathematical models; Monitoring systems; Mortality; Mortality prediction; Observational studies; Parameter identification; Patients; Performance prediction; Physiology; Predictions; Pressure curve; Resource allocation; SAPS-2; Sepsis; Variability; Variables; Variability; Diastolic blood pressure; Machine learning; Hemodynamic parameters; ICU; Blood pressure; Artificial intelligence; SAPS-2; Mortality prediction; Complexity
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.107934

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.
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.
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.
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.
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•Novel AI-based score predicts mortality in ICU patients using heart rate and blood pressure curves.•Heart rate complexity, age, and diastolic blood pressure are key predictors of in-hospital death.•The proposed algorithm requires minimal clinical data and no laboratory samples.•Potential for implementation in ICU monitoring systems for real-time mortality prediction.