An artificial intelligence model utilizing heart rate complexity and hemodynamic parameters predicted ICU mortality (overall rate 24.0%) with performance comparable or superior to SAPS-2.
Cohort (n=1,888)
Does an AI model utilizing hemodynamic parameters improve mortality prediction compared to SAPS-2 in ICU patients?
An AI model using basic clinical and hemodynamic parameters, including heart rate complexity, provides comparable or superior ICU mortality prediction compared to the SAPS-2 score without requiring extensive laboratory data.
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.
Boussen et al. (Wed,) conducted a cohort in ICU patients (n=1,888). Artificial intelligence model vs. Simplified Acute Physiology Score II (SAPS-2) was evaluated on ICU mortality. An artificial intelligence model utilizing heart rate complexity and hemodynamic parameters predicted ICU mortality (overall rate 24.0%) with performance comparable or superior to SAPS-2.