背景:在重症监护病房(ICU)中,准确的死亡率预测对于有效的患者管理和资源分配至关重要。简化急性生理学评分II(SAPS-2),虽然常用,严重依赖全面的临床数据和血液样本。这项研究旨在开发一种人工智能(AI)模型,利用关键的血液动力学参数来预测最初24小时内的ICU死亡率,并评估其相对于SAPS-2的性能。
方法:我们对选定的血流动力学参数和心率曲线结构进行了分析,以确定ICU死亡率的潜在预测因素。随后在不同的患者队列上训练和验证了机器学习模型。然后将AI算法的性能与SAPS-2进行比较,重点是分类准确性,校准,和普适性。
结果:该研究包括从3月27日开始的1298名ICU患者,2015年3月27日,2017.从2022年到2023年的额外队列包括590名患者,导致1888名患者的总数据集。观察到的死亡率为24.0%。死亡率的关键决定因素是格拉斯哥昏迷量表评分,心率复杂度,患者年龄,舒张压低于50mmHg的持续时间,心率变异性,和特定的平均和收缩压阈值。AI模型,在这些决定因素的指导下,在预测死亡率方面表现出相当的表现,如果不是优越的,到SAPS-2.
结论:AI模型,将心率和血压曲线分析与基本临床参数相结合,提供了一种方法来预测ICU患者的院内死亡率。该模型提供了现有工具的替代方案,这些工具依赖于广泛的临床数据和实验室输入。将其整合到ICU监测系统中可能会促进更简化的死亡率预测过程。
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.
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.