人工智能用于ICU患者循环性休克早期检测

中文摘要 AI 辅助

循环性休克是重症监护病房（ICU）患者死亡的主要原因之一，其早期检测对于及时治疗和改善临床结局至关重要。本研究旨在开发并评估一个两阶段级联机器学习框架，用于危重患者循环性休克的早期检测和病因分类。利用重症监护医学信息集市（MIMIC）-IV数据库的数据，定义了四组患者：脓毒性休克、心源性休克、低血容量性休克和非休克对照组，共包含32,907名患者。在ICU入院后的前六小时内收集生命体征和实验室数据。经过数据清洗和缺失值插补后，使用每个变量的平均值进行模型开发。比较了多种机器学习算法，包括逻辑回归、随机森林、XGBoost和多层感知器（MLP）网络。随机森林和XGBoost达到了最高的整体性能，休克检测的AUROC约为0.82-0.83，所有四个类别的宏平均灵敏度约为0.61，精确度约为0.58。非休克组的分类性能最高，其次是脓毒性休克和心源性休克，而低血容量性休克的性能最低。这些结果表明，机器学习模型能够识别与循环性休克相关的血流动力学恶化的早期迹象，并可能支持ICU中的临床决策。然而，对于特定休克亚型（尤其是低血容量性和心源性休克）的分类以及实时临床实施，仍需进一步改进。

英文摘要

Circulatory shock is one of the leading causes of mortality in intensive care units (ICUs), and its early detection is critical to enable timely treatment and improve clinical outcomes. This study aimed to develop and evaluate a two-stage cascade machine learning framework for the early detection and etiological classification of circulatory shock in critically ill patients. Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, four patient groups were defined: septic shock, cardiogenic shock, hypovolemic shock, and a non?shock control group, comprising a total of 32,907 patients. Vital signs and laboratory data were collected during the first six hours after ICU admission. After data cleaning and missing-value imputation, the mean value of each variable was used for model development. Several machine learning algorithms were compared, including logistic regression, Random Forest, XGBoost, and multilayer perceptron (MLP) networks. Random Forest and XGBoost achieved the highest overall performance, with an AUROC of approximately 0.82-0.83 for shock detection, and a macro-averaged sensitivity of approximately 0.61 and precision of approximately 0.58 across all four classes. Classification performance was highest for the non?shock group, followed by septic and cardiogenic shock, while hypovolemic shock showed the lowest performance. These results indicate that machine learning models can identify early signs of hemodynamic deterioration associated with circulatory shock and may support clinical decision-making in the ICU. However, further improvements are needed for the classification of specific shock subtypes, particularly hypovolemic and cardiogenic shock, as well as for real-time clinical implementation.

发表机构

• Universitat Oberta de Catalunya（加泰罗尼亚开放大学）
• Better Care SL
• SCIAS-Hospital de Barcelona（巴塞罗那SCIAS医院）

机构由 AI 辅助整理，请以论文原文为准。