Mușat Florentina, Păduraru Dan Nicolae, Bolocan Alexandra, Palcău Cosmin Alexandru, Copăceanu Andreea-Maria, Ion Daniel, Jinga Viorel, Andronic Octavian
Carol Davila University of Medicine and Pharmacy, Faculty of Medicine, General Surgery Department, University Emergency Hospital of Bucharest, 050098 Bucharest, Romania.
Bucharest University of Economic Studies, Faculty of Cybernetics, Statistics and Informatics, 010374 Bucharest, Romania.
Biomedicines. 2024 Dec 19;12(12):2892. doi: 10.3390/biomedicines12122892.
Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database. Eligible studies investigated the development of ML models incorporating commonly available laboratory and clinical data for predicting survival outcomes in adult ICU patients with sepsis. Study selection followed the PRISMA guidelines and relied on predefined inclusion criteria. All records were independently assessed by two reviewers, with conflicts resolved by a third senior reviewer. Data related to study design, methodology, results, and interpretation of the results were extracted in a predefined grid. Overall, 19 studies were identified, encompassing primarily logistic regression, random forests, and neural networks. Most used datasets were US-based (MIMIC-III, MIMIC-IV, and eICU-CRD). The most common variables used in model development were age, albumin levels, lactate levels, and ventilator. ML models demonstrated superior performance metrics compared to conventional methods and traditional scoring systems. The best-performing model was a gradient boosting decision tree, with an area under curve of 0.992, an accuracy of 0.954, and a sensitivity of 0.917. However, several critical limitations should be carefully considered when interpreting the results, such as population selection bias (i.e., single center studies), small sample sizes, limited external validation, and model interpretability. Through real-time integration of routine laboratory and clinical data, ML-based tools can assist clinical decision-making and enhance the consistency and quality of sepsis management across various healthcare contexts, including ICUs with limited resources.
脓毒症带来了重大的诊断和预后挑战，传统的评分系统，如序贯器官衰竭评估（SOFA）和急性生理学及慢性健康状况评分系统（APACHE），在预测准确性方面存在局限性。基于机器学习（ML）的预测生存模型可以通过考虑影响脓毒症患者预后的众多复杂因素，支持重症监护病房（ICU）的风险评估和治疗决策。使用PubMed数据库对2014年至2024年发表的研究进行了系统的文献综述。符合条件的研究调查了结合常用实验室和临床数据的ML模型的开发，以预测成年ICU脓毒症患者的生存结果。研究选择遵循系统评价和Meta分析的首选报告项目（PRISMA）指南，并依赖于预定义的纳入标准。所有记录由两名评审员独立评估，冲突由第三位资深评审员解决。与研究设计、方法、结果以及结果解释相关的数据在预定义的表格中提取。总体而言，共确定了19项研究，主要包括逻辑回归、随机森林和神经网络。大多数使用的数据集来自美国（多中心重症监护医学信息数据库第三版、多中心重症监护医学信息数据库第四版和电子ICU临床研究数据库）。模型开发中最常用的变量是年龄、白蛋白水平、乳酸水平和呼吸机使用情况。与传统方法和传统评分系统相比，ML模型表现出更好的性能指标。表现最佳的模型是梯度提升决策树，曲线下面积为0.992，准确率为0.954，灵敏度为0.917。然而，在解释结果时应仔细考虑几个关键限制，如人群选择偏差（即单中心研究）、样本量小、外部验证有限以及模型可解释性。通过实时整合常规实验室和临床数据，基于ML的工具可以协助临床决策，并提高包括资源有限的ICU在内的各种医疗环境中脓毒症管理的一致性和质量。
