机器学习用于预测脓毒症患者死亡率及其相关并发症：一项系统综述与荟萃分析。
完整摘要 · 中文译文
背景：脓毒症仍是全球重症监护病房的主要死亡原因，并可能伴随复杂并发症，包括脑病、凝血功能障碍、急性肾损伤、心肌功能障碍和谵妄，每一种并发症均显著增加不良结局的风险。机器学习是一种有前景的预后预测方法，能够捕捉临床危险因素之间复杂的非线性关系。然而，其在脓毒症及其特定并发症中的预测性能仍不清楚。 方法：我们系统检索了PubMed、Embase、Web of Science和Cochrane Library中截至2026年4月的相关文献。纳入旨在开发或验证机器学习模型以预测成年脓毒症患者及其相关并发症死亡率的研究。采用随机效应模型或双变量模型计算AUC、灵敏度和特异度。 结果：总体而言，机器学习模型对脓毒症短期死亡率显示出中等预测性能，训练集合并AUC为0.82（46项研究，15个模型），验证集为0.79（50项研究，25个模型）。逻辑回归是评估最广泛的模型，表现出稳定的性能（验证集AUC = 0.81）。集成方法（包括随机森林和XGBoost）表现同样良好，两组AUC均在0.81至0.83之间。尽管LightGBM和DL-CMT分别在训练集和验证集中获得最高AUC，但两项估计均基于单项研究。在并发症中， 脓毒症相关脑病（AUC = 0.81，3项研究）和脓毒症诱导性凝血病（AUC = 0.80，4项研究）的证据最为确凿且得到了最佳验证。脓毒症相关急性肾损伤表现出中等且稳定的性能（验证集 AUC = 0.79，4项研究）。脓毒症诱导性心肌病从训练集到验证集表现出显著衰减（AUC 0.84 对 0.78，均来自单项研究），随机森林在训练集中表现良好（AUC = 0.98），但未被广泛报道。脓毒性休克达到了最高的 AUC（0.95），但这是基于单项研究。脓毒症相关谵妄的预测效用有限（AUC = 0.67，1项研究），脓毒症诱导性心肌损伤为中等（AUC = 0.77，1项研究）。对于长期死亡率，证据极为缺乏。仅 SAE（训练期间 AUC 0.88，验证期间 0.86）和 SCRS（合并 AUC 0.76）提供了单项研究估计值，无法得出可靠结论。 结论：在本荟萃分析中，机器学习模型对脓毒症短期死亡率显示出中等的预测性能。在并发症中，SAE 和 SIC 具有最强的证据和最佳的性能，而 SAD 显示出有限的效用。长期死亡率的证据极为稀缺。这些发现支持机器学习在早期预后风险分层中的潜在作用，但也凸显了迫切需要进行严格的外部验证和更高质量的研究，特别是对于研究不足的并发症。
完整摘要 · 英文原文
BACKGROUND: Sepsis remains a leading cause of death in intensive care units worldwide and may be accompanied by complex complications, including encephalopathy, coagulopathy, acute kidney injury, myocardial dysfunction, and delirium, each of which substantially increases the risk of poor outcomes. Machine learning, a promising approach for prognostic prediction, can capture complex nonlinear relationships among clinical risk factors. However, its predictive performance in sepsis and its specific complications remains unclear. METHOD: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library for relevant literature through April 2026. Studies that aimed to develop or validate machine learning models to predict mortality in adult patients with sepsis and its associated complications were included. The random-effects or bivariate model was used to calculate the AUC, sensitivity, and specificity. RESULTS: Overall, machine learning models show moderate predictive performance for short-term mortality in sepsis, with a combined AUC of 0.82 for the training set (46 studies, 15 models) and 0.79 for the validation set (50 studies, 25 models). Logistic regression is the most widely evaluated model, demonstrating stable performance (validation AUC = 0.81). Integrated methods, including random forest and XGBoost, performed equally well, with AUCs between 0.81 and 0.83 in both groups. Although LightGBM and DL-CMT achieved the highest AUCs in the training and validation sets, respectively, both estimates were based on a single study. Among the complications, the evidence for sepsis-associated encephalopathy (AUC = 0.81, 3 studies) and sepsis-induced coagulopathy (AUC = 0.80, 4 studies) is the most conclusive and best demonstrated. Sepsis-associated acute kidney injury showed moderate and stable performance (validation AUC = 0.79, 4 studies). Sepsis-induced cardiomyopathy showed significant attenuation from training to validation (AUC 0.84 vs 0.78, all from a single study), and random forest performed well in training (AUC = 0.98) but was not widely reported. Septic shock reached the highest AUC (0.95), but this was based on a single study. The predictive utility of sepsis-associated delirium is limited (AUC = 0.67, 1 study), and sepsis-induced myocardial injury is moderate (AUC = 0.77, 1 study).For long-term mortality, evidence is extremely lacking. Only SAE (AUC 0.88 during training and 0.86 during validation) and SCRS (combined AUC 0.76) provided single study estimates, ruling out reliable conclusions. CONCLUSION: In this meta-analysis, machine learning models showed moderate predictive performance for short-term mortality in sepsis. Among complications, SAE and SIC had the strongest evidence and best performance, whereas SAD showed limited utility. Evidence for long-term mortality was extremely scarce. These findings support the potential role of machine learning in early prognostic risk stratification, yet they also highlight the urgent need for rigorous external validation and higher-quality studies, particularly for under-investigated complications.