{"_":"Machine learning-assisted prognostic model for mortality in ICU patients with culture-confirmed infection: a preliminary single-center retrospective cohort study.","i":["Klebsiella pneumoniae"]}
肺炎克雷伯菌（KP）是重症监护室的主要病原体，与大量死亡率有关。然而，专门为KP感染的重症监护室（ICU）患者开发的预后工具仍然有限。本研究旨在确定稳定的预后因素，并开发该人群死亡率的预测模型。回顾性地纳入了2020年8月至2025年2月期间在一家三级医院共587名经培养证实KP感染（首次培养阳性）的成年ICU患者。使用最小绝对收缩和选择算子（LASSO）和基于自举的稳定性选择筛选候选预测因子，以确定稳定的预后预测因子。使用最终的稳定预测因子，开发并比较了11个具有10倍交叉验证的模型（包括逻辑回归和基于树的方法）。使用鉴别、校准和临床实用性来评估模型性能。广义加性模型（GAM）用于探索潜在的非线性预测结果关联。587名患者中有122名死亡（20.8%）。确定了五个稳定的预后因素：急性生理学和慢性健康评估II（APACHE II）评分、乳酸、病毒共感染、急性肾损伤（AKI）和肺泡动脉氧梯度（A-aDO2）。在评估的算法中，逻辑回归实现了区分、校准和可解释性的最佳平衡，受试者工作特征曲线下的交叉验证面积（AUC）为0.809。APACHE II（约16.56）、乳酸（约2.87 mmol/L）和A-aDO2（约203.27 mmHg）观察到非线性或阈值样模式。基于逻辑回归，构建了一个列线图和一个基于网络的计算器，用于个性化风险评估。基于五个稳定变量的简约模型在预测重症监护室KP感染患者的死亡率方面表现良好。然而，这些发现是探索性的，在任何临床应用之前都需要外部验证。
(KP) is a major pathogen in intensive care units and is associated with substantial mortality. However, prognostic tools specifically developed for intensive care unit (ICU) patients with KP infection remain limited. This study aimed to identify stable prognostic factors and develop a predictive model for mortality in this population. A total of 587 adult ICU patients with culture-confirmed KP infection (first positive culture) at a tertiary hospital between August 2020 and February 2025 were retrospectively included. Candidate predictors were screened using least absolute shrinkage and selection operator (LASSO) and bootstrap-based stability selection to identify stable prognostic predictors. Using the final stable predictors, 11 models (including logistic regression and tree-based approaches) with 10-fold cross-validation were developed and compared. Model performance was evaluated using discrimination, calibration, and clinical utility. Generalized additive models (GAMs) were used to explore potential non-linear predictor-outcome associations. Mortality occurred in 122/587 patients (20.8%). Five stable prognostic factors were identified: Acute Physiology and Chronic Health Evaluation II (APACHE II) score, lactate, viral co-infection, acute kidney injury (AKI), and the alveolar-arterial oxygen gradient (A-aDO). Among the evaluated algorithms, logistic regression achieved the most favorable balance of discrimination, calibration, and interpretability, with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.809. Non-linear or threshold-like patterns were observed for APACHE II (approximately 16.56), lactate (approximately 2.87 mmol/L), and the A-aDO (approximately 203.27 mmHg). Based on logistic regression, a nomogram and a web-based calculator were constructed for individualized risk estimation. A parsimonious model based on five stable variables showed good performance for predicting mortality among ICU patients with KP infection. However, these findings are exploratory and require external validation before any clinical application.
Xu Ran, Zhaojun Wang, Jingjing Shao, Ziyue Ma, Qinfu Liu, Dan Yang, et al. {"_":"Machine learning-assisted prognostic model for mortality in ICU patients with culture-confirmed infection: a preliminary single-center retrospective cohort study.","i":["Klebsiella pneumoniae"]}. Frontiers in cellular and infection microbiology. 2026; doi:10.3389/fcimb.2026.1858732.
Xu Ran, Zhaojun Wang, Jingjing Shao, Ziyue Ma, Qinfu Liu, Dan Yang, et al (2026). {"_":"Machine learning-assisted prognostic model for mortality in ICU patients with culture-confirmed infection: a preliminary single-center retrospective cohort study.","i":["Klebsiella pneumoniae"]}. Frontiers in cellular and infection microbiology. https://doi.org/10.3389/fcimb.2026.1858732
Xu Ran, Zhaojun Wang, Jingjing Shao, Ziyue Ma, Qinfu Liu, Dan Yang, et al. {"_":"Machine learning-assisted prognostic model for mortality in ICU patients with culture-confirmed infection: a preliminary single-center retrospective cohort study.","i":["Klebsiella pneumoniae"]}[J]. Frontiers in cellular and infection microbiology, 2026 doi:10.3389/fcimb.2026.1858732.
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出