Diagnostic value of short-term longitudinal clinical and laboratory trajectories for gram-negative bacteremia in intensive care units patients.

IF 2
Olcay Dilken, Ahmet Doğukan Bayrak, Okan Derin, Uğur Tekbaş, Mustafa Altınay, Mehmet Nuri Yakar, Bülent Ergin, Serpil Kılıç Depren
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Abstract

Early identification of gram-negative bacteremia (GN-BSI) in intensive care units (ICUs) remains challenging at the time of blood culture sampling, when clinical signs are often nonspecific and existing diagnostic approaches typically rely on single-timepoint measurements. We conducted a retrospective cohort study of adult ICU patients admitted between July 2022 and January 2024 to investigate whether short-term longitudinal patterns in routinely collected clinical and laboratory data contain diagnostically relevant information for GN-BSI. Clinical and laboratory variables were extracted at three consecutive timepoints (Day -2, -1, and 0 relative to blood culture collection), and diagnostic models incorporating this temporal information were developed using complementary statistical and machine-learning approaches. Model performance was evaluated on a held-out test set using discrimination, calibration, and decision curve analysis. Among 568 patients, models incorporating short-term longitudinal data demonstrated good and consistent discrimination for GN-BSI (AUC range 0.81-0.83). A parsimonious logistic regression model of the mean values of seven predictors achieved an AUC of 0.81, which was not significantly different from the best-performing machine learning model (MILD-SVM, AUC 0.83; bootstrap test, p = 0.728). Diagnostic performance was stable across modeling approaches, indicating robustness of the underlying signal rather than dependence on a specific algorithm. Decision curve analysis suggested a higher net benefit of model-based risk stratification compared with treat-all or treat-none strategies across clinically relevant threshold probabilities. Central venous catheter presence, prior antibiotic use, hemoglobin, creatinine, and albumin consistently emerged as influential predictors. These findings indicate that short-term longitudinal clinical trajectories contain diagnostically meaningful information for GN-BSI at the time of blood culture sampling and support further external validation and prospective evaluation prior to clinical implementation.

EXPRESS:短期临床和实验室纵向轨迹对ICU患者革兰氏阴性菌血症的诊断价值。
在重症监护病房(icu)进行血液培养取样时,早期识别革兰氏阴性菌血症仍然具有挑战性,因为临床症状通常是非特异性的,现有的诊断方法通常依赖于单时间点测量。我们对2022年7月至2024年1月期间入住ICU的成年患者进行了一项回顾性队列研究,以调查常规收集的临床和实验室数据中的短期纵向模式是否包含革兰氏阴性菌血症的诊断相关信息。在三个连续的时间点(相对于血培养收集的第2、第1和第0天)提取临床和实验室变量,并使用互补的统计和机器学习方法开发包含这些时间信息的诊断模型。使用判别、校准和决策曲线分析在一个固定测试集上评估模型的性能。在568例患者中,纳入短期纵向数据的模型对革兰氏阴性菌血症(AUC范围0.81-0.83)表现出良好且一致的辨别能力,在重新校准后具有良好的校准效果。诊断性能在各种建模方法中都是稳定的,这表明潜在信号的鲁棒性,而不是依赖于特定的算法。决策曲线分析表明,在临床相关阈值概率上,与全治疗或不治疗策略相比,基于模型的风险分层的净收益更高。血红蛋白、肌酐和白蛋白一直是有影响的贡献者。这些发现表明,短期纵向临床轨迹在血液培养取样时包含对革兰氏阴性菌血症的诊断有意义的信息,并支持在临床实施之前进一步进行外部验证和前瞻性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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