Data Sheet 1_Development and validation of a multidimensional predictive model for 28-day mortality in ICU patients with bloodstream infections: a cohort study.csv

BackgroundBloodstream infections (BSI) are a leading cause of sepsis and death in intensive care unit (ICU). Traditional severity scores, including the Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APSIII), and Simplified Acute Physiology Score II (SAPS II), exhibit limitations in effectively predicting mortality among BSI patients, primarily due to their reliance on a narrow range of clinical variables. This study aimed to develop and validate a comprehensive nomogram model for 28-day all-cause mortality prediction in BSI patients.
MethodsA retrospective cohort study was conducted using data from 3,615 patients with positive blood cultures from the MIMIC-IV database, divided into training (n=2,532) and validation (n=1,083) cohorts. Through a two-step variable selection process combining LASSO regression and Boruta algorithm, we identified 12 predictive variables from 58 initial clinical parameters. The model’s performance was evaluated using AUROC, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).
ResultsThe nomogram demonstrated superior discrimination (AUROC: 0.760 vs. 0.671, P<0.001 for SOFA; 0.760 vs. 0.705, P<0.001 for APSIII; 0.760 vs. 0.707, P<0.001 for SAPS II) in the training cohort, with consistent performance in the validation cohort (AUROC: 0.742). Key predictors identified in our model included the need for mechanical ventilation, the presence of malignancy, platelet count, and scores on the Glasgow Coma Scale (GCS). The model showed significant improvements in NRI and IDI, with consistent net benefit across a wide range of threshold probabilities in DCA.
ConclusionsThis study developed and validated a predictive model for 28-day mortality in BSI patients that demonstrated superior performance compared to traditional severity scores. By integrating clinical, laboratory, and treatment-related variables, the model provides a more comprehensive approach to risk stratification. These findings highlight its potential for improving early identification of high-risk patients and guiding clinical decision-making, though further prospective validation is needed to confirm its generalizability.

背景 血流感染（Bloodstream Infections, BSI）是重症监护病房（Intensive Care Unit, ICU）内引发脓毒症及死亡的首要诱因之一。传统严重程度评分工具，包括序贯器官衰竭评分（Sequential Organ Failure Assessment, SOFA）、急性生理学评分Ⅲ（Acute Physiology Score III, APSIII）以及简化急性生理学评分Ⅱ（Simplified Acute Physiology Score II, SAPS II），在有效预测血流感染患者死亡率方面存在显著局限，核心原因在于其仅依赖范围有限的临床变量。本研究旨在开发并验证一款可用于预测血流感染患者28天全因死亡率的综合列线图模型。
方法 本研究采用回顾性队列研究设计，使用来自MIMIC-IV数据库的3615例血培养阳性患者的临床数据，将其划分为训练队列（n=2532）与验证队列（n=1083）。通过结合LASSO回归与Boruta算法的两步变量筛选流程，从58项初始临床参数中筛选出12个有效预测变量。本研究采用受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve, AUROC）、净重新分类指数（Net Reclassification Improvement, NRI）、综合判别改善指数（Integrated Discrimination Improvement, IDI）以及决策曲线分析（Decision Curve Analysis, DCA）对模型的性能进行全面评估。
结果 在训练队列中，该列线图展现出更优的区分能力：其AUROC为0.760，相较于SOFA评分的0.671（P<0.001）、APSIII评分的0.705（P<0.001）以及SAPS II评分的0.707（P<0.001）均有显著提升；且该模型在验证队列中保持了一致的优异性能，AUROC达0.742。本模型筛选出的关键预测变量包括机械通气需求、恶性肿瘤病史、血小板计数以及格拉斯哥昏迷量表（Glasgow Coma Scale, GCS）评分。该模型在NRI与IDI指标上均表现出显著改善，且在决策曲线分析中，于广泛的阈值概率范围内均呈现出稳定的净获益。
结论 本研究开发并验证了一款用于预测血流感染患者28天全因死亡率的预测模型，其整体性能优于传统严重程度评分工具。该模型整合了临床、实验室及治疗相关变量，为患者的风险分层提供了更为全面的分析思路。本研究结果凸显了该模型在早期识别高危患者、指导临床决策方面的潜在应用价值，但仍需开展进一步的前瞻性验证研究以确认其临床外推性。