Zhang Tianshan, Xiao Bojie, Tang Guo, Cheng Tao, Gao Hongguang, Zhang Ping, Yao Rong
Emergency Department of West China Hospital, Sichuan University, Chengdu, China.
Department of Emergency Medicine, West China Hospital, West China School of Nursing, Sichuan University, Chengdu, China.
Front Med (Lausanne). 2025 Jul 25;12:1603374. doi: 10.3389/fmed.2025.1603374. eCollection 2025.
To develop and validate a rapid-assessment scoring system for predicting in-hospital mortality in heat stroke (HS) patients, thereby facilitating early identification and intervention for critical cases.
We conducted a retrospective cohort analysis of HS patients admitted to emergency department (ED) of 13 hospitals in southwest of China between July 1, 2022 and December 31, 2024. Clinical parameters including demographic data, initial vital signs, and major organ function biomarkers were systematically collected. Patients were further divided into a training cohort and a validation cohort at a 7:3 ratio. The primary endpoint was all-cause in-hospital mortality. Through rigorous variable selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariable logistic regression modeling, we developed a prognostic nomogram. Model performance was assessed via receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and clinical impact curve (CIC) evaluation, with comparative benchmarking against established scoring systems [Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II)].
A total of 307 patients were included in the study. 114 experienced in-hospital mortality, while 193 survived. Non-survivors exhibited significantly altered baseline values across multiple physiological domains: reduced Glasgow Coma Scale (GCS), impaired oxygenation index (OI), elevated fibrin degradation products (FDP), D-dimer, activated partial thromboplastin time (APTT), and serum creatinine (Cr) (all < 0.0001). Through LASSO regression followed by multivariate logistic regression analysis, 27 initially significant variables were refined to four independent prognostic indicators: Cr, GCS, OI, and FDP. These predictors were subsequently integrated into a multivariate prognostic nomogram demonstrating discriminative capacity for mortality risk stratification in both training (AUC 0.811, 95% CI 0.751-0.871) and validation cohorts (AUC 0.766, 95% CI 0.706-0.826). DCA revealed superior net benefit across clinically relevant probability thresholds. The AUC of the nomogram in the entire cohort (0.794) was significantly superior to the SOFA score (0.703, DeLong's test, = 0.0008) and comparable to the APACHE II score (0.765, DeLong's test, = 0.3581).
We developed and validated a prognostic tool utilizing routinely available parameters in ED to predict in-hospital mortality in HS patients. This clinically implementable model demonstrates comparable accuracy to established intensive care scoring systems while offering distinct advantages in rapid bedside application, potentially enabling time-critical therapeutic decisions in emergency settings.
开发并验证一种快速评估评分系统，用于预测中暑（HS）患者的院内死亡率，从而便于对危重症病例进行早期识别和干预。
我们对2022年7月1日至2024年12月31日期间在中国西南部13家医院急诊科收治的HS患者进行了回顾性队列分析。系统收集了包括人口统计学数据、初始生命体征和主要器官功能生物标志物在内的临床参数。患者进一步按7:3的比例分为训练队列和验证队列。主要终点是全因院内死亡率。通过使用最小绝对收缩和选择算子（LASSO）回归进行严格的变量选择，随后进行多变量逻辑回归建模，我们开发了一种预后列线图。通过受试者操作特征（ROC）曲线分析、决策曲线分析（DCA）和临床影响曲线（CIC）评估来评估模型性能，并与既定的评分系统[序贯器官衰竭评估（SOFA）和急性生理与慢性健康评估II（APACHE II）]进行比较基准测试。
本研究共纳入307例患者。114例患者发生院内死亡，193例存活。非存活者在多个生理领域的基线值有显著改变：格拉斯哥昏迷量表（GCS）降低、氧合指数（OI）受损、纤维蛋白降解产物（FDP）、D - 二聚体、活化部分凝血活酶时间（APTT）和血清肌酐（Cr）升高（均P < 0.0001）。通过LASSO回归和多变量逻辑回归分析，27个最初显著的变量被细化为4个独立的预后指标：Cr、GCS、OI和FDP。这些预测指标随后被整合到一个多变量预后列线图中，该列线图在训练队列（AUC 0.811，95%CI 0.751 - 0.871）和验证队列（AUC 0.766，95%CI 0.706 - 0.826）中均显示出对死亡风险分层的判别能力。DCA显示在临床相关概率阈值范围内具有更高的净效益。列线图在整个队列中的AUC（0.794）显著优于SOFA评分（0.703，DeLong检验，P = 0.0008），与APACHE II评分相当（0.765，DeLong检验，P = 0.3581）。
我们开发并验证了一种利用急诊科常规可用参数预测HS患者院内死亡率的预后工具。这种临床可实施的模型显示出与既定的重症监护评分系统相当的准确性，同时在床边快速应用方面具有明显优势，可能有助于在紧急情况下做出关键的治疗决策。
