An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study.

IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2025-02-06 DOI:10.2196/58779
Guanghao Liu, Shixiang Zheng, Jun He, Zi-Mei Zhang, Ruoqiong Wu, Yingying Yu, Hao Fu, Li Han, Haibo Zhu, Yichang Xu, Huaguo Shao, Haidan Yan, Ting Chen, Xiaopei Shen
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Abstract

Background: Septic shock (SS) is a syndrome with high mortality. Early forewarning and diagnosis of SS, which are critical in reducing mortality, are still challenging in clinical management.

Objective: We propose a simple and fast risk-stratified forewarning model for SS to help physicians recognize patients in time. Moreover, further insights can be gained from the application of the model to improve our understanding of SS.

Methods: A total of 5125 patients with sepsis from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database were divided into training, validation, and test sets. In addition, 2180 patients with sepsis from the eICU Collaborative Research Database (eICU) were used as an external validation set. We developed a simplified risk-stratified early forewarning model for SS based on the weight of evidence and logistic regression, which was compared with multi-feature complex models, and clinical characteristics among risk groups were evaluated.

Results: Using only vital signs and rapid arterial blood gas test features according to feature importance, we constructed the Septic Shock Risk Predictor (SORP), with an area under the curve (AUC) of 0.9458 in the test set, which is only slightly lower than that of the optimal multi-feature complex model (0.9651). A median forewarning time of 13 hours was calculated for SS patients. 4 distinct risk groups (high, medium, low, and ultralow) were identified by the SORP 6 hours before onset, and the incidence rates of SS in the 4 risk groups in the postonset interval were 88.6% (433/489), 34.5% (262/760), 2.5% (67/2707), and 0.3% (4/1301), respectively. The severity increased significantly with increasing risk in both clinical features and survival. Clustering analysis demonstrated a high similarity of pathophysiological characteristics between the high-risk patients without SS diagnosis (NS_HR) and the SS patients, while a significantly worse overall survival was shown in NS_HR patients. On further exploring the characteristics of the treatment and comorbidities of the NS_HR group, these patients demonstrated a significantly higher incidence of mean blood pressure <65 mmHg, significantly lower vasopressor use and infused volume, and more severe renal dysfunction. The above findings were further validated by multicenter eICU data.

Conclusions: The SORP demonstrated accurate forewarning and a reliable risk stratification capability. Among patients forewarned as high risk, similar pathophysiological phenotypes and high mortality were observed in both those subsequently diagnosed as having SS and those without such a diagnosis. NS_HR patients, overlooked by the Sepsis-3 definition, may provide further insights into the pathophysiological processes of SS onset and help to complement its diagnosis and precise management. The importance of precise fluid resuscitation management in SS patients with renal dysfunction is further highlighted. For convenience, an online service for the SORP has been provided.

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重症监护室脓毒性休克的简易快速风险分级早期预警模型:开发、验证和解释研究。
背景:感染性休克(SS)是一种死亡率很高的综合征。早期预警和诊断是降低死亡率的关键,但在临床管理中仍具有挑战性。目的:提出一种简单快速的SS风险分层预警模型,帮助医生及时识别患者。方法:将重症监护医疗信息市场- iv (MIMIC-IV)数据库中的5125例脓毒症患者分为训练集、验证集和测试集。此外,从eICU合作研究数据库(eICU)中选取2180例脓毒症患者作为外部验证集。我们基于证据权和logistic回归建立了SS的简化风险分层早期预警模型,并与多特征复杂模型进行比较,评价各风险组的临床特征。结果:根据特征重要性,仅使用生命体征和快速动脉血气检测特征构建脓毒性休克风险预测器(SORP),测试集的曲线下面积(AUC)为0.9458,仅略低于最优多特征复合模型(0.9651)。SS患者的中位预警时间为13小时。发病前6 h的SORP将SS分为高、中、低、超低4个不同的危险组,发病后时间内4个危险组的SS发病率分别为88.6%(433/489)、34.5%(262/760)、2.5%(67/2707)和0.3%(4/1301)。严重程度随着临床特征和生存风险的增加而显著增加。聚类分析显示,未诊断为SS的高危患者(NS_HR)与SS患者的病理生理特征具有较高的相似性,而NS_HR患者的总生存率明显较差。进一步探讨NS_HR组的治疗特点及合共病,发现NS_HR组患者的平均血压发生率明显高于NS_HR组。结论:SORP具有准确的预警和可靠的风险分层能力。在预先被警告为高风险的患者中,在随后被诊断为SS的患者和未被诊断为SS的患者中观察到相似的病理生理表型和高死亡率。被脓毒症-3定义所忽视的NS_HR患者可能为SS发病的病理生理过程提供进一步的见解,并有助于补充其诊断和精确治疗。进一步强调了SS合并肾功能不全患者精确液体复苏管理的重要性。为方便起见,本署已提供网上查询服务。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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