@Article{info:doi/10.2196/58779,
author="Liu, Guanghao
and Zheng, Shixiang
and He, Jun
and Zhang, Zi-Mei
and Wu, Ruoqiong
and Yu, Yingying
and Fu, Hao
and Han, Li
and Zhu, Haibo
and Xu, Yichang
and Shao, Huaguo
and Yan, Haidan
and Chen, Ting
and Shen, Xiaopei",
title="An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="6",
volume="27",
pages="e58779",
keywords="septic shock; early forewarning; risk stratification; machine learning",
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. ",
issn="1438-8871",
doi="10.2196/58779",
url="https://www.jmir.org/2025/1/e58779",
url="https://doi.org/10.2196/58779"
}