An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study
BackgroundSeptic 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. ObjectiveWe propose a simple and fast risk-stratified forewarning model fo...
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JMIR Publications
2025-02-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e58779 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Guanghao Liu |
| collection | DOAJ |
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BackgroundSeptic 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.
ObjectiveWe 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.
MethodsA 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.
ResultsUsing 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.
ConclusionsThe 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. |
| format | Article |
| id | doaj-art-1b81c38d76574faa9956bedaea8b90d3 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-1b81c38d76574faa9956bedaea8b90d32025-02-06T17:31:33ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-02-0127e5877910.2196/58779An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation StudyGuanghao Liuhttps://orcid.org/0000-0001-8386-5410Shixiang Zhenghttps://orcid.org/0000-0001-9543-9953Jun Hehttps://orcid.org/0000-0003-1510-1093Zi-Mei Zhanghttps://orcid.org/0000-0003-3428-9482Ruoqiong Wuhttps://orcid.org/0009-0000-0195-2079Yingying Yuhttps://orcid.org/0009-0008-6124-7791Hao Fuhttps://orcid.org/0009-0003-2725-8319Li Hanhttps://orcid.org/0009-0003-0280-5398Haibo Zhuhttps://orcid.org/0009-0002-3371-9321Yichang Xuhttps://orcid.org/0009-0000-7142-8344Huaguo Shaohttps://orcid.org/0009-0000-7633-6879Haidan Yanhttps://orcid.org/0000-0003-0214-7471Ting Chenhttps://orcid.org/0000-0002-3228-9166Xiaopei Shenhttps://orcid.org/0000-0002-0004-4228 BackgroundSeptic 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. ObjectiveWe 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. MethodsA 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. ResultsUsing 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. ConclusionsThe 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.https://www.jmir.org/2025/1/e58779 |
| spellingShingle | 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 An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study Journal of Medical Internet Research |
| title | An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study |
| title_full | An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study |
| title_fullStr | An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study |
| title_full_unstemmed | An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study |
| title_short | An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study |
| title_sort | easy and quick risk stratified early forewarning model for septic shock in the intensive care unit development validation and interpretation study |
| url | https://www.jmir.org/2025/1/e58779 |
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