脓毒症患者基于年龄和合并症的预后风险模型评估：大规模、多中心、回顾性研究中机器学习和传统方法的见解

IF 2.7 3区医学 Q2 CRITICAL CARE MEDICINE

SHOCK Pub Date : 2025-02-07 DOI:10.1097/SHK.0000000000002562

Guoxiang Liu, Zhaoming Shang, Ning Ning, Juan Li, Wenwu Sun, Yiwen Fan, Yiran Guo, Jiawei Ye, Wenzhen Zhou, Junwei Qian, Chaoping Ma, Jiyuan Zhang, Xiaofei Jiang, Changqin Zhu, Enqiang Mao, Mingquan Chen, Chengjin Gao

{"title":"脓毒症患者基于年龄和合并症的预后风险模型评估：大规模、多中心、回顾性研究中机器学习和传统方法的见解","authors":"Guoxiang Liu, Zhaoming Shang, Ning Ning, Juan Li, Wenwu Sun, Yiwen Fan, Yiran Guo, Jiawei Ye, Wenzhen Zhou, Junwei Qian, Chaoping Ma, Jiyuan Zhang, Xiaofei Jiang, Changqin Zhu, Enqiang Mao, Mingquan Chen, Chengjin Gao","doi":"10.1097/SHK.0000000000002562","DOIUrl":null,"url":null,"abstract":"Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-adjusted Charlson Comorbidity Index (ACCI) and its simplified version, the quick ACCI (qACCI), for mortality in septic patients.Methods: This retrospective study included septic patients from four Chinese medical centers. The internal validation cohort comprised patients from Xinhua Hospital, Ruijin Hospital, and Huashan Hospital, while participants from Renji Hospital served as the external validation cohort. Machine learning models identified ACCI's feature importance. Restricted cubic spline regression and subgroup analysis assess the correlation between ACCI and mortality risk. The qACCI, derived from the ACCI components, was also evaluated for predictive reliability.Results: A total of 3,287 septic patients were included: 2,974 in the internal cohort (mean age 67.96 years; 37.5% male) and 313 in the external cohort (mean age 67.90 years; 48.2% male). Machine learning models identified ACCI as a key predictor of in-hospital mortality. A linear correlation was confirmed between ACCI and risks of in-hospital, 30-day, and ICU mortality. Sensitivity analysis revealed consistent results across subgroups, demonstrating significantly higher mortality risks in the moderate- (HR 2.18, 95% CI 1.77-2.70) and high-ACCI (HR 3.72, 95% CI 2.99-4.65) groups compared to the low-ACCI group (HR 1, Reference). The ACCI achieved an AUC of 0.788 for in-hospital mortality, outperforming the SOFA in gastrointestinal (0.831 vs. 0.794) and central nervous system infections (0.803 vs. 0.739). The qACCI showed moderate predictive performance in both the internal (AUC, 0.734) and external (AUC, 0.758) cohorts.Conclusions: As composite indicators of age and comorbidity, ACCI and qACCI provide valuable and reliable tools for clinicians to identify high-risk patients early.","PeriodicalId":21667,"journal":{"name":"SHOCK","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of Prognostic Risk Models Based on Age and Comorbidity in Septic Patients: Insights from Machine Learning and Traditional Methods in a Large-Scale, Multicenter, Retrospective Study.\",\"authors\":\"Guoxiang Liu, Zhaoming Shang, Ning Ning, Juan Li, Wenwu Sun, Yiwen Fan, Yiran Guo, Jiawei Ye, Wenzhen Zhou, Junwei Qian, Chaoping Ma, Jiyuan Zhang, Xiaofei Jiang, Changqin Zhu, Enqiang Mao, Mingquan Chen, Chengjin Gao\",\"doi\":\"10.1097/SHK.0000000000002562\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-adjusted Charlson Comorbidity Index (ACCI) and its simplified version, the quick ACCI (qACCI), for mortality in septic patients.Methods: This retrospective study included septic patients from four Chinese medical centers. The internal validation cohort comprised patients from Xinhua Hospital, Ruijin Hospital, and Huashan Hospital, while participants from Renji Hospital served as the external validation cohort. Machine learning models identified ACCI's feature importance. Restricted cubic spline regression and subgroup analysis assess the correlation between ACCI and mortality risk. The qACCI, derived from the ACCI components, was also evaluated for predictive reliability.Results: A total of 3,287 septic patients were included: 2,974 in the internal cohort (mean age 67.96 years; 37.5% male) and 313 in the external cohort (mean age 67.90 years; 48.2% male). Machine learning models identified ACCI as a key predictor of in-hospital mortality. A linear correlation was confirmed between ACCI and risks of in-hospital, 30-day, and ICU mortality. Sensitivity analysis revealed consistent results across subgroups, demonstrating significantly higher mortality risks in the moderate- (HR 2.18, 95% CI 1.77-2.70) and high-ACCI (HR 3.72, 95% CI 2.99-4.65) groups compared to the low-ACCI group (HR 1, Reference). The ACCI achieved an AUC of 0.788 for in-hospital mortality, outperforming the SOFA in gastrointestinal (0.831 vs. 0.794) and central nervous system infections (0.803 vs. 0.739). The qACCI showed moderate predictive performance in both the internal (AUC, 0.734) and external (AUC, 0.758) cohorts.Conclusions: As composite indicators of age and comorbidity, ACCI and qACCI provide valuable and reliable tools for clinicians to identify high-risk patients early.\",\"PeriodicalId\":21667,\"journal\":{\"name\":\"SHOCK\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SHOCK\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/SHK.0000000000002562\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SHOCK","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/SHK.0000000000002562","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}

引用次数: 0

摘要

背景：年龄和合并症显著影响脓毒症患者的预后并影响治疗决策。为了给临床医生提供识别高危患者的有效工具，本研究评估了年龄调整Charlson共病指数（ACCI）及其简化版快速ACCI （qACCI）对脓毒症患者死亡率的预测价值。方法：回顾性研究了来自中国四家医疗中心的脓毒症患者。内部验证队列由新华医院、瑞金医院和华山医院的患者组成，外部验证队列由仁济医院的患者组成。机器学习模型确定了ACCI的特征重要性。限制性三次样条回归和亚组分析评估ACCI与死亡风险之间的相关性。由ACCI组成的qACCI也被评估为预测可靠性。结果：共纳入3287例脓毒症患者：内部队列2974例(平均年龄67.96岁；37.5%为男性)，外部队列313例(平均年龄67.90岁；48.2%的男性)。机器学习模型将ACCI确定为住院死亡率的关键预测因子。ACCI与住院死亡率、30天死亡率和ICU死亡率之间存在线性相关。敏感性分析显示各亚组的结果一致，显示中等acci组（HR 2.18, 95% CI 1.77-2.70）和高acci组（HR 3.72, 95% CI 2.99-4.65）的死亡风险明显高于低acci组（HR 1，参考文献）。ACCI在住院死亡率方面的AUC为0.788，优于胃肠道感染（0.831比0.794）和中枢神经系统感染（0.803比0.739）的SOFA。qACCI在内部（AUC, 0.734）和外部（AUC, 0.758）队列中均显示出中等的预测性能。结论：ACCI和qACCI作为年龄和合并症的复合指标，为临床医生早期识别高危患者提供了有价值、可靠的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Evaluation of Prognostic Risk Models Based on Age and Comorbidity in Septic Patients: Insights from Machine Learning and Traditional Methods in a Large-Scale, Multicenter, Retrospective Study.

Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-adjusted Charlson Comorbidity Index (ACCI) and its simplified version, the quick ACCI (qACCI), for mortality in septic patients.

Methods: This retrospective study included septic patients from four Chinese medical centers. The internal validation cohort comprised patients from Xinhua Hospital, Ruijin Hospital, and Huashan Hospital, while participants from Renji Hospital served as the external validation cohort. Machine learning models identified ACCI's feature importance. Restricted cubic spline regression and subgroup analysis assess the correlation between ACCI and mortality risk. The qACCI, derived from the ACCI components, was also evaluated for predictive reliability.

Results: A total of 3,287 septic patients were included: 2,974 in the internal cohort (mean age 67.96 years; 37.5% male) and 313 in the external cohort (mean age 67.90 years; 48.2% male). Machine learning models identified ACCI as a key predictor of in-hospital mortality. A linear correlation was confirmed between ACCI and risks of in-hospital, 30-day, and ICU mortality. Sensitivity analysis revealed consistent results across subgroups, demonstrating significantly higher mortality risks in the moderate- (HR 2.18, 95% CI 1.77-2.70) and high-ACCI (HR 3.72, 95% CI 2.99-4.65) groups compared to the low-ACCI group (HR 1, Reference). The ACCI achieved an AUC of 0.788 for in-hospital mortality, outperforming the SOFA in gastrointestinal (0.831 vs. 0.794) and central nervous system infections (0.803 vs. 0.739). The qACCI showed moderate predictive performance in both the internal (AUC, 0.734) and external (AUC, 0.758) cohorts.

Conclusions: As composite indicators of age and comorbidity, ACCI and qACCI provide valuable and reliable tools for clinicians to identify high-risk patients early.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

SHOCK 医学-外科

CiteScore

6.20

自引率

3.20%

发文量

199

审稿时长

1 months

期刊介绍： SHOCK®: Injury, Inflammation, and Sepsis: Laboratory and Clinical Approaches includes studies of novel therapeutic approaches, such as immunomodulation, gene therapy, nutrition, and others. The mission of the Journal is to foster and promote multidisciplinary studies, both experimental and clinical in nature, that critically examine the etiology, mechanisms and novel therapeutics of shock-related pathophysiological conditions. Its purpose is to excel as a vehicle for timely publication in the areas of basic and clinical studies of shock, trauma, sepsis, inflammation, ischemia, and related pathobiological states, with particular emphasis on the biologic mechanisms that determine the response to such injury. Making such information available will ultimately facilitate improved care of the traumatized or septic individual.