预测ICU脓毒症人群侵袭性真菌感染风险：AMI风险评估工具

IF 5.4 2区医学 Q1 INFECTIOUS DISEASES

Infection Pub Date : 2025-02-03 DOI:10.1007/s15010-024-02465-w

Wenyi Jin, Donglin Yang, Zhe Xu, Jiaze Song, Haijuan Jin, Xiaoming Zhou, Chen Liu, Hao Wu, Qianhui Cheng, Jingwen Yang, Jiaying Lin, Liang Wang, Chan Chen, Zhiyi Wang, Jie Weng

{"title":"预测ICU脓毒症人群侵袭性真菌感染风险：AMI风险评估工具","authors":"Wenyi Jin, Donglin Yang, Zhe Xu, Jiaze Song, Haijuan Jin, Xiaoming Zhou, Chen Liu, Hao Wu, Qianhui Cheng, Jingwen Yang, Jiaying Lin, Liang Wang, Chan Chen, Zhiyi Wang, Jie Weng","doi":"10.1007/s15010-024-02465-w","DOIUrl":null,"url":null,"abstract":"Background: Invasive fungal infections (IFI) represent a significant contributor to mortality among sepsis patients in the Intensive Care Unit (ICU). Early diagnosis of IFI is challenging, and currently, there are no predictive tools for identifying sepsis patients who may develop IFI. Our study aims to develop a predictive scoring system to assess the risk of IFI in patients with sepsis admitted to the ICU.Methods: A retrospective collection of data from a total of 549 patients was conducted. Data-driven, clinically knowledge-driven, and decision tree models were used to identify predictive variables for risk of IFI in ICU patients with sepsis. Demographic data, vital signs, laboratory values, comorbidities, medication use, and clinical outcomes were all collected. The optimal model was selected based on model performance and clinical utility to establish a risk score.Results: Among adult patients with sepsis admitted to the ICU, 127 patients (23.1%) developed IFI. The final data-driven model included four predictive factors, the clinically knowledge-driven model included three predictive factors, and the decision tree model included two. Based on the good performance and clinical utility of the clinically knowledge-driven model, it was chosen as the optimal risk scoring model (C-statistics: 0.79 (95% confidence interval (CI): 0.75-0.83); Hosmer-Lemeshow (H-L) test P = 0.884). The ICU sepsis patient invasive fungal infection risk (AMI) score, created based on the clinically knowledge-driven model, includes mechanical ventilation, application of immunosuppressants, and the types of antibiotics used. The C-statistics for this risk score was 0.79 (95% CI:0.75-0.84) with good calibration (H-L test P = 0.992 and see calibration curve: Fig. 2). Moreover, in terms of clinical utility, the decision curve analysis for AMI showed a favorable net benefit.Conclusions: The application of the AMI score can effectively distinguish whether ICU sepsis patients will develop IFI, which is beneficial for clinicians to formulate targeted and timely preventive and treatment measures based on the risk of IFI.","PeriodicalId":13600,"journal":{"name":"Infection","volume":" ","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting the risk of invasive fungal infections in ICU sepsis population: the AMI risk assessment tool.\",\"authors\":\"Wenyi Jin, Donglin Yang, Zhe Xu, Jiaze Song, Haijuan Jin, Xiaoming Zhou, Chen Liu, Hao Wu, Qianhui Cheng, Jingwen Yang, Jiaying Lin, Liang Wang, Chan Chen, Zhiyi Wang, Jie Weng\",\"doi\":\"10.1007/s15010-024-02465-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Invasive fungal infections (IFI) represent a significant contributor to mortality among sepsis patients in the Intensive Care Unit (ICU). Early diagnosis of IFI is challenging, and currently, there are no predictive tools for identifying sepsis patients who may develop IFI. Our study aims to develop a predictive scoring system to assess the risk of IFI in patients with sepsis admitted to the ICU.Methods: A retrospective collection of data from a total of 549 patients was conducted. Data-driven, clinically knowledge-driven, and decision tree models were used to identify predictive variables for risk of IFI in ICU patients with sepsis. Demographic data, vital signs, laboratory values, comorbidities, medication use, and clinical outcomes were all collected. The optimal model was selected based on model performance and clinical utility to establish a risk score.Results: Among adult patients with sepsis admitted to the ICU, 127 patients (23.1%) developed IFI. The final data-driven model included four predictive factors, the clinically knowledge-driven model included three predictive factors, and the decision tree model included two. Based on the good performance and clinical utility of the clinically knowledge-driven model, it was chosen as the optimal risk scoring model (C-statistics: 0.79 (95% confidence interval (CI): 0.75-0.83); Hosmer-Lemeshow (H-L) test P = 0.884). The ICU sepsis patient invasive fungal infection risk (AMI) score, created based on the clinically knowledge-driven model, includes mechanical ventilation, application of immunosuppressants, and the types of antibiotics used. The C-statistics for this risk score was 0.79 (95% CI:0.75-0.84) with good calibration (H-L test P = 0.992 and see calibration curve: Fig. 2). Moreover, in terms of clinical utility, the decision curve analysis for AMI showed a favorable net benefit.Conclusions: The application of the AMI score can effectively distinguish whether ICU sepsis patients will develop IFI, which is beneficial for clinicians to formulate targeted and timely preventive and treatment measures based on the risk of IFI.\",\"PeriodicalId\":13600,\"journal\":{\"name\":\"Infection\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infection\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s15010-024-02465-w\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infection","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s15010-024-02465-w","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}

引用次数: 0

摘要

背景：侵袭性真菌感染（IFI）是重症监护病房（ICU）脓毒症患者死亡率的重要因素。IFI的早期诊断具有挑战性，目前还没有预测工具来识别可能发展为IFI的败血症患者。我们的研究旨在开发一个预测评分系统来评估ICU收治的脓毒症患者IFI的风险。方法：回顾性收集549例患者的资料。使用数据驱动、临床知识驱动和决策树模型来识别ICU脓毒症患者IFI风险的预测变量。收集了人口统计数据、生命体征、实验室值、合并症、药物使用和临床结果。根据模型性能和临床效用选择最优模型，建立风险评分。结果：ICU收治的成年脓毒症患者中，127例（23.1%）发生IFI。最终数据驱动模型包括4个预测因素，临床知识驱动模型包括3个预测因素，决策树模型包括2个预测因素。基于临床知识驱动模型的良好表现和临床效用，选择该模型作为最佳风险评分模型(c -统计量：0.79(95%置信区间（CI）： 0.75 ~ 0.83)；Hosmer-Lemeshow （H-L）检验P = 0.884)。ICU脓毒症患者侵袭性真菌感染风险（AMI）评分基于临床知识驱动模型，包括机械通气、免疫抑制剂应用、抗生素使用类型。该风险评分的c统计量为0.79 (95% CI:0.75-0.84)，校正效果良好（H-L检验P = 0.992，见校正曲线：图2）。此外，在临床应用方面，AMI的决策曲线分析显示出良好的净收益。结论：AMI评分的应用可以有效区分ICU脓毒症患者是否会发生IFI，有利于临床医生根据IFI风险制定有针对性、及时的防治措施。

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

Predicting the risk of invasive fungal infections in ICU sepsis population: the AMI risk assessment tool.

Background: Invasive fungal infections (IFI) represent a significant contributor to mortality among sepsis patients in the Intensive Care Unit (ICU). Early diagnosis of IFI is challenging, and currently, there are no predictive tools for identifying sepsis patients who may develop IFI. Our study aims to develop a predictive scoring system to assess the risk of IFI in patients with sepsis admitted to the ICU.

Methods: A retrospective collection of data from a total of 549 patients was conducted. Data-driven, clinically knowledge-driven, and decision tree models were used to identify predictive variables for risk of IFI in ICU patients with sepsis. Demographic data, vital signs, laboratory values, comorbidities, medication use, and clinical outcomes were all collected. The optimal model was selected based on model performance and clinical utility to establish a risk score.

Results: Among adult patients with sepsis admitted to the ICU, 127 patients (23.1%) developed IFI. The final data-driven model included four predictive factors, the clinically knowledge-driven model included three predictive factors, and the decision tree model included two. Based on the good performance and clinical utility of the clinically knowledge-driven model, it was chosen as the optimal risk scoring model (C-statistics: 0.79 (95% confidence interval (CI): 0.75-0.83); Hosmer-Lemeshow (H-L) test P = 0.884). The ICU sepsis patient invasive fungal infection risk (AMI) score, created based on the clinically knowledge-driven model, includes mechanical ventilation, application of immunosuppressants, and the types of antibiotics used. The C-statistics for this risk score was 0.79 (95% CI:0.75-0.84) with good calibration (H-L test P = 0.992 and see calibration curve: Fig. 2). Moreover, in terms of clinical utility, the decision curve analysis for AMI showed a favorable net benefit.

Conclusions: The application of the AMI score can effectively distinguish whether ICU sepsis patients will develop IFI, which is beneficial for clinicians to formulate targeted and timely preventive and treatment measures based on the risk of IFI.

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

Infection 医学-传染病学

CiteScore

12.50

自引率

1.30%

发文量

224

审稿时长

6-12 weeks

期刊介绍： Infection is a journal dedicated to serving as a global forum for the presentation and discussion of clinically relevant information on infectious diseases. Its primary goal is to engage readers and contributors from various regions around the world in the exchange of knowledge about the etiology, pathogenesis, diagnosis, and treatment of infectious diseases, both in outpatient and inpatient settings. The journal covers a wide range of topics, including: Etiology: The study of the causes of infectious diseases. Pathogenesis: The process by which an infectious agent causes disease. Diagnosis: The methods and techniques used to identify infectious diseases. Treatment: The medical interventions and strategies employed to treat infectious diseases. Public Health: Issues of local, regional, or international significance related to infectious diseases, including prevention, control, and management strategies. Hospital Epidemiology: The study of the spread of infectious diseases within healthcare settings and the measures to prevent nosocomial infections. In addition to these, Infection also includes a specialized "Images" section, which focuses on high-quality visual content, such as images, photographs, and microscopic slides, accompanied by brief abstracts. This section is designed to highlight the clinical and diagnostic value of visual aids in the field of infectious diseases, as many conditions present with characteristic clinical signs that can be diagnosed through inspection, and imaging and microscopy are crucial for accurate diagnosis. The journal's comprehensive approach ensures that it remains a valuable resource for healthcare professionals and researchers in the field of infectious diseases.