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 aim...
| Publicado en: | Infection Vol. 53; no. 4; pp. 1425 - 1436 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187120468&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187120468 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03008126 NXO jtl: Infection issn: 03008126 maglogo: N pubinfo: dt: Aug2025 vid: 53 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187120468 182769112 187120468 187120468 10.1007/s15010-024-02465-w 187120468 ppf: 1425 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting the risk of invasive fungal infections in ICU sepsis population: the AMI risk assessment tool. aug: au: Jin, Wenyi Yang, Donglin Xu, Zhe Song, Jiaze Jin, Haijuan Zhou, Xiaoming Liu, Chen Wu, Hao Cheng, Qianhui Yang, Jingwen Lin, Jiaying Wang, Liang Chen, Chan Wang, Zhiyi Weng, Jie affil: https://ror.org/0156rhd17 Department of General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, 325027, Wenzhou, China sug: subj: Mycoses Risk Factors Risk Assessment Methods Intensive Care Units Sepsis Complications Mycoses Etiology Predictive Value of Tests Mycoses Diagnosis Early Diagnosis Prediction Models Evaluation Human Funding Source Male Female Adult Middle Age Aged Retrospective Design Record Review Decision Trees Descriptive Statistics Data Analysis Software Confidence Intervals Chi Square Test Respiration, Artificial Immunosuppressive Agents Antibiotics Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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