AUTHOR=Qi Zhili , Dong Lei , Lin Jin , Duan Meili 

TITLE=Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2024.1348896

DOI=10.3389/fcimb.2024.1348896

ISSN=2235-2988

ABSTRACT=Purpose: This study aims to develop and validation a nomogram for predicting the risk of bloodstream infections(BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU).: Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital(BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram.Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility. Results: The model dataset consisted of 16355 patients (1583 BSI patients) from the MIMIC-IV database, which were divided into the training dataset and the internal validation dataset in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 437 patients (21 with BSI )as the BFH validation dataset.The nomogram based on Glasgow Coma Scale(GCS), sepsis related organ failure assessment (SOFA), temperature, heart rate, respiratory rate, white blood cell(WBC), red width of distribution(RDW), renal replacement therapy and presence of severe liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness. Conclusion: Using the nomogram developed in this study to estimate individual risk, clinicians and nurses can identify patients at high risk for bloodstream infections in ICU.