ArticleEuropean journal of medical research2025
Development and validation of a nomogram to predict bacterial blood stream infection.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development and Validation of a Nomogram Model for Predicting the Severity of Post-Stroke Dysphagia Based on fNIRS-Derived Brain Functional Connectivity Features.Brain and behavior · 2026Article
- The Predictive Value of Nomogram Model Based on Neutrophil-to-Lymphocyte Ratio (NLR) and Prothrombin Time-International Normalized Ratio (PT-INR)-to-Albumin Ratio (PTAR) for Early Bacterial Intra-Abdominal Infection After Orthotopic Liver Transplantation.Journal of inflammation research · 2026Article
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9 authors.
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Abstract
objectiveTo identify the risk factors of bacterial blood stream infection (BSI) and construct a nomogram to predict the occurrence of bacterial BSI.
methodsBlood stream infection is characterized by a systemic infection patient with positive blood culture and has one or more clinical symptoms, such as fever (body temperature > 38 °C) or hypothermia (body temperature < 36 °C), chills, hypotension, oliguria, or high lactic acid levels. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest algorithms were utilized to identify the potential risk factors for BSI. Independent risk factors identified by multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.
resultsA total of 195 bacterial BSI patients were enrolled. gender, Acute Physiology and Chronic Health Evaluation-II (APACHEII) score, nCD64 index, erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), lymphocyte count, T-cell count, B-cell count, NK-cell count, Interleukin-8 (IL-8), Interleukin-10 (IL-10) and Interleukin-17A(IL-17A) were independent risk factors for BSI. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.836 (95% CI 0.653-0.874) in the training set and 0.871 (95% CI 0.793-0.861) in the validation set. The calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting bacterial BSI.
conclusionOverall, this study successfully identified five risk factors for BSI patients and developed a nomogram, offering individualized diagnosis and risk assessment to predict bacterial BSI in infected patients.
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