Work

Evaluation of the Efficacy of a Nomogram to Predict Multidrug-Resistant Pulmonary Infections Based on Data from Neurosurgery Ward Patients

Year: 2025

Type: article

Abstract: Objective: This study aimed to construct and evaluate a nomogram based on data from neurosurgery ward patients to predict the probability of multidrug-resistant (MDR) pneumonia occurrence. Methods: We retrospectively collected clinical data, early laboratory test results, and physician prescriptions for 35 variables from patients. Univariate and stepwise regression analyses were used to screen variables to determine predictive factors, and a nomogram was constructed in the training group based on the results of the logistic regression model. Using the validation group, discrimination, calibration, and clinical applicability were assessed based on the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results: , the Day 1 neutrophil-to-lymphocyte ratio (NLR), albumin≤30 g/L, hemoglobin, combination of antibacterial drugs, and tracheostomy), which demonstrated significantly higher sensitivity and specificity in the early identification of MDR pneumonia (AUC of the training group = 0.816 (95% CI: 0.760-0.862), AUC of the validation group = 0.797 (95% CI: 0.720-0.874)) and good calibration. DCA confirmed the clinical applicability of this nomogram. Conclusion: We propose for the first time that augmented renal clearance (ARC) is an independent risk factor for the occurrence of MDR pneumonia in neurosurgical patients. Moreover, we successfully established a convenient prediction model that consists of six prediction factors, which can assist neurosurgeons in making early predictions of the incidence of MDR pneumonia.

Source: Infection and Drug Resistance

DOI: 10.2147/idr.s527114

Authors: Ran Zhou, Xiaolong Chen, Hengmin Jia, Wen Duan

Institution: University of Science and Technology of China

Language: English

Topic: Nosocomial Infections in ICU

Subfield: Critical Care and Intensive Care Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,836