ArticleFrontiers in cellular and infection microbiology2026
Identification of risk factors and construction of a prediction model for multidrug-resistant organism infections in neutropenic patients.
Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Multidrug-resistant organisms (MDROs) pose a severe threat to neutropenic patients with compromised immunity, leading to poor outcomes and increased healthcare burdens. This study aimed to develop and validate a prediction model for MDRO infections in this population. Methods: A total of 391 neutropenic patients (206 in training cohort, 185 in validation cohort) admitted to Ningbo Medical Center Lihuili Hospital, from January 2023 to December 2024 were enrolled. Demographic, clinical, and outcome data were collected. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses were used to identify independent risk factors, and a nomogram was constructed for prediction. Model performance was evaluated via receiver operating characteristic (ROC),calibration curves and decision curve analysis (DCA). Results: MDRO infection occurred in 14.1% of patients, associated with longer hospital stays, higher costs, and increased mortality. Independent risk factors included comorbid cardiac disease (OR = 13.500, 95%CI: 2.484-73.384, P = 0.003), ECOG score≥2 (OR = 3.210, 95%CI: 1.114-9.255, P = 0.031), neutropenia duration≥7 days (OR = 4.028, 95%CI: 1.399-11.600, P = 0.010), and broad-spectrum antibiotic use in 3 months prior (OR = 13.053, 95%CI: 2.419-70.441, P = 0.003). The nomogram demonstrated good discriminative ability, with an area under the ROC curve of 0.874 in the training cohort and 0.764 in the validation cohort. Calibration curves confirmed favorable prediction accuracy and the DCA showed good clinical applicability. Conclusions: This study identifies key risk factors and provides a practical predictive tool for early identification of high-risk patients, enabling targeted interventions to reduce MDRO infections, improve patient outcomes, and alleviate healthcare burdens.
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