ArticleFrontiers in cellular and infection microbiology2026
From microbial dynamics to risk prediction: a nomogram-based model for hospital-acquired infections in rehabilitation settings.
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: To analyze microbial infection patterns and develop a predictive model for hospital-acquired infection (HAI) in rehabilitation inpatients. Methods: A retrospective cohort study included 635 patients admitted between August 2018 and February 2025; 4,523 clinical specimens were analyzed. After exclusions, 361 patients were classified into HAI (n=213) and non-HAI (n=148) groups. Significant variables from univariate analysis were incorporated into LASSO and logistic regression to build a prediction model, which was visualized as a nomogram. A simplified scoring tool and a web application were developed. External validation was performed using 332 patients from three hospitals. Results: Among 4,523 specimens from 635 rehabilitation inpatients, the overall positivity rate was 61.2%. Sputum cultures were most frequent, while urine cultures increased over time. Key pathogens like Conclusion: Our findings elucidate key microbiological patterns and predictive factors for HAI in rehabilitation inpatients. The developed model, utilizing readily available clinical parameters, shows robust and generalizable performance in stratifying infection risk, which can aid early intervention and optimize resource allocation in rehabilitation care.
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