Development and validation of a predictive nomogram for hospitalized patients with multidrug-resistant pathogen-associated lower respiratory infections

Objective Antimicrobial resistance (AMR) threatens global health. Multidrug-resistant pathogen-associated lower respiratory infections(MDR-LRIs) cause poor clinical outcomes. Existing models lack generalizability. This study developed and validated a risk-prediction model for respiratory inpatients using clinical data to guide early screening and rational antibiotic therapy. Methods This retrospective study collected 3186 bronchoalveolar lavage fluid samples from a large tertiary general hospital in Southwest China between January 2024 and May 2026. A total of 333 patients with lower respiratory infections were finally enrolled, including 156 multidrug-resistant cases and 177 drug-susceptible cases. Collected data covered demographic characteristics, comorbidities, invasive procedures, history of antibiotic use and laboratory findings. Lasso regression was used to screen key predictors, and logistic regression was applied to construct the prediction model. Model performance was evaluated via ROC curve analysis, calibration test and bootstrap validation. Results Among multidrug‑resistant bacterial cases, Pseudomonas aeruginosa (28.84%), Klebsiella pneumoniae (23.08%) and Acinetobacter baumannii (18.59%) accounted for the largest proportions. Eight independent risk factors with the highest weight were identified: age,invasive procedures,Non-invasive ventilator support,neurological disorders,combination of two or more antibiotics,combined antifungal therapy,hemoglobin,serum albumin. The model achieved favorable predictive accuracy, with AUC values of 0.885 (95% confidence interval [CI]: 0.8407–0.9293) and 0.870 (95% CI: 0.8009–0.9389) in the training and validation sets, respectively. Calibration analysis revealed good consistency of the model (Hosmer‑Lemeshow test, P = 0.2974). Conclusion The clinical prediction model developed and validated in this study, which incorporates iatrogenic interventions and antibiotic exposure, enables early identification of patients at high risk of MDR-LRIs, reduces infection incidence, and provides evidence-based support for hospital infection control and public health policies.