Purpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.
Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.
Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.
Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.
Keywords: multi-drug resistant bacteria, risk factors, prediction model