Development and validation of a nomogram model for predicting MDRO infections in elderly ICU patients with pulmonary infections.

Background: MDRO infections are increasingly problematic in ICUs, especially among elderly patients with lung infections, but knowledge about these infections in this group is limited. This study aimed to assess the status and risk factors of MDRO infections in elderly ICU patients and develop a risk prediction model to aid clinical decisions.

Methods: Using a retrospective cohort study, a total of 494 elderly patients with lung infections admitted to the ICU from January 2017 to December 2022 were selected, and the patients were divided into the MDRO group (259) and the non-MDRO group (235) based on whether or not the patients developed MDRO infections. Lasso and multifactorial logistic regression were applied to analyze the independent risk factors for multidrug-resistant bacterial infections in elderly patients with pulmonary infections, and to construct a nomogram model of the risk of MDRO infections. The differentiation, consistency and clinical benefit of the model were evaluated by receiver operating characteristic curve(ROC), calibration curves and decision curve analysis, respectively, and the stability of the model was verified by Bootstrap method.

Results: Duration of hospitalization before MDRO diagnosis, chronic obstructive pulmonary disease, personal history of cerebrovascular disease, tracheotomy and prior carbapenem exposure were found to be independent risk factors for multidrug-resistant bacterial infections in elderly patients with pulmonary infections in the intensive care unit (all p < 0.05). The nomogram model, constructed based on the results of logistic regression analysis, exhibited an area under the ROC curve of 0.748 with a 95% confidence interval of 0.705-0.790. The Hosmer-Lemeshow test indicated that the model predicted a good fit (p = 0.75), and the DCA curve suggested that the model had a good clinical utility.

Conclusion: Risk prediction model is effective in predicting the risk of MDRO infection in the ICU elderly pulmonary infection population and can be used to assess risk and inform preventive treatment and nursing interventions.