Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study

Abstract

Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email [email protected]; [email protected]: 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

Keywords

• multi-drug resistant bacteria
• risk factors
• prediction model.