Risk Factors and Prognosis Analyses of Hospital-Acquired Pneumonia in Elderly Critically Ill Patients with Acute Ischemic Stroke Based on Machine Learning

Qingxin Jiao,1 Xingyu Liu,2 Huimin Chen,3,4 Ziqi Hu,5 Shengyuan Jiao,6 Zhongyang Sun,7,8 Conglan Lu,7 Limin Huang,3,9 Wenxiu Du,10 Dongsheng Jiao5 1Department of Research, Xi’an Medical University, Xi’an, Shaanxi, 710021, People’s Republic of China; 2Department of General Medicine, Central Medical Branch of PLA General Hospital, Beijing, 100120, People’s Republic of China; 3Department of Emergency Medicine, Nanjing Pukou People’s Hospital, Nanjing, Jiangsu, 211800, People’s Republic of China; 4Department of Emergency, the Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210000, People’s Republic of China; 5Department of Neurology, Air Force Hospital of Eastern Theater Command, Nanjing, Jiangsu, 210002, People’s Republic of China; 6Department of Radiation Medical Protection, School of Military Preventive Medicine, Air Force Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 7Department of Orthopedics, Air Force Hospital of Eastern Theater Command, Nanjing, Jiangsu, 210002, People’s Republic of China; 8Department of Orthopedics, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, People’s Republic of China; 9Department of Emergency Medicine, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu, 211166, People’s Republic of China; 10Department of Emergency Medicine, Women’s Hospital of Nanjing Medical University (Nanjing Women and Children’s Healthcare Hospital), Nanjing, Jiangsu, 210000, People’s Republic of ChinaCorrespondence: Wenxiu Du, Department of Emergency Medicine, Women’s Hospital of Nanjing Medical University (Nanjing Women and Children’s Healthcare Hospital), Nanjing, Jiangsu, 210000, People’s Republic of China, Email [email protected] Dongsheng Jiao, Department of Neurology, Air Force Hospital of Eastern Theater, Nanjing, Jiangsu, 210002, People’s Republic of China, Email [email protected]: Increased post-stroke sympathetic drive is linked to hospital-acquired pneumonia (HAP). This study investigated the incidence, prognosis, and risk factors of HAP in elderly critically ill acute ischemic stroke (AIS) patients.Methods: We analyzed HAP risk factors and prognosis in critically ill AIS patients (aged > 50, NIHSS > 15) from the First Affiliated Hospital of Xi’an Medical University (September 2023–February 2024). Nine factors from 19 variables were selected, with 11 machine learning algorithms for HAP risk prediction. Kaplan–Meier survival estimate, Cox proportional hazards model, 10-fold cross-validation, Friedman and post-hoc Nemenyi tests were used for prognosis analysis and algorithm selection. SHapley Additive explanation values explained feature weights.Results: Of 785 patients, 215 (27.39%) developed HAP, 40.38% were > 80 years, 67.01% male, with 30.68% overall mortality. Key predictive variables included respiratory failure, hospital stays, consecutive febrile days, number of bacteria, antibiotics, CRP, immunopotentiator, blood transfusion, and ICU admission. XGBoost performed best (AUC: 0.995 [0.995– 0.996] training sets, 0.898 [0.891– 0.905] validation sets). HAP, respiratory failure, number of bacteria and ICU admission deteriorated survival, longer hospital stays improved prognosis. Top 3 features via SHAP were number of bacteria, ICU admission and consecutive febrile days.Conclusion: Elderly critically ill AIS patients with HAP are more prone to respiratory failure, prolonged fever, blood transfusion, ICU admission, or death. The number of bacteria-positive species and elevated CRP levels (≥ 5 mg/L) were identified as among the most significant predictors associated with the development of HAP in our model. Administration of antibiotics and immunopotentiators was significantly associated with improved prognosis in our cohort. However, further interventional studies are required to confirm a causal therapeutic benefit.Keywords: hospital-acquired pneumonia, acute ischemic stroke, machine learning, risk factor, elderly adults