Chunjing Du,1,2,* Hua Zhang,3,* Yi Zhang,1,4,* Hanwen Zhang,1,4 Jiajia Zheng,5 Chao Liu,6 Fengmin Lu,7 Ning Shen1,6 

1Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People’s Republic of China; 2Department of Critical Care Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People’s Republic of China; 3Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, People’s Republic of China; 4Peking University Health Science Center, Peking University, Beijing, 100191, People’s Republic of China; 5Department of Laboratory Medicine, Peking University Third Hospital, Beijing, 100191, People’s Republic of China; 6Department of Infectious Diseases, Peking University Third Hospital, Beijing, 100191, People’s Republic of China; 7State Key Laboratory of Natural and Biomimetic Drugs, Department of Microbiology & Infectious Disease Center, School of Basic Medicine, Peking University Health Science Center, Beijing, 100191, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Ning Shen, Center of Infectious Disease, Department of Infectious Diseases, Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, 49 North Garden Road, Beijing, 100191, People’s Republic of China, Email shenningputh@163.com Fengmin Lu, State Key Laboratory of Natural and Biomimetic Drugs, Department of Microbiology & Infectious Disease Center, School of Basic Medicine, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, People’s Republic of China, Email lu.fengmin@hsc.pku.edu.cn

Purpose: Klebsiella pneumoniae infections pose a significant threat to public health with high morbidity and mortality rates. The early identification of risk factors for mortality and accurate prognostic evaluation are important. Therefore, we aimed to identify the risk factors for mortality in patients with K. pneumoniae infections and develop a nomogram model for prognosis.
Methods: Patients diagnosed with K. pneumoniae infection were recruited from the intensive care unit of Peking University Third Hospital. The enrolled patients were categorized into survivor and non-survivor groups. Univariate and multivariate regression analyses were performed to identify independent risk factors for 30-day mortality, and a nomogram was constructed and validated.
Results: A total of 408 patients infected with K. pneumoniae at different sites were included in this study. PO2, lactate, respiratory failure, urinary tract infection, heart rate, 24h-urineoutput, neutrophil count, alkaline phosphatase, and vasoactive drug use were significant risk factors and were integrated into a nomogram to predict the risk of 7-day, 14-day, 21-day, and 28-day mortality. The nomogram demonstrated superior prognostic ability, achieving higher area under the receiver operating characteristic curve (AUC) (> 0.8) and concordance index (C-index) (> 0.8) values than the Pitt bacteremia, sequential organ failure assessment (SOFA), and acute physiology and chronic health evaluation (APACHE) II scores (all AUC and C-index < 0.75). Cross-validation of the nomogram confirmed its consistent performance, with both AUC and C-index values exceeding 0.75. The nomogram demonstrated a strong Hosmer-Leme-show goodness-of-fit and good calibration (p > 0.05). Additionally, decision curve analysis revealed that the nomogram provided significant clinical utility for prognostic prediction.
Conclusion: The 30-day mortality risk factors for K. pneumoniae infections were identified, and a predictive nomogram model was developed. The nomogram demonstrated good accuracy and predictive efficiency, providing a practical tool for short-term risk assessment and potentially improving clinical outcomes by providing early intervention and personalized patient management.

Keywords: Klebsiella pneumoniae, infection, prognosis, mortality, risk factor