Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis

• Published in: BMC infectious diseases 2024-04, Vol.24 (1), p.442-442, Article 442
• Main Authors: Wei, Jian, Liang, Ruiyuan, Liu, Siying, Dong, Wanguo, Gao, Jian, Hua, Tianfeng, Xiao, Wenyan, Li, Hui, Zhu, Huaqing, Hu, Juanjuan, Cao, Shuang, Liu, Yu, Lyu, Jun, Yang, Min
• Format: Article
• Language: English
• Subjects: Aged; Aged patients; Aged, 80 and over; Blood; Blood cell count; Calibration; China; Chronic illnesses; Conversion tables; Diabetes; Disease; Female; Health aspects; Health risks; Hospital Mortality; Hospital patients; Hospitals; Humans; Immunosenescence; Inflammation; Intensive care; Intensive care units; Intensive Care Units - statistics & numerical data; Machine learning; Male; Medical advice systems; Medical research; Medicine, Experimental; MIMIC-IV; Mortality; Mortality risk; Nomogram; Nomograms; Nomography (Mathematics); Older people; Patient outcomes; Patients; Physiology; Prediction models; Prognosis; Reclassification; Regression analysis; Retrospective Studies; Risk Factors; ROC Curve; Sensitivity analysis; Sepsis; Sepsis - mortality; Statistics; Survival; Training; Urinary tract; Urinary tract infection; Urinary tract infections; Urinary Tract Infections - mortality; Urine; Urosepsis; Ventilation
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-024-09319-8

Urinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations. Data of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis. This study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram. The nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.