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

被引:0
|
作者
Wei, Jian [1 ,2 ]
Liang, Ruiyuan [3 ,4 ]
Liu, Siying [1 ,2 ]
Dong, Wanguo [1 ,2 ]
Gao, Jian [1 ,2 ]
Hua, Tianfeng [1 ,2 ]
Xiao, Wenyan [1 ,2 ]
Li, Hui [1 ,2 ]
Zhu, Huaqing [5 ]
Hu, Juanjuan [1 ,2 ]
Cao, Shuang [1 ,2 ]
Liu, Yu [3 ,4 ]
Lyu, Jun [6 ]
Yang, Min [1 ,2 ]
机构
[1] Anhui Med Univ, Affiliated Hosp 2, Dept Crit Care Med 2, 678 Furong Rd, Hefei 230601, Anhui, Peoples R China
[2] Anhui Med Univ, Affiliated Hosp 2, Lab Cardiopulm Resuscitat & Crit Care, 678 Furong Rd, Hefei 230601, Anhui, Peoples R China
[3] Anhui Univ, Key Lab Intelligent Comp & Signal Proc, Minist Educ, 111 Jiulong Rd, Hefei 230601, Anhui, Peoples R China
[4] Anhui Univ, Sch Integrated Circuits, 111 Jiulong Rd, Hefei 230601, Anhui, Peoples R China
[5] Anhui Med Univ, Biol & Dept Biochem, Lab Mol, 81 Meishan Rd, Hefei 230022, Anhui, Peoples R China
[6] Jinan Univ, Affiliated Hosp 1, Dept Clin Res, 613 West Huangpu Ave, Guangzhou 510630, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Urinary tract infection; Urosepsis; Nomogram; MIMIC-IV; INTERNATIONAL CONSENSUS DEFINITIONS; SEVERE SEPSIS; APACHE-II; EPIDEMIOLOGY; INFECTION; DEATH;
D O I
10.1186/s12879-024-09319-8
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Background 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.Methods 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.Results 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.Conclusions 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.
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页数:13
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