An acute kidney injury risk prediction model for elderly patients with sepsis based on the intensive care medicine information database-Ⅳ
10.3760/cma.j.issn.0254-9026.2023.02.006
- VernacularTitle:基于数据库建立老年脓毒症患者急性肾损伤风险预测模型
- Author:
Jingjing ZHAO
1
;
Fujin CHEN
;
Ting CHEN
;
Jing WANG
;
Xiuhua SUI
;
Hanmin FENG
;
Li YAO
Author Information
1. 合肥市第二人民医院（安徽医科大学附属合肥医院）重症医学科，合肥 230011
- Keywords:
Elderly;
Sepsis;
Kidney injury;
Predictive model
- From:
Chinese Journal of Geriatrics
2023;42(2):169-175
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the risk factors of acute kidney injury(stage 3)developed within 48 hours in elderly patients with sepsis, and to use them to develop a risk prediction model and then evaluate and externally validate the model.Methods:Clinical data of all elderly patients(age≥ 60 years)with sepsis in the intensive care medicine information database(MIMIC-Ⅳ v1.0)were extracted.Independent risk factors were determined by multivariate logistic regression analysis.A risk prediction model was constructed, a nomogram was drawn, and the receiver operating characteristic curve(ROC)and the Hosmer-Lemeshow(H-L)test were used to evaluate the model's prediction accuracy and R-squared.Clinical data of elderly patients(age≥ 60 years)with sepsis admitted to the Department of Critical Care Medicine of the Second People's Hospital of Hefei from May 2019 to October 2021 were retrospectively collected and fed into the prediction model to conduct external validation.Results:A total of 1 977 elderly patients with sepsis were screened out from the MIMIC-IV database and included in the training set, of whom, 544 developed AKI-stage 3 within 48 hours.Univariate analysis was performed for factors that might be associated with acute kidney injury in elderly patients with sepsis.Compared with the normal group that did not progress to AKI stage 3, there were statistically significant differences in 28 indicators, such as the duration of ICU stay, intravenous fluid intake in 24 hours, and use of vasoactive drugs[5(3, 9)d vs.7(4, 12)d; 2.05(1.17, 3.27)ml·kg -1·h -1vs.2.37(1.47, 4.10)ml·kg -1·h -1; 761(53.11%) vs.375(68.93%), P<0.001]. Based on the results of multivariate logistic regression analysis, a prediction model was finally constructed with 9 variables: albumin( OR=0.983, 95% CI: 0.966-0.999, P=0.040), aspartate transaminase( OR=1.000, 95% CI: 1.000-1.000, P<0.001), APTT( OR=1.005, 95% CI: 1.001-1.009, P=0.028), total bilirubin( OR=1.003, 95% CI: 1.001-1.004, P=0.001), serum creatinine( OR=1.005, 95% CI: 1.004~1.007, P<0.001), Charlson score( OR=1.117, 95% CI: 1.061-1.177, P<0.001), intravenous fluid intake in 24 hours( OR=1.101, 95% CI: 1.034-1.173, P=0.003), weight( OR=1.023, 95% CI: 1.018-1.029, P<0.001), and mechanical ventilation( OR=2.412, 95% CI: 1.843-3.157, P<0.001). Then a nomogram was generated.The area under the ROC curve(AUC)of the prediction model was 0.755(95% CI: 0.731-0.780), and the H-L test was conducted( χ2=10.89, P=0.208>0.05), indicating a good fit.Data from 102 elderly patients were included in the validation set, with 27 cases that had developed AKI-stage3 within 48 hours, and were fed into the prediction model, with an AUC of 0.778(95% CI: 0.676-0.880)and χ2=3.72 and P=0.882>0.05 from the H-L test, consistent with the results of the training set. Conclusions:The model has some predictive value for acute kidney injury in elderly patients with sepsis.
