Establishment and Validation of A Prediction Model for the Occurrence of Acute Kidney Disease in Patients with Liver Cirrhosis
10.12290/xhyxzz.2023-0394
- VernacularTitle:肝硬化患者发生急性肾脏病预测模型的建立与验证
- Author:
Jing OUYANG
1
,
2
,
3
;
Hong CHANG
1
,
2
,
3
;
Mengjiao YANG
1
,
2
,
3
;
Meng ZHANG
1
,
2
,
3
;
Meng TIAN
1
,
2
,
3
;
Ya ZHENG
2
,
3
;
Yuping WANG
2
,
3
;
Zhaofeng CHEN
2
,
3
Author Information
1. The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, China
2. Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou 730000, China
3. Gansu Clinical Research Center for Digestive Diseases, Lanzhou 730000, China
- Publication Type:Journal Article
- Keywords:
liver cirrhosis;
acute kidney disease;
nomogram;
predictive model
- From:
Medical Journal of Peking Union Medical College Hospital
2024;15(1):89-98
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a model that can predict the occurrence of acute kidney disease (AKD) in liver cirrhotic patients and evaluate its performance. Methods Liver cirrhotic patients who hospitalized in the department of gastroenterology of the First Hospital of Lanzhou University from January 2017 to January 2022 were retrospectively included. They were divided into AKD and non-AKD groups according to whether they were combined with AKD during hospitalization, and were randomized into training and validation sets in a 7∶3 ratio. The clinical data of patients in the two groups were collected, and LASSO regression and multifactorial Logistic regression were used to screen the influencing factors for the occurrence of AKD in patients with liver cirrhosis and to establish a prediction model. The model was then evaluated by using the receiver operating characteristic curve, the calibration curve and the clinical decision curve. Results A total of 796 cases of liver cirrhotic patients who met the inclusion and exclusion criteria were enrolled. Among them, 103 cases were in the AKD group and 693 cases were in the non-AKD group; 561 cases were in the training set and 235 cases were in the validation set. The results of LASSO regression and multifactorial Logistic regression showed that a history of diabetes (OR=2.922, 95% CI: 1.290-6.564, P=0.009), hepatic encephalopathy (OR=6.210, 95% CI: 2.278-17.479, P < 0.001), gastrointestinal bleeding (OR=2.501, 95% CI: 1.236-5.073, P=0.011), ascites (OR=3.219, 95% CI: 1.664-6.539, P < 0.001), male (OR=0.477, 95% CI: 0.254-0.879, P=0.019), hemoglobin (OR=0.987, 95% CI: 0.975-0.999, P=0.044), albumin (OR=0.952, 95% CI: 0.911-0.991, P=0.023), and prothrombin time (OR=0.865, 95% CI: 0.779-0.920, P < 0.001) were the independent influences on the occurrence of AKD in liver cirrhotic patients, and were used to construct a prediction model. The area under the curve of the model in the training set and validation set for predicting the occurrence of AKD in liver cirrhotic patients was 0.895 (95% CI: 0.865-0.925) and 0.869 (95% CI: 0.807-0.930), respectively. The calibration curves showed that the model had good fit and consistency and the clinical decision curves showed that the use of the model for predicting the risk of AKD could benefit liver cirrhotic patients overall. Conclusions A prediction model for the occurrence of AKD in liver cirrhotic patients was established based on eight influencing factors, including gender, history of diabetes, and hepatic encephalopathy. It was validated to have good discrimination, calibration, and clinical utility, and is expected to assist in the clinical early screening and identification of liver cirrhosis-associated AKD.
