Development and validation of a prediction model for abnormal bone mass in end-stage renal disease patients
10.3760/cma.j.cn441217-20231023-01034
- VernacularTitle:终末期肾病患者骨量异常风险预测模型的建立与验证
- Author:
Jing LU
1
;
Yujia WANG
;
Yuxia ZHANG
;
Zhiqing CHEN
;
Yongqi LI
;
Min WU
;
Rining TANG
Author Information
1. 东南大学医学院，南京210009
- Keywords:
Kidney failure, chronic;
Bone and bones;
Osteoporosis;
Nomograms;
End-stage renal disease;
Abnormal bone mass;
LASSO regression;
Prediction model
- From:
Chinese Journal of Nephrology
2024;40(5):345-357
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify the risk factors, and develop and validate a risk prediction model for abnormal bone mass in end-stage renal disease (ESRD) patients.Methods:It was a retrospective cross-sectional study. The clinical and laboratory data of ESRD patients who were hospitalized in the Department of Nephrology, Zhongda Hospital Affiliated to Southeast University from January 2022 to May 2023 were collected retrospectively. The patients were randomly divided into training and validation cohorts at a ratio of 7∶3. They were further divided into normal and abnormal bone mass groups according to the T value measured by dual-energy X-ray absorptiometry (DXA). Then, backward stepwise regression and least absolute shrinkage and selection operator (LASSO) were respectively used to develop the risk prediction model for abnormal bone mass in ESRD patients. Akaike information criterion (AIC), bayesian information criterion (BIC), and accuracy were used to evaluate the performance of these two models, after which the preferable model was selected. Moreover, the receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow test, and decision curve analyses (DCA) were applied to evaluate the diagnostic performance of the preferable model. Finally, a dynamic nomogram for individual assessment was constructed based on the preferable model.Results:A total of 254 ESRD patients were enrolled, including 160 (63.0%) males, 161 (63.4%) hemodialysis patients, and 202 (79.5%) patients with abnormal bone mass. There was no significant difference in the prevalence of abnormal bone mass between training group ( n=178) and validation group ( n=76) (79.2% vs. 80.3%, χ2=0.036, P=0.849). The final variables and variable parameters included in the LASSO and stepwise regression models were the same, which were five variables: age, body mass index, hypertension, diabetes, and osteocalcin. Both models also had the same AIC, BIC, and accuracy in the training group, which were 113.45, 132.54, and 0.837, respectively. Therefore, the LASSO model and the stepwise regression model performed consistently in this study and could be considered as the same model, hereafter referred to as the Model. The Model's area under the ROC curve in the training and validation groups was 0.923 (95% CI 0.884-0.963) and 0.809 (95% CI 0.675-0.943), respectively. The optimal cutoff for the training group was 0.858, with a sensitivity of 0.801, a specificity of 0.973 and an accuracy of 0.837; when this cutoff value was taken, the validation group's sensitivity was 0.689, specificity was 0.800, and accuracy was 0.711. The Model demonstrated excellent performance in the calibration curve, Hosmer-Lemeshow test ( P>0.05), and DCA. Finally, based on the five predictors of the Model, a dynamic nomogram was created for clinicians to enter baseline clinical parameters for early identification of high-risk patients with abnormal bone mass. Conclusions:A dynamic nomogram for abnormal bone mass in ESRD patients is constructed with good predictive performance based on the prediction model, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors and assist physicians in making a personalized diagnosis for patients.
