Risk Management and Healthcare Policy (Feb 2022)
Development and Validation of a Prediction Model for 5-Year Risk of Kidney Dysfunction in Patients with Hypertension and Glucose Metabolism Disorder
Abstract
Mengyue Lin,1,2 Mulalibieke Heizhati,1 Lin Gan,1,2 Ling Yao,1 Wenbo Yang,1 Mei Li,1 Jing Hong,1 Zihao Wu,1 Hui Wang,1,2 Nanfang Li1 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region “Hypertension Research Laboratory”; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People’s Republic of China; 2Xinjiang Medical University, Urumqi, Xinjiang, People’s Republic of ChinaCorrespondence: Nanfang Li, Email [email protected]: Patients with hypertension and glucose metabolism disorder (GMD) are at high risk of developing kidney dysfunction (KD). Therefore, we aimed to develop a nomogram for predicting individuals’ 5-year risk of KD in hypertensives with GMD.Patients and Methods: In total, 1961 hypertensives with GMD were consecutively included. Baseline data were extracted from medical electronic system, and follow-up data were obtained using annual health check-ups or hospital readmission. KD was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2. Subjects were randomly divided into training and validation sets with a ratio of 7 to 3. Least absolute shrinkage and selection operator method was used to identify potential predictors. Cox proportional hazard model was applied to build a nomogram for predicting KD risk. The discriminative ability, calibration and usefulness of the model were evaluated. The prediction model was verified by internal validation.Results: During the follow-up of 5351 person-years with a median follow-up of 32 (range: 3– 91) months, 130 patients developed KD. Age, sex, ethnicity, hemoglobin A1c, uric acid, and baseline eGFR were identified as significant predictors for incident KD and used for establishing nomogram. The prediction model displayed good discrimination with C-index of 0.770 (95% CI: 0.712– 0.828) and 0.763 (95% CI: 0.704– 0.823) in training and validation sets, respectively. Calibration curve indicated good agreement between the predicted and actual probabilities. The decision curve analysis demonstrated that the model was clinically useful.Conclusion: The prediction nomogram, including six common easy-to-obtain factors, shows good performance for predicting 5-year risk of KD in hypertensives with GMD. This quantitative tool could help clinicians, and even primary care providers, recognize potential KD patients early and make strategy for prevention and management.Keywords: kidney dysfunction, nomogram, hypertension, diabetes, prediction
