Development and Validation of Prediction Models for Hypertensive Nephropathy, the PANDORA Study

Xiaoli Yang, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Bingqing Zhou, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Li Zhou, Department of Epidemiology, School of Public Health and Management, Chongqing Medical University, Chongqing, China.
Liufu Cui, Department of Cardiology, Kailuan General Hospital, Tangshan, China.
Jing Zeng, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Shuo Wang, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Weibin Shi, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Ye Zhang, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Xiaoli Luo, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Chunmei Xu, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Yuanzheng Xue, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Hao Chen, Department of Epidemiology, School of Public Health and Management, Chongqing Medical University, Chongqing, China.
Shuohua Chen, Department of Cardiology, Kailuan General Hospital, Tangshan, China.
Guodong Wang, Department of Cardiology, Kailuan General Hospital, Tangshan, China.
Li Guo, Department of Endocrinology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Pedro A. Jose, Division of Renal Disease & Hypertension, The George Washington University School of Medicine & Health Sciences, Washington, DC, United States.
Christopher S. Wilcox, Division of Nephrology and Hypertension, Department of Medicine and Center for Hypertension, Kidney and Vascular Health, Georgetown University, Washington, DC, United States.
Shouling Wu, Department of Cardiology, Kailuan General Hospital, Tangshan, China.
Gengze Wu, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Chunyu Zeng, Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.

Document Type

Journal Article

Publication Date

1-1-2022

Journal

Frontiers in cardiovascular medicine

Volume

9

DOI

10.3389/fcvm.2022.794768

Keywords

chronic kidney disease; hypertension; hypertensive nephropathy; pulse pressure; risk model

Abstract

Importance: Hypertension is a leading cause of end-stage renal disease (ESRD), but currently, those at risk are poorly identified. Objective: To develop and validate a prediction model for the development of hypertensive nephropathy (HN). Design Setting and Participants: Individual data of cohorts of hypertensive patients from Kailuan, China served to derive and validate a multivariable prediction model of HN from 12, 656 individuals enrolled from January 2006 to August 2007, with a median follow-up of 6.5 years. The developed model was subsequently tested in both derivation and external validation cohorts. Variables: Demographics, physical examination, laboratory, and comorbidity variables. Main Outcomes and Measures: Hypertensive nephropathy was defined as hypertension with an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m and/or proteinuria. Results: About 8.5% of patients in the derivation cohort developed HN after a median follow-up of 6.5 years that was similar in the validation cohort. Eight variables in the derivation cohort were found to contribute to the risk of HN: salt intake, diabetes mellitus, stroke, serum low-density lipoprotein, pulse pressure, age, hypertension duration, and serum uric acid. The discrimination by concordance statistics (C-statistics) was 0.785 (IQR, 0.770-0.800); the calibration slope was 1.129, the intercept was -0.117; and the overall accuracy by adjusted was 0.998 with similar results in the validation cohort. A simple points scale developed from these data (0, low to 40, high) detected a low morbidity of 7% in the low-risk group (0-10 points) compared with >40% in the high-risk group (>20 points). Conclusions and Relevance: A prediction model of HN over 8 years had high discrimination and calibration, but this model requires prospective evaluation in other cohorts, to confirm its potential to improve patient care.

Department

Medicine

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