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

Xiaoli Yang; Bingqing Zhou; Li Zhou; Liufu Cui; Jing Zeng; Shuo Wang; Weibin Shi; Ye Zhang; Xiaoli Luo; Chunmei Xu; Yuanzheng Xue; Hao Chen; Shuohua Chen; Guodong Wang; Li Guo; Pedro A Jose; Christopher S Wilcox; Shouling Wu; Gengze Wu; Chunyu Zeng

doi:10.3389/fcvm.2022.794768

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

Front Cardiovasc Med. 2022 Mar 10:9:794768. doi: 10.3389/fcvm.2022.794768. eCollection 2022.

Authors

Xiaoli Yang^{1

2}, Bingqing Zhou^{1

2}, Li Zhou³, Liufu Cui⁴, Jing Zeng^{1

2}, Shuo Wang^{1

2}, Weibin Shi^{1

2}, Ye Zhang^{1

2}, Xiaoli Luo^{1

2}, Chunmei Xu^{1

2}, Yuanzheng Xue^{1

2}, Hao Chen³, Shuohua Chen⁴, Guodong Wang⁴, Li Guo⁵, Pedro A Jose⁶, Christopher S Wilcox⁷, Shouling Wu⁴, Gengze Wu^{1

2}, Chunyu Zeng^{1

2

8

9}

Affiliations

¹ Department of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
² Chongqing Key Laboratory for Hypertension Research, Chongqing Cardiovascular Clinical Research Center, Chongqing Institute of Cardiology, Chongqing, China.
³ Department of Epidemiology, School of Public Health and Management, Chongqing Medical University, Chongqing, China.
⁴ Department of Cardiology, Kailuan General Hospital, Tangshan, China.
⁵ Department of Endocrinology, Southwest Hospital, Third Military Medical University, Chongqing, China.
⁶ Division of Renal Disease & Hypertension, The George Washington University School of Medicine & Health Sciences, Washington, DC, United States.
⁷ Division of Nephrology and Hypertension, Department of Medicine and Center for Hypertension, Kidney and Vascular Health, Georgetown University, Washington, DC, United States.
⁸ State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, The Third Military Medical University, Chongqing, China.
⁹ Cardiovascular Research Center of Chongqing College, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Chongqing, China.

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 m² 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 R ² 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.

Keywords: chronic kidney disease; hypertension; hypertensive nephropathy; pulse pressure; risk model.