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. 2023 Jan 1;18(1):28-35.
doi: 10.2215/CJN.0000000000000035.

A Health Survey-Based Prediction Equation for Incident CKD

Ariana J Noel  1 , Anan Badder Eddeen  2 , Douglas G Manuel  1   2   3   4   5   6   7   8 , Emily Rhodes  3 , Navdeep Tangri  7 , Gregory L Hundemer  1   3   6   8 , Peter Tanuseputro  2   3   4   6 , Gregory A Knoll  1   2   3   6   8 , Ranjeeta Mallick  3 , Manish M Sood  1   3   6   8
Affiliations

A Health Survey-Based Prediction Equation for Incident CKD

Ariana J Noel et al. Clin J Am Soc Nephrol. .

Abstract

Background: Prediction tools that incorporate self-reported health information could increase CKD awareness, identify modifiable lifestyle risk factors, and prevent disease. We developed and validated a survey-based prediction equation to identify individuals at risk for incident CKD (eGFR <60 ml/min per 1.73 m2), with and without a baseline eGFR.

Methods: A cohort of adults with an eGFR ≥70 ml/min per 1.73 m2 from Ontario, Canada, who completed a comprehensive general population health survey between 2000 and 2015 were included (n=22,200). Prediction equations included demographics (age, sex), comorbidities, lifestyle factors, diet, and mood. Models with and without baseline eGFR were derived and externally validated in the UK Biobank (n=15,522). New-onset CKD (eGFR <60 ml/min per 1.73 m2) with ≤8 years of follow-up was the primary outcome.

Results: Among Ontario individuals (mean age, 55 years; 58% women; baseline eGFR, 95 (SD 15) ml/min per 1.73 m2), new-onset CKD occurred in 1981 (9%) during a median follow-up time of 4.2 years. The final models included lifestyle factors (smoking, alcohol, physical activity) and comorbid illnesses (diabetes, hypertension, cancer). The model was discriminating in individuals with and without a baseline eGFR measure (5-year c-statistic with baseline eGFR: 83.5, 95% confidence interval [CI], 82.2 to 84.9; without: 81.0, 95% CI, 79.8 to 82.4) and well calibrated. In external validation, the 5-year c-statistic was 78.1 (95% CI, 74.2 to 82.0) and 66.0 (95% CI, 61.6 to 70.4), with and without baseline eGFR, respectively, and maintained calibration.

Conclusions: Self-reported lifestyle and health behavior information from health surveys may aid in predicting incident CKD.

Podcast: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast.aspx?p=CJASN&e=2023_01_10_CJN05650522.mp3.

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Conflict of interest statement

D.G. Manuel reports serving on advisory boards and committees related to coronavirus disease 2019 (no for-profit boards) and serving as a public health consultant for the World Bank and the European Commission. G.A. Knoll reports serving on the editorial board of Canadian Journal of Kidney Health and Disease. M.M. Sood reports having consultancy agreements with, and receiving honoraria and a speaker fee from, AstraZeneca; serving on the editorial boards of American Journal of Kidney Disease, Canadian Journal of Cardiology, and CJASN; serving as an editor for Canadian Journal of Kidney Disease and Health; and serving as a member of the ASN Highlights ESRD Team. N. Tangri reports having consultancy agreements with Marizyme Tricida Inc., Mesentech Inc., PulseData Inc., and Renibus; having an ownership interest in Clinpredict Ltd., Klinrisk, Marizyme, Mesentech Inc., PulseData Inc., Quanta Tricida Inc., and Renibus; receiving research funding from AstraZeneca Inc., Bayer, BI-Lilly, Janssen, Otsuka, and Tricida Inc.; receiving honoraria from AstraZeneca Inc., Bayer, BI-Lilly, Janssen, Otsuka Pharmaceuticals, and Pfizer; having patents or royalties from Klinrisk and Marizyme; having advisory or leadership roles for Clinpredict, Klinrisk, and Tricida Inc.; having other interests or relationships with National Kidney Foundation; and being the founder of Clinpredict and Klinrisk. All remaining authors have nothing to disclose.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Calibration plot of final prediction model with and without baseline eGFR in development cohort at 5 years.
See this image and copyright information in PMC

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