You are here

  • Home
  • Archive
  • Volume 7, Issue 1
  • Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study

Article Text

Article menu

Download PDFPDF

XML
Clinical care education/Nutrition
Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study
  1. Dahai Yu1,2,
  2. Jin Shang1,
  3. Yamei Cai1,
  4. Zheng Wang1,
  5. Xiaoxue Zhang1,
  6. Bin Zhao3,
  7. Zhanzheng Zhao1,
  8. David Simmons1,4
  1. 1Department of Nephrology, the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
  2. 2Primary Care Centre Versus Arthritis, Research Institute for Primary Care & Health Sciences, Keele University, Keele, UK
  3. 3The Second Division of Internal Medicine, Kejing Community Health Centre, Jiyuan, China
  4. 4Western Sydney University, Campbelltown, Sydney, New South Wales, Australia
  1. Correspondence to Professor Zhanzheng Zhao; zhanzhengzhao{at}zzu.edu.cn

Abstract

Objective To derive, and externally validate, a risk score for cardiovascular death among patients with type 2 diabetes and newly diagnosed diabetic nephropathy (DN).

Research design and methods Two independent prospective cohorts with type 2 diabetes were used to develop and externally validate the risk score. The derivation cohort comprised 2282 patients with an incident, clinical diagnosis of DN. The validation cohort includes 950 patients with incident, biopsy-proven diagnosis of DN. The outcome was cardiovascular death within 2 years of the diagnosis of DN. Logistic regression was applied to derive the risk score for cardiovascular death from the derivation cohort, which was externally validated in the validation cohort. The score was also estimated by applying the United Kingdom Prospective Diabetes Study (UKPDS) risk score in the external validation cohort.

Results The 2-year cardiovascular mortality was 12.05% and 11.79% in the derivation cohort and validation cohort, respectively. Traditional predictors including age, gender, body mass index, blood pressures, glucose, lipid profiles alongside novel laboratory test items covering five test panels (liver function, serum electrolytes, thyroid function, blood coagulation and blood count) were included in the final model.

C-statistics was 0.736 (95% CI 0.731 to 0.740) and 0.747 (95% CI 0.737 to 0.756) in the derivation cohort and validation cohort, respectively. The calibration slope was 0.993 (95% CI 0.974 to 1.013) and 1.000 (95% CI 0.981 to 1.020) in the derivation cohort and validation cohort, respectively.

The UKPDS risk score substantially underestimated cardiovascular mortality.

Conclusions A new risk score based on routine clinical measurements that quantified individual risk of cardiovascular death was developed and externally validated. Compared with the UKPDS risk score, which underestimated the cardiovascular disease risk, the new score is a more specific tool for patients with type 2 diabetes and DN. The score could work as a tool to identify individuals at the highest risk of cardiovascular death among those with DN.

  • cardiovascular mortality
  • prediction
  • statistical
  • prognostic models

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/bmjdrc-2019-000735

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    View Full Text

    Footnotes

    • Contributors DY, YC, JS, ZW, BZ, XZ, ZZ and DS conceptualized the survey. DY defined the analytic strategy, analyzed the data and drafted the manuscript. YC, JS and BZ supported statistical modeling. All authors critically revised the manuscript for important intellectual content and read and approved the final version of the manuscript.

    • Funding This work was supported by the National Natural Science Foundation of China (grant no. 81873611), Science and Technology Innovation Team of Henan (grant no. 17IRTSTHN020); Foundation for Leading Personnel of Central Plains of China (grant no. 194200510006); the Excellent Youth Foundation of Henan Scientific Committee (grant no. 154100510017). DY was funded by an Honorary Public Health Academic Contract from Public Health England.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Ethics approval The study was approved by the by the Clinical Research Ethics Committee of the First Affiliated Hospital of Zhengzhou University (reference number: KY-2017-53).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.