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Cardiac risk factors and prevention
Original research
Predictive models for cardiovascular and kidney outcomes in patients with type 2 diabetes: systematic review and meta-analyses
  1. Tayler A Buchan1,2,
  2. Abdullah Malik2,3,
  3. Cynthia Chan4,
  4. Jason Chambers5,
  5. Yujin Suk6,
  6. Jie Wei Zhu6,
  7. Fang Zhou Ge6,
  8. Le Ming Huang6,
  9. Lina Abril Vargas4,
  10. Qiukui Hao1,7,
  11. Sheyu Li7,8,
  12. Reem A Mustafa9,
  13. Per Olav Vandvik10,11,
  14. Gordon Guyatt1,
  15. Farid Foroutan1,2
  1. 1 Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
  2. 2 Ted Rogers Center for Heart Research, Toronto General Hospital-University Health Network, Toronto, Ontario, Canada
  3. 3 Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
  4. 4 Faculty of Science, McMaster University, Hamilton, Ontario, Canada
  5. 5 Schulich School of Medicine, Western University, London, Ontario, Canada
  6. 6 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
  7. 7 Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
  8. 8 Chinese Evidence-based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
  9. 9 Internal Medicine, Division of Nephrology and Hypertension, University of Kansas School of Medicine, Kansas City, Kansas, USA
  10. 10 University of Oslo, Oslo, Norway
  11. 11 MAGIC Evidence Ecosystem Foundation, Oslo, Norway
  1. Correspondence to Farid Foroutan, Ted Rogers Center for Heart Research, Toronto General Hospital-University Health Network, Toronto, ON M5G 2C4, Canada; farid.foroutan{at}uhn.ca

Abstract

Objective To inform a clinical practice guideline (BMJ Rapid Recommendations) considering sodium glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists for treatment of adults with type 2 diabetes, we summarised the available evidence regarding the performance of validated risk models on cardiovascular and kidney outcomes in these patients.

Methods We systematically searched bibliographic databases in January 2020 to identify observational studies evaluating risk models for all-cause and cardiovascular mortality, heart failure (HF) hospitalisations, end-stage kidney disease (ESKD), myocardial infarction (MI) and ischaemic stroke in ambulatory adults with type 2 diabetes. Using a random effects model, we pooled discrimination measures for each model and outcome, separately, and descriptively summarised calibration plots, when available. We used the Prediction Model Risk of Bias Assessment Tool to assess risk of bias of each included study and the Grading of Recommendations, Assessment, Development, and Evaluation approach to evaluate our certainty in the evidence.

Results Of 22 589 publications identified, 15 observational studies reporting on seven risk models proved eligible. Among the seven models with >1 validation cohort, the Risk Equations for Complications of Type 2 Diabetes (RECODe) had the best calibration in primary studies and the highest pooled discrimination measures for the following outcomes: all-cause mortality (C-statistics 0.75, 95% CI 0.70 to 0.80; high certainty), cardiovascular mortality (0.79, 95% CI 0.75 to 0.84; low certainty), ESKD (0.73, 95% CI 0.52 to 0.94; low certainty), MI (0.72, 95% CI 0.69 to 0.74; moderate certainty) and stroke (0.71, 95% CI 0.68 to 0.74; moderate certainty). This model does not, however, predict risk of HF hospitalisations.

Conclusion Of available risk models, RECODe proved to have satisfactory calibration in primary validation studies and acceptable discrimination superior to other models, though with high risk of bias in most primary studies.

Trial registration number CRD42020168351.

  • diabetes

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information and are widely available.

https://doi.org/10.1136/heartjnl-2021-319243

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    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplementary information and are widely available.

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    Footnotes

    • Twitter @faridftn

    • Contributors TAB, POV, GG, SL and FF conceived the study idea. TAB, AM, CC, JC, YS, JWZ, FZG, LMH, LAV and FF designed the search strategy, screened studies for eligibility, assessed study risk of bias, assessed the quality of the body of evidence, wrote the first draft of the manuscript, conducted data analysis, interpreted the data analysis and critically revised the manuscript. All authors approved the final version of this manuscript. FF is the guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests TAB, RAM, POV, GG and FF are members of the Grading of Recommendations, Assessment, Development, and Evaluation working group.

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

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.