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Prediction models of diabetes complications: a scoping review
  1. Ruth Ndjaboue1,2,3,
  2. Gérard Ngueta4,
  3. Charlotte Rochefort-Brihay5,
  4. Sasha Delorme6,
  5. Daniel Guay6,
  6. Noah Ivers7,8,
  7. Baiju R Shah9,
  8. Sharon E Straus10,
  9. Catherine Yu11,
  10. Sandrine Comeau5,
  11. Imen Farhat5,
  12. Charles Racine5,
  13. Olivia Drescher5,
  14. Holly O Witteman12
  1. 1 Faculty of Medicine, Université Laval, Quebec, Quebec, Canada
  2. 2 School of social work, Université de Sherbrooke, Sherbrooke, Quebec, Canada
  3. 3 CIUSSS de l'Estrie, Research Centre on Aging, Sherbrooke, Quebec, Canada
  4. 4 Université de Sherbrooke Faculté des Sciences, Sherbrooke, Quebec, Canada
  5. 5 Université Laval Faculté de médecine, Quebec, Quebec, Canada
  6. 6 Diabetes Action Canada, Toronto, Ontario, Canada
  7. 7 Women’s College Research Institute, Women's College Hospital, Toronto, Ontario, Canada
  8. 8 Department of Family Medicine and Community Medicine, University of Toronto, Toronto, Ontario, Canada
  9. 9 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
  10. 10 Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
  11. 11 Knowledge Translation, St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, Ontario, Canada
  12. 12 Family and Emergency Medicine, Laval University, Quebec City, Quebec, Canada
  1. Correspondence to Dr Ruth Ndjaboue, Faculty of Medicine, Université Laval, Quebec, QC G1V 0A6, Canada; ruth.ndjaboue{at}


Background Diabetes often places a large burden on people with diabetes (hereafter ‘patients’) and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

Methods Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

Results Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

Conclusion This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics.

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  • Contributors HW originally conceptualised the study, which was then led by RN as principal investigator. RN, IF closely contributed to the design of the study. HOW, DG and SES brought expertise in the definition of the search strategy for predictors. HOW, BRS, CY and SS brought expertise in the definition of the search strategy for diabetes complications. RN, IF and GN collaborated to draft the grid for extraction data and do pilot screening. CRB, SC, CR and RN participated in data screening, selection and extraction. RN, GN, IN, SS, CY and HOW brought methodological expertise in study selection and extraction. RN, CR-B and GN verified the underlying data and drafted the first version of the article with early revision by HOW. RN, IF, GN and BS brought expertise in prediction models. HOW, DG, SD and CY prepared the dissemination plan. All the co-authors critically revised the article and approved the final version for submission for publication. RN and HW had full access to all the data and had final responsibility for the decision to submit for publication.

  • Funding Funding for this study comes from two grants from the Canadian Institutes of Health Research (CIHR) FDN-148426 (Foundation grant, PI: Witteman) and SCA-145101 (SPOR chronic disease network grant funding Diabetes Action Canada, PI: Lewis). The CIHR had no role in determining the study design, the plans for data collection or analysis, the decision to publish, nor the preparation of this manuscript. HW was funded by a Research Scholar Junior 2 Career Development Award by the Fonds de Recherche du Québec—Santé (Number: Not applicable) and is funded by a Canada Research Chair (Tier 2) in Human-Centred Digital Health. RN is funded by Diabetes Action Canada (Number: Not applicable), the Society for Medical Decision Making (Number: Not applicable), the Gordon and Betty Moore Foundation (Number: Not applicable), and a Canada Research Chair (Tier 2) in Inclusivity and Active Ageing.

  • Competing interests All authors have completed the Unified Competing Interest form. RN is funded by Diabetes Action Canada, a strategic patient-oriented research (SPOR) network in diabetes and its related complications, part of the Canadian Institutes of Health Research (CIHR) SPOR Program in Chronic Disease. Patient partners (DG, SD) were recruited through Diabetes Action Canada and some co-authors (BS, IN, CY, HOW) also collaborated with Diabetes Action Canada. Other co-authors have no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years and no relationships or activities that could appear to have influenced the submitted work.

  • 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.