Prediction models of diabetes complications

A scoping review

Bibliographic Data

ID14622902
AuthorsRuth Ndjaboue (0000-0002-4716-6505, Université Laval, corresponding author), Gerard Ngueta (0000-0002-6794-9873, Université de Sherbrooke Faculté des Sciences, Sherbrooke, Quebec, Canada), Charlotte Rochefort-Brihay (Université Laval Faculté de médecine, Quebec, Quebec, Canada), Sasha Delorme (Diabetes Action Canada, Toronto, Ontario, Canada), Daniel Guay (0000-0001-5057-959X, Diabetes Action Canada, Toronto, Ontario, Canada), Noah Ivers (0000-0003-2500-2435, Women’s College Research Institute, Women's College Hospital, Toronto, Ontario, Canada), Baiju R Shah (0000-0003-3598-3628, Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada), Sharon E Straus (0000-0002-6106-832X, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada), Catherine Yu (0009-0007-8575-4889, Knowledge Translation, St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, Ontario, Canada), Sandrine Comeau (Université Laval Faculté de médecine, Quebec, Quebec, Canada), Imen Farhat (0000-0002-1856-9057, Université Laval Faculté de médecine, Quebec, Quebec, Canada), Charles Racine (Université Laval Faculté de médecine, Quebec, Quebec, Canada), Olivia Drescher (0000-0002-4281-6695, Université Laval Faculté de médecine, Quebec, Quebec, Canada), Holly O Witteman (0000-0003-4192-0682, Family and Emergency Medicine, Laval University, Quebec City, Quebec, Canada)
Year2022
Volume76
Issue10
Pages896-904
Publication date2022-10-01
Open AccessNo
TypeARTICLE
VenueJournal of Epidemiology and Community Health (JOURNAL)
Journal identifiersISSN: 0143-005X • E-ISSN: 1470-2738
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/jech-2021-217793
PMID35772935
OpenAlexW4283731435
LanguageEN
References cited122

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. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt

Amputation · Complication · Dementia · Diabetes mellitus · Disease · Intensive care medicine · MEDLINE · Type 2 diabetes · Artificial Intelligence in Healthcare · Diabetes Management and Research · Endocrinology · Internal Medicine · Machine Learning in Healthcare · Medicine · Surgery

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