RT Journal Article
SR Electronic
T1 Prediction models of diabetes complications: a scoping review
JF Journal of Epidemiology and Community Health
JO J Epidemiol Community Health
FD BMJ Publishing Group Ltd
SP 896
OP 904
DO 10.1136/jech-2021-217793
VO 76
IS 10
A1 Ruth Ndjaboue
A1 Gérard Ngueta
A1 Charlotte Rochefort-Brihay
A1 Sasha Delorme
A1 Daniel Guay
A1 Noah Ivers
A1 Baiju R Shah
A1 Sharon E Straus
A1 Catherine Yu
A1 Sandrine Comeau
A1 Imen Farhat
A1 Charles Racine
A1 Olivia Drescher
A1 Holly O Witteman
YR 2022
UL http://jech.bmj.com/content/76/10/896.abstract
AB 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/