Prediabetes Screening with American Diabetes Association (ADA) Scoring in the Primary Health Care Yogyakarta (Development And Validation Of Scoring Systems)

Jeem, Yaltafit Abror; Koesnanto, Hari; Ikhsan, Mohammad Robikhul

doi:10.22146/rpcpe.50503

Cited by 1 publication

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After applying the inclusion criteria and reading through the full text, 24 articles containing a total of 19 different models were included, all of which were diagnostic models. Eight of the studies [24][25][26][27][28][29][30][31] involved two or more models. Our study included 85 422 participants, of whom 33 872 were diagnosed with prediabetes.…”

Section: Resultsmentioning

confidence: 99%

“…Twenty‐four diagnostic trials were assessed for risk of bias and suitability according to the QUADAS‐2 evaluation form. Thirteen 24,26,28,31,35–40,43,45,48 (54.2%) of the 24 studies were judged to be at unclear risk of bias, six 25,29,34,44,46,47 (25%) at high risk of bias, and five 27,30,41,42,49 (20.8%) at low risk of bias.…”

Section: Resultsmentioning

confidence: 99%

“…Thirteen 24,26,28,31,[35][36][37][38][39][40]43,45,48 (54.2%) of the 24 studies were judged to be at unclear risk of bias, six 25,29,34,44,46,47 (25%) at high risk of bias, and five 27,30,41,42,49 The study population as a family practice population.…”

Section: Risk Of Bias and Assessment Of Applicabilitymentioning

confidence: 99%

See 2 more Smart Citations

A meta‐analysis of diabetes risk prediction models applied to prediabetes screening

Liu,

Yu,

Feng

et al. 2024

Diabetes Obesity Metabolism

View full text Add to dashboard Cite

AimTo provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes.MethodsThe Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS‐2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes.ResultsA total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information.ConclusionsNumerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

show abstract