FINDRISC in Latin America: a systematic review of diagnosis and prognosis models. Issue 1 (22nd April 2020)

• Record Type: Journal Article
• Authors: Carrillo-Larco, Rodrigo M; Aparcana-Granda, Diego J; Mejia, Jhonatan R; Bernabé-Ortiz, Antonio
• Is Part Of: BMJ open diabetes research and care. Volume 8:Issue 1(2020)
• Publication Date: 2020-04-22
• Subjects: type 2 diabetes mellitus -- prognostic models -- diagnostic models -- low- and middle-income countries -- FINDRISC; Diabetes -- Periodicals; 616.462005
• DOI: 10.1136/bmjdrc-2019-001169
• Languages: English
• ISSNs: 2052-4897

Abstract

Abstract : This review aimed to assess whether the FINDRISC, a risk score for type 2 diabetes mellitus (T2DM), has been externally validated in Latin America and the Caribbean (LAC). We conducted a systematic review following the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) framework. Reports were included if they validated or re-estimated the FINDRISC in population-based samples, health facilities or administrative data. Reports were excluded if they only studied patients or at-risk individuals. The search was conducted in Medline, Embase, Global Health, Scopus and LILACS. Risk of bias was assessed with the PROBAST (Prediction model Risk of Bias ASsessment Tool) tool. From 1582 titles and abstracts, 4 (n=7502) reports were included for qualitative summary. All reports were from South America; there were slightly more women, and the mean age ranged from 29.5 to 49.7 years. Undiagnosed T2DM prevalence ranged from 2.6% to 5.1%. None of the studies conducted an independent external validation of the FINDRISC; conversely, they used the same (or very similar) predictors to fit a new model. None of the studies reported calibration metrics. The area under the receiver operating curve was consistently above 65.0%. All studies had high risk of bias. There has not been any external validation of the FINDRISC model in LAC. Selected reports re-estimated the FINDRISC, although they have several methodological limitations. There is a need for big data to develop—or improve—T2DM diagnostic and prognostic models in LAC. This could benefit T2DM screening and early diagnosis.