FINDRISC in Latin America: A systematic review of diagnosis and prognosis models

Rodrigo M. Carrillo-Larco, Diego J. Aparcana-Granda, Jhonatan R. Mejia, Antonio Bernabé-Ortiz

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations


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.

Original languageEnglish
Article numbere001169
JournalBMJ Open Diabetes Research and Care
Issue number1
StatePublished - 22 Apr 2020


  • diagnostic models
  • low- and middle-income countries
  • prognostic models
  • type 2 diabetes mellitus


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