OBJECTIVE: To compare risk factor-based screening tools for identifying prediabetes. METHODS: Participants in an employer-based wellness program were tested for glycosylated hemoglobin (A1C) at a regularly scheduled appointment, and prediabetes risk factor information was collected. The likelihood of having prediabetes and the need for laboratory testing were determined based on 3 risk factor-based screening tools: the Prediabetes Screening Test (PST), Prediabetes Risk Test (PRT), and 2016 American Diabetes Association guidelines (ADA2016). The results from the screening tools were compared with those of the A1C test. The predictive ability of the PST, PRT, and ADA2016 were compared using logistic regression. Results were validated with data from a secondary population. RESULTS: Of the 3 risk factor-based tools examined, the PRT demonstrated the best combination of sensitivity and specificity for identifying prediabetes. From July 2016 to March 2017, 740 beneficiaries of an employer-sponsored wellness program had their A1C tested and provided risk factor information. The population prevalence of prediabetes was 9.3%. Analysis of a second independent population with a prediabetes prevalence of more than 50% of confirmed PRT's superiority despite differences in the calculated sensitivity and specificity for each population. CONCLUSION: Because PRT predicts prediabetes better than PST or ADA2016, it should be used preferentially.
OBJECTIVE: To compare risk factor-based screening tools for identifying prediabetes. METHODS:Participants in an employer-based wellness program were tested for glycosylated hemoglobin (A1C) at a regularly scheduled appointment, and prediabetes risk factor information was collected. The likelihood of having prediabetes and the need for laboratory testing were determined based on 3 risk factor-based screening tools: the Prediabetes Screening Test (PST), Prediabetes Risk Test (PRT), and 2016 American Diabetes Association guidelines (ADA2016). The results from the screening tools were compared with those of the A1C test. The predictive ability of the PST, PRT, and ADA2016 were compared using logistic regression. Results were validated with data from a secondary population. RESULTS: Of the 3 risk factor-based tools examined, the PRT demonstrated the best combination of sensitivity and specificity for identifying prediabetes. From July 2016 to March 2017, 740 beneficiaries of an employer-sponsored wellness program had their A1C tested and provided risk factor information. The population prevalence of prediabetes was 9.3%. Analysis of a second independent population with a prediabetes prevalence of more than 50% of confirmed PRT's superiority despite differences in the calculated sensitivity and specificity for each population. CONCLUSION: Because PRT predicts prediabetes better than PST or ADA2016, it should be used preferentially.
Authors: Courtney E Gamston; Anna N Kirby; Richard A Hansen; David T Redden; Heather P Whitley; Courtney Hanson; Kimberly B Lloyd Journal: J Am Pharm Assoc (2003) Date: 2019-07-13
Authors: Ramona S DeJesus; Carmen Radecki Breitkopf; Lila J Rutten; Debra J Jacobson; Patrick M Wilson; Jennifer St Sauver Journal: Popul Health Manag Date: 2016-09-30 Impact factor: 2.459
Authors: Jared P Reis; Norrina B Allen; Michael P Bancks; J Jeffrey Carr; Cora E Lewis; Joao A Lima; Jamal S Rana; Samuel S Gidding; Pamela J Schreiner Journal: Diabetes Care Date: 2018-01-09 Impact factor: 17.152