A Review of Methodological Approaches for Developing Diagnostic Algorithms for Diabetes Screening

Abstract

Background and PurposeDiagnostic algorithms are invaluable tools for screening diabetes. This review aimed to evaluate and identify the most robust methodological approaches for developing diagnostic algorithms for screening diabetes.MethodsFollowing a literature search, methodological quality of algorithm development studies was evaluated using the TRIPOD guidelines (Collins, Reitsma, Altman, & Moons, 2015).ResultsMethods used for developing the algorithms included logistic regression models, classification and regression trees, Random Forest and TreeNet, Artificial Neural Networks, and Naïve Bayes. Methodological issues for algorithm development studies were related to handling of missing values, reporting recruitment methods, categorization of continuous variables, and statistical controls.ConclusionsMost studies exhibited critical methodological flaws and poor adherence to reporting standards. Diabetes screening algorithms can easily be availed electronically and utilized by nurses at minimal cost even in underserved areas.