Non-laboratory-based risk prediction tools for pre-diabetes showed satisfactory internal discrimination (AUROC 0.68-0.82), but generalizability was unclear due to a lack of external validation.
Systematic Review (n=24)
Do non-laboratory-based risk prediction tools accurately detect undiagnosed pre-diabetes?
Non-laboratory-based risk tools for pre-diabetes show satisfactory internal performance but lack consistent external validation, limiting their generalizability.
Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies' quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68-0.82), sensitivity (0.60-0.89), and specificity (0.50-0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31-0.79) and sensitivity (0.31-0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.
Cheng et al. (Wed,) conducted a systematic review in Undiagnosed Pre-Diabetes (n=24). Non-laboratory-based risk prediction tools was evaluated on Tool performance (AUROC, sensitivity, specificity). Non-laboratory-based risk prediction tools for pre-diabetes showed satisfactory internal discrimination (AUROC 0.68-0.82), but generalizability was unclear due to a lack of external validation.
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