Updating a clinical prediction model for identifying monogenic diabetes to include both clinical features and biomarkers

Résumé fourni par la source

Objective Selecting appropriate individuals for monogenic diabetes genetic testing is challenging. We aimed to develop a new probability calculator, integrating clinical features and biomarkers, to aid identification of monogenic diabetes. Research Design and Methods We developed two prediction models (for early-insulin-treated, proxy for type 1 diabetes; and not-early-insulin-treated patients, proxy for type 2 diabetes) using a Bayesian recalibration mixture model approach. We used case-control data (monogenic diabetes=594, non-monogenic diabetes diabetes=597) for initial model development (clinical features only) and recalibrated to population-data (UNITED study, n=1,299) including biomarkers (C-peptide and islet-autoantibodies). We externally validated the calculator in an independent population-based cohort (n=1,025). Results For early-insulin-treated individuals, the model incorporating biomarkers improved discrimination over using clinical features only (ROCAUC 0.98 [95%CrI 0.95–0.98] vs. 0.80 [95%CrI 0.71–0.82], p<0.001) or biomarkers alone (ROC AUC 0.96 [95% CI 0.95–0.97]). For not-early-insulin-treated participants, the calculator showed good discrimination (ROCAUC: 0.86 [95%CrI 0.85–0.88]). Both models calibrated well and showed good discrimination in external validation (0.98 and 0.92 for early- and not-early-insulin-treated individuals, respectively). Using a ≥5% probability threshold to guide testing results in positive test rates for monogenic diabetes of 16–19%. Conclusions We developed an updated monogenic diabetes probability calculator that integrates both clinical features and biomarkers, providing greater discrimination than using clinical features or biomarkers alone and providing appropriate measures for selecting individuals for monogenic diabetes diagnostic testing. This is now available as an online calculator and has immediate clinical utility for White European individuals diagnosed with diabetes ≤35 years.

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