A methodological approach for creating virtual patient cohorts reflecting real-world diabetes treatment outcomes

A methodological approach for creating virtual patient cohorts reflecting real-world diabetes treatment outcomes. Download PDF Abstract Virtual patients (VPs) are widely used to evaluate the performance, scope, and robustness of glycemic-control strategies. Existing virtual cohorts often include too few subjects or fail to reflect the diversity of the intended clinical population.

Generating new VPs that reproduce real-patient (RP) characteristics is therefore crucial for advancing diabetes therapies. We present a data-driven method to construct VP cohorts matched to individual RPs using routine therapy and outcome data. Starting from published probability distributions of Hovorka model parameters, we generated candidate VPs by Monte Carlo sampling and retained only physiologically plausible parameter sets.

For each RP, the common pool of physiologically plausible VPs was evaluated separately. VPs passing the RP-specific basal-insulin prefilter were then simulated under meal-and-exercise scenarios derived from that RP’s data. We then used a constraint satisfaction problem (CSP) to select, for each RP, the strictest similarity thresholds that preserved at least 20 matched VPs.

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