Researchers developed a prototype predictive modeling system by using existing data from manually assessed eligible and ineligible patients to develop a prediction model. The model’s performance was evaluated retroactively across three clinical trials, using manually selected patients as controls.
The correlation of the model’s patient selection and the manual patient selection ranged from 0.88 to 0.99 for the three trials, all with high confidence intervals. The results suggest predictive modeling to be a feasible approach to clinical trial selection, without the restrictions of traditional eligibility criteria and with the potential to be automated in the future.
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