A data-zone scoring system to assess the generalizability of clinical trial results to individual patients.

Abstract

Evaluating the applicability of a clinical trial to a specific patient is difficult. A novel framework, the Trial Score, was created to quantify the generalizability of a trial's result based on participants' baseline characteristics and not on the trial's inclusion and exclusion criteria. For each Systolic Blood Pressure Intervention Trial (SPRINT) participant, the Euclidean distance in six-dimensional space from the theoretical "average" participant was calculated to produce an individual Trial Score that incorporates multiple distinct continuous-variable baseline characteristics. We prospectively defined the "data-rich," "data-limited," and "data-free" zones as Trial Scores < 90th percentile, the 90th-97.5th percentile, and >97.5th percentile, respectively. Trial Scores were then calculated for National Health and Nutrition Examination Survey participants to map data zones of the general population. Individual participant data from the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD-BP) was used to test if participants further from the average SPRINT participant behave differently than the overall SPRINT results. The National Health and Nutrition Examination Survey cohort and the ACCORD-BP trial demonstrate large percentages of participants in SPRINT's data-free and data-limited zones. Time-to-event rates seen with intensive and standard blood pressure control in SPRINT were the same as ACCORD-BP participants within SPRINT's data-rich zone (hazard ratio 0.97, p = 0.84 and hazard ratio 0.95, p = 0.70). However, these rates were significantly different than those of ACCORD-BP participants outside SPRINT's data-rich zone (hazard ratio 0.64, p < 0.01 and hazard ratio 0.77, p < 0.01). ACCORD-BP participants with SPRINT Trial Scores in the 90th percentile or below have similar event rates to SPRINT participants in both the intensive and standard blood pressure groups. Quantifying the difference between an individual patient and the average clinical trial participant holds promise as a tool to more precisely determine applicability of a specific trial to individual patients.