We had patient-level data from these studies, which we got from the NHLBI BioLINCC data repository. With the individual patient-level data from the trials, we classified patients on the basis of their Charlson Comorbidity Index scores. Then we essentially redid the original analysis through the CCI-based subgroups and looked for evidence of treatment effect heterogeneity.
I think the main takeaway from the analysis of all these studies is that, generally speaking, the patient populations of the trials tended to be fairly narrow in terms of comorbidity burden, which many people might expect. One way we characterized that was by looking at the variance of the CCI scores around the median score of trial participants. It generally varied from about 1.5 to 3.25, but in general, most of the trials had a narrow population of patients. The majority of patients had comorbidity scores ≤ 4, which is about a 60-year-old patient with perhaps one or two major comorbidities.
The last finding was that, despite the narrow range of comorbidity burden within the trials, we still found treatment effect heterogeneity based on comorbidity score. It was suggested in about five trials where we foundThere was also some other treatment effect heterogeneity on a risk difference scale. Overall, we probably found about eight of what we think are potentially important interactions.
It's really hard to, from a holistic standpoint, apply that to patients who are more complex than having one variable or not. Using a comorbidity score, I think, allows us to more holistically capture patient complexity. You could make a case that it's better for finding treatment effect heterogeneity than the way that we classically do it.