Use of Machine Learning to Derive Discrete Clinical Phenotypes and Assess Treatment-Effect Heterogeneity in the Steroids in Cardiac Surgery Trial Dataset

BACKGROUND: Robust clinical trial data provide a key component for the development of evidence-informed medicine. However, clinical trial data may demonstrate treatment-effect heterogeneity, where some patients benefit from an intervention while others receive no benefit or perhaps even harm. If so, targeted therapy or a "personalized medicine" approach could provide treatment to a certain patient subset, that is, a specific clinical phenotype, who are most likely to benefit. Using data from the Steroids in Cardiac Surgery (SIRS) clinical trial, we tested the hypothesis that methylprednisolone, which did not have a significant effect on mortality or major morbidity, improves outcomes in 1 or more clinical phenotypes. METHODS: We used the partitioning around medoids algorithm to derive phenotypic clusters using 30 preoperative variables in a Cleveland Clinic cardiac surgery developmental dataset. Patients in the SIRS trial were assigned to the derived clusters. Methylprednisolone-response heterogeneity was evaluated among SIRS patients for the coprimary outcomes of 30-day mortality and composite of death and major morbidity. This was accomplished by fitting separate logistic regression models for each outcome and evaluating the interaction between treatment groups and assigned phenotypic cluster. RESULTS: The 16,395 patients in the developmental dataset were clustered into 4 phenotypes: younger and healthier; mid-age and moderately sick; oldest, sicker and more aortic valve surgery; sickest, more coronary artery bypass grafting (CABG) and low left ventricular ejection fraction (LVEF). The phenotypes had differing risk profiles and were associated with patient outcomes. For example, patients in sickest, high CABG and low LVEF group were at highest risk amongst all phenotypes, with significantly increased odds of experiencing a composite of mortality and severe morbidity (odds ratio [OR]: 3.4, 95% confidence interval [CI], 2.4-4.8) compared to younger and healthier group. When clustering was applied to the SIRS trial dataset (N = 6836), patients in the sickest, more CABG and low LVEF group similarly represented the highest-risk category for mortality and severe morbidity (OR: 2.1; 95% CI, 1.6-2.9). After examining the treatment-effect in each phenotype, we did not find evidence that methylprednisolone treatment-effect on the coprimary outcomes differed by phenotypes (all treatment-phenotype interaction term P > .05). CONCLUSIONS: Despite substantial differences in preoperative risk profiles, findings were neutral for methylprednisolone across all phenotypes. However, the general concept of evaluating trial results across clinical phenotypes represents a novel approach to identify subgroup differences in treatment-effect and to collect preliminary evidence of potential benefit with targeted therapy.

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