Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.

TitleMachine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.
Publication TypeJournal Article
Year of Publication2023
AuthorsXu J, Zhang H, Zhang H, Bian J, Wang F
JournalSci Rep
Date Published2023 Jan 12
KeywordsAlgorithms, Clinical Trials as Topic, Electronic Health Records, Eligibility Determination, Humans, Machine Learning, Models, Statistical

Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients' clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual's EHRs can determine the subphenotypes-homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria.

Alternate JournalSci Rep
PubMed ID36635438
PubMed Central IDPMC9837131
Grant ListR01 AG076448 / AG / NIA NIH HHS / United States
R21 CA253394 / CA / NCI NIH HHS / United States
R01 AG076234 / AG / NIA NIH HHS / United States
RF1 AG072449 / AG / NIA NIH HHS / United States
R21 AG068717 / AG / NIA NIH HHS / United States
Institute of Artificial Intelligence for Digital Health
Faculty Publication