Dynamic machine learning model to forecast patient availability for clinical trials.

Abstract

e13641 Background: Patient eligibility is a routine concept in clinical research, comprised of simple or complex set of inclusion or exclusion criteria applied in a specific sequence. Our methodology introduces the concept of ‘availability’ as a narrower term that is closer to the actual number that will be considered for a specific study and who may decide to participate. This specific research develops Machine Learning (ML) model that dynamically predicts likelihood of Multiple Myeloma progression and departure from targeted outcomes to sustain therapy and to indicate potential availability for clinical trials – to reduce screening efforts and improve screening precision of clinical research organizations. Methods: The cohort for training these models was drawn from the ConcertAI Oncology Research database, further enriched by key variables derived from unstructured data sources. Eligible patients for the analysis had a diagnosis of Multiple Myeloma made after Jan 1, 2020. Progressions were labelled using International Myeloma Working Group Criteria. Patients that did not satisfy the criteria were censored. Labels were assigned to patients after initial diagnosis of MM, and the duration between diagnosis to label was computed to build time to event (TTE) models. Over 2000 features were created to build this model, e.g., laboratory values, vitals, biomarkers, medications, ECOG, stage, etc. Variable creation preserved temporality of lab variables and vitals and noted presence of other variables. Several ML methods were employed to build the TTE models, with gradient boosting resulting in the highest c-index (0.8) based on 12 clinically relevant features. A temporal validation of the model was performed on a simulated index date. Results: Cohort used for training the model comprised of N = 4315 patients with average age at diagnosis of 72.3 years, and ~11% of the patients labelled as progressing. Temporal validation was performed on N = 1874 patients, with ~9% progression rate. The area under the curve (AUC) and weighted F1 score for this cohort was 87% and 89% respectively. Conclusions: Patient availability assessments are complex, not supported effectively by existing solutions and often require 100’s of hours from highly trained clinical personnel. An AI model predicting Multiple Myeloma progression built using a large-scale real-world dataset demonstrated 2-4x improvement in forecasting patient availability over other approaches. Integrating these solutions into clinical workflows can improve the effectiveness of research teams; enable more patients to have clinical trials as viable options at the right time; and allow care teams to have forward visibility to choices of standard-of-care and clinical trial alternatives in the best interest of patient outcomes. [Table: see text]