e20002 Background: Identifying optimal therapy options can help maximize treatment outcomes. Finding ways to help improve treatment decision is of great value to achieve better patient care. With the availability of robust patient real world data and the application of state of the art Artificial Intelligence and Machine Learning (AIML) technology, new opportunities have emerged for a broad spectrum of research needs from oncology R&D to commercialization. To illustrate the above advancements, this study identified patients diagnosed with CLL who may progress to next line of treatment in the near future (e.g. future 3 months). More importantly, we can identify treatment patterns which are more effective in treating different types of CLL patients. Methods: This study includes multiple steps which have already been analyzed for feasibility: 1. Collect CLL patients. IQVIA's real world data contains ~60,000 active CLL treated patients. ~2,000 patients have progressed line of treatment in 3 month. 2. Define patients into positive and negative cohorts based on those who have/have not advanced to line L2+. 3. Determine patient profiles based on treatment regimens, symptoms, lab tests, doctor visits, hospital visits, and co-morbidity, etc. 4. Select patient and treatment features to fit an AIML predictive model. 5. Test different algorithms to achieve best model results and validate model performance. 6. Score and classify CLL patients into high and low probability based on the predictive model. 7. Match patients based on feature importance and compare regimens between positive and negative cohort. Results: Model accuracy is above 90%. Top clinical features are calculated for each patient. Optimum treatment patterns between high and low probability patients are identified, with controlling patient key features. Conclusions: Conclusions from this study is expected to yield deeper insight into more tailored treatments by patient type. CLL patients started with oral therapy(targeting) have better response than other treatments.
P-292 Tumor downstaging as an associated factor with recurrence of disease in patients with operated rectal cancer after neoadjuvant chemoradiotherapy: Real-world data from Peru
