NCT07420790

Design and Validation of a Generative AI and Propensity Score Matching Model for the VEN-DEC Phase II Study in Elderly AML Eligible for Allo-SCT; Evaluation of an Exploratory Approach Respect to a Randomized Phase III Trial

Designing a Generative AI Model and Propensity Score Matching Methodology for Validation of "The Phase II Study on Venetoclax (VEN) Plus Decitabine (DEC) (VEN-DEC) in Elderly (e60 <75years) Patients With Newly Diagnosed Acute Myeloid Leukemia (AML) Eligible for Allogeneic Stem Cell Transplantation (Allo-SCT)". Evaluation of an Exploratory Approach Respect to a Randomized Phase III Trial

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,941 (estimated)
• Sponsor: Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

Summary

To better delineate the contribution of VEN-DEC to the treatment of AML patients aged between ≥ 60 and \< 75 years and deemed fit for Allo-HSCT, real-world data on a patient-level basis will be collected and utilized to generate a matched control cohort of same AML patients treated with intensive chemotherapy. In addittion, to further validate the efficacy of the VEN-DEC treatment approach in elderly AML patients, an advanced generative AI model will be constructed and trained using the historical cohort data. The AI model aims to simulate outcomes based on the standard

Conditions

• Acute Myeloid Leukemia

Timeline

• Start date: 2026-02-01
• Primary completion: 2026-07-01
• Completion: 2026-12-01
• First posted: 2026-02-19
• Last updated: 2026-02-19