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

NCT07420790 | Recruiting DMC

• 1 other identifier: NP 6575
• Study Type: observational
• Target: 1,941
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Acute Myeloid Leukaemia; Artificial Intelligence; Chemotherapy; Chemo-free regimen

Outcome Measures

Primary Outcomes (2)

• Propensity Score Matching (PSM) objective

  Design of a model based on Generative Artificial Intelligence and the Propensity Score Matching methodology for the validation of the "Phase II Study on Venetoclax (VEN) plus Decitabine (DEC) (VEN-DEC) in elderly patients (≥60, \<75 years) with newly diagnosed acute myeloid leukemia (AML) eligible for allogeneic stem cell transplantation (Allo-SCT)". Evaluation of an exploratory approach as an alternative to a randomized phase III study. Propensity Score Matching objective The PSM method can be used to reduce the effects of confounding when using observational data to estimate treatment effects. The objective of this analysis is to validate the VEN-DEC treatment Program as more effective than the conventional chemotherapy treatment for inducing CR in intermediate/high risk AML patients older than 60 years and offering them a higher probability to be transplanted and cured.

  8-12 months

• Artificial Intelligence objectives

  Design of a model based on Generative Artificial Intelligence and the Propensity Score Matching methodology for the validation of the "Phase II Study on Venetoclax (VEN) plus Decitabine (DEC) (VEN-DEC) in elderly patients (≥60, \<75 years) with newly diagnosed acute myeloid leukemia (AML) eligible for allogeneic stem cell transplantation (Allo-SCT)". Evaluation of an exploratory approach as an alternative to a randomized phase III study. AI Objectives By AI generative methodology, the objective is confirming the superiority of VENDEC in AML patients older than 60 years and acquiring information useful to guide the use of VEN-DEC or similar treatments in AML patients with clinical features similar to those of the VEN-DEC phase II study patients' population but younger than 60 years.

  8-12 months

Study Arms (2)

Experimental cohort

The experimental cohort will be represented by 93 elderly patients (aged ≥60 and \<75 years) with AML deemed to be eligible for Allo-HSCT who were treated with VENDEC regimen and submitted to Allo-HSCT in first CR

Historical (Control) Cohort

The historical or control cohort will be represented by 1848 pseudonymized adult patients (aged between \> 18 and \< 75 years) with AML treated with CHT.

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Experimental Cohort The experimental cohort will be represented by 93 elderly patients (aged ≥60 and \<75 years) with AML deemed to be eligible for Allo-HSCT who were treated with VENDEC regimen and submitted to Allo-HSCT in first CR. Historical (Control) Cohort The historical or control cohort will be represented by 1848 pseudonymized adult patients (aged between \> 18 and \< 75 years) with AML treated with CHT

You may qualify if:

• Patients with AML treated with cht (historical cohort) or VenDec (experimental cohort)

Sponsors & Collaborators

• Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia: lead

Study Sites (1)

USD TMO Adulti

Brescia, Italy, 25100, Italy

RECRUITING

MeSH Terms

Conditions

Leukemia, Myeloid, Acute

Condition Hierarchy (Ancestors)

Leukemia, Myeloid; Leukemia; Neoplasms by Histologic Type; Neoplasms; Hematologic Diseases; Hemic and Lymphatic Diseases

Central Study Contacts

Domenico Russo, MD

CONTACT

00390303996811; domenico.russo@unibs.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Ordinary Professor, MD, PhD, Principal Investigator

Study Record Dates

• First Submitted: February 5, 2026
• First Posted: February 19, 2026
• Study Start: February 20, 2026
• Primary Completion (Estimated): July 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: April 30, 2026

Record last verified: 2026-02

Locations

IT (1)