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

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

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Key information

Conditions

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

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

USD TMO Adulti

Brescia, Italy, 25100

Location status: Recruiting

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

Treatment and study plan

Primary outcomes

Propensity Score Matching (PSM) objective

Time frame: 8-12 months

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.
Artificial Intelligence objectives

Time frame: 8-12 months

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.

Study contacts

Contact information is provided by the study sponsor or research team.

Domenico Russo, MD

CONTACT

[email protected]

00390303996811

Sponsors and collaborators

Lead sponsor

Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia

Other

Registry information

Official study title

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

Acronym: VenDec-AI

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2026
First posted: Feb 19, 2026
Registry last updated: Apr 30, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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