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Construction of a Machine Learning-Based Predictive Model for Treatment Failure in Elderly Acute Myeloid Leukemia (AML) Using the Venetoclax and Azacitidine (VA) Regimen

Construction of a Machine Learning-Based Predictive Model for Treatment Failure in Elderly Acute Myeloid Leukemia (AML) Using the Venetoclax and Azacitidine (VA) Regimen

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500115834
Enrollment
Unknown
Registered
2025-12-31
Start date
2026-01-01
Completion date
Unknown
Last updated
2026-01-05

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

acute myeloid leukaemia

Interventions

Observation group:None

Sponsors

The Second Affiliated Hospital of Army Medical University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Patients diagnosed with adult acute myeloid leukemia between January 1, 2021, and December 31, 2024 (diagnostic criteria refer to the "Chinese Guidelines for the Diagnosis and Treatment of Adult Acute Myeloid Leukemia [Non-Acute Promyelocytic Leukemia] [2023 Edition]"); 2. Elderly patients aged 60-90 years (including the boundary values); 3. Patients with complete clinical data, whose initial induction regimen was the VA regimen.

Exclusion criteria

Exclusion criteria: 1. Patients with an active second tumor present at the time of diagnosis; 2. Patients who could not undergo efficacy evaluation.

Design outcomes

Primary

MeasureTime frame
Complete remission rate;—

Secondary

MeasureTime frame
Overall Survival;—

Countries

China

Contacts

Public ContactHan Xiao

The Second Affiliated Hospital of Army Medical University

zxiao2@126.com+86 23 6875 5678

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026