Development of a Dynamic Counterfactual Risk Stratification Strategy for Newly Diagnosed Patients With AML Treated With Venetoclax and Azacitidine
Year: 2025
Type: article
Abstract: PURPOSE The objective of this study was to develop a flexible risk stratification strategy for AML that is specific for venetoclax plus azacitidine (ven/aza), addresses real-world data (RWD) issues, and is also adaptable to different use cases. METHODS A series of tunable risk models (RMs) were generated from a dynamic counterfactual machine learning (ML) strategy. These used a range of features from diagnostic AML samples and were tested using objective metrics on a single-institution cohort of 316 newly diagnosed patients treated with ven/aza. RM performance was tested using various model assumptions, data elements, and end points and with applications to an external AML real-world cohort (RWC). RESULTS Favorable, intermediate, and adverse risk groups were identified in a series of ML-based RMs using different assumptions, for genetic-only or genetic-plus-phenotypic data elements and with overall survival and event-free survival as end points. Most RMs demonstrated equitable patient distribution (approximately 20%-40% in each risk group), significant separation between risk strata (log-rank–based P values <0.001), and predictability computed by time-dependent survival AUC values of 0.60-0.70. Similar performance was observed when the proposed RM strategy was adapted and compared with the European Leukemia Net 2022 using the external RWC. CONCLUSION The proposed ML strategy addresses a variety of RWD considerations and is readily tunable through coding and parameter updates for different contexts and use case needs. This strategy represents a novel approach to developing more effective RMs for AML and possibly other diseases.
Source: JCO Clinical Cancer Informatics
DOI: 10.1200/cci-24-00308
Authors: Nazmul Islam, Justin L. Dale, Jamie S. Reuben, Karan Sapiah, James W. Coates, Frank R. Markson, Jingjing Zhang, Lezhou Wu, Maura Gasparetto, Brett M. Stevens, Sarah E. Staggs, William M. Showers, Monica R. Ransom, Jairav Desai, Ujjwal V. Kulkarni, Krysta L. Engel, Craig T. Jordan, Michael Boyiadzis, Clayton A. Smith
Institution: University of Colorado Anschutz
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4416928708