Construction and validation of a risk prediction model for complications in patients with acute leukemia based on machine learning.

ABSTRACT: Early-phase severe complications remain a major cause of morbidity and mortality during induction chemotherapy for acute leukaemia. Existing risk scores capture only limited prognostic variance and are rarely well-calibrated for clinical decision support. To develop and externally validate a machine-learning model that accurately predicts severe complications after induction, and to assess its clinical utility across key patient sub-groups. We retrospectively assembled electronic-health-record data from three tertiary haematology centres (2013-2024). After exclusion of duplicates and predefined ineligible cases, 2 870 adults with newly diagnosed AML or ALL were analysed (derivation = 2 009; external validation = 861). Forty-two candidate predictors spanning demographics, comorbidity indice

SUBMITTER: Xu R 

PROVIDER: S-EPMC12630594 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature