Dynamic Mortality Risk Prediction in Myelodysplastic Syndromes Using Longitudinal Clinical Data.

Abstract

Patients with myelodysplastic syndromes (MDS) exhibit diverse disease trajectories necessitating different clinical approaches ranging from watch-and-wait strategies to hematopoietic stem cell transplantation. Existing risk scores like the IPSS-R or Endothelial Activation and Stress Index provide static risk stratification at diagnosis but do not capture evolving disease dynamics. We addressed this problem by introducing a dynamic, data-driven approach to repeatedly predict short-term mortality risks, across the patient's disease course. We developed a machine learning model on the basis of gradient-boosted decision trees to estimate 1-year mortality risks from both longitudinal parameters from blood values and diagnosis-based features. We trained the model on a data set of patients from the MDS Registry Düsseldorf (n = 1,024) and validated it on patients from University Hospitals Heidelberg (n = 286) and Mannheim (n = 31). Validations on independent cohorts achieved area under the receiver operating characteristic curve scores of around 0.8 and better predictive performance for 1-year mortality compared with a diagnosis-only baseline model. The model accurately predicted mortality risks as early as within the first 90 days of diagnosis. Feature importance analysis revealed clinically plausible feature-label relations, supporting interpretability. Comparison with the IPSS-R and training on 1-year AML progression revealed the advantages and generalizability of the approach. This dynamic risk model enables continuous, individualized assessment of 1-year mortality risk in patients with MDS, offering a supplement to static scores used at diagnosis. Our results highlight the utility and importance of including longitudinal parameters in risk assessment analysis.