Personalized Prediction of the Time to Loss of Response to Azacytidine in MDS Patients

Azacytidine is the only approved treatment for patients with higher-risk myelodysplastic syndromes (MDS); yet less than half of the patients will achieve a response, whereas the duration of response is highly heterogeneous and there are no predictors for response duration. The aim of this study is to estimate the patient’s time to loss of response (LoR) to azacytidine based on clinical measurements during treatment. To this end, a personalized prediction framework is proposed that estimates the LoR of a new patient using a patient similarity-based approach. Namely, the new patient’s clinical data—represented as a multivariate time series—are compared to a reference set of patients. The comparison uses distance metrics that quantify how similar two patients’ time series are, assuming patients with similar trajectories tend to have similar LoR. Then, the LoR of the new patient is predicted by averaging the outcomes of the most similar reference patients. The pipeline includes a data normalization strategy that centers each feature on its baseline value and scales it to highlight relative changes and distance metrics to quantify similarity. Both real-world and simulated data were utilized to evaluate the proposed methodology, employing the leave-one-out validation and the Mean Absolute Percentage Error (MAPE) to assess accuracy. The estimated MAPE was found to be 30.52% and 11.82% in the real-world and simulated dataset, respectively. The best and most robust predictions were achieved using the Euclidean distance metric and setting the number of most similar patients around three to five. This study proposes a personalized predictive approach for the LoR to azacitidine in the MDS clinical setting, demonstrating potential for a serviceable prediction of LoR and forming the foundation for further research.