Model-Based Prediction of Clinically Relevant Thrombocytopenia after Allogeneic Hematopoietic Stem Cell Transplantation.

Platelet reconstitution after allogeneic hematopoietic cell transplantation (allo-HCT) is heterogeneous and influenced by various patient- and transplantation-related factors, associated with poor prognoses for poor graft function (PGF) and isolated thrombocytopenia. Tailored interventions could improve the outcome of patients with PGF and post-HCT thrombocytopenia. To provide individual predictions of 180-day platelet counts from early phase data, we developed a model of long-term platelet reconstitution after allo-HCT. A large cohort (n = 1949) of adult patients undergoing their first allo-HCT was included. Real-world data from 1,048 retrospective patients were used for non-linear mixed-effects model development. Bayesian forecasting was used to predict platelet-time profiles for 518 retrospective and 383 prospective patients during internal and external model validation, respectively. Thrombocytopenia was defined as mean platelet count < 75 × 109/L, derived from the last 12 platelet measurements within the first 180 days post-HCT. Thrombocytopenia affected 37% of all patients and was associated with significantly reduced overall survival (P-value < 0.0001). On days +7, +14, +21, and +28, the developed model achieved areas under the receiver-operating characteristic of ≥ 0.68, ≥ 0.75, ≥ 0.78, and 0.81 for the prediction of post-HCT thrombocytopenia, respectively, with anti-thymocyte globulin, donor relation, and total protein measurements representing prognostic markers for post-HCT platelet kinetics. A publicly accessible web-based demonstrator of the model was established (https://hsct.precisiondosing.de). In summary, the developed model predicts individual platelet counts from day +28 post-HCT adequately, utilizing internal and external datasets. The web-based demonstrator provides a basis to implement model-based predictions in clinical practice and to confirm these findings in future clinical studies.