# Model‐Based Prediction of Clinically Relevant Thrombocytopenia after Allogeneic Hematopoietic Stem Cell Transplantation

## TL;DR

This study creates a model to predict platelet recovery after stem cell transplants, helping identify patients at risk for low platelet counts and poor outcomes.

## Contribution

A novel model for predicting post-transplant thrombocytopenia using early data and Bayesian forecasting.

## Key findings

- Thrombocytopenia affected 37% of patients and was linked to reduced survival.
- The model achieved high predictive accuracy (AUC ≥ 0.68 to 0.81) from day +7 to +28 post-transplant.
- Prognostic markers included anti-thymocyte globulin, donor relation, and total protein measurements.

## Abstract

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.