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  4. Model-Based Prediction of Clinically Relevant Thrombocytopenia after Allogeneic Hematopoietic Stem Cell Transplantation
 
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February 6, 2025
Journal Article
Title

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

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.
Author(s)
Götz, Katharina M.
Saarland University
Turki, Amin T.
University Hospital Essen
Och, Katharina
Saarland University
Selzer, Dominik
Saarland University
Brossette, Christian
Saarland University
Graf, Norbert  
Saarland University
Rauch, Jochen
Fraunhofer-Institut für Biomedizinische Technik IBMT  
Theobald, Stefan  
Saarland University
Braun, Yvonne
Saarland University
Rüdesheim, Simeon  
Weiler, Gabriele
Fraunhofer-Institut für Biomedizinische Technik IBMT  
Rohm, Kerstin
Fraunhofer-Institut für Biomedizinische Technik IBMT  
Schwab, Matthias
University Tübingen
Eisenberg, Lisa  
Universität Ulm  
Pfeifer, Nico  
Max-Planck-Institut für Informatik
Schwarz, Ulf
Saarland University
Kiefer, Stephan
Fraunhofer-Institut für Biomedizinische Technik IBMT  
Riede, Claudia
Averbis GmbH Freiburg
Smola, Sigrun  
Beelen, Dietrich W.
University Hospital Essen
Kaddu-mulindwa Dominic  
Saarland University
Rissland, Jürgen  
Bittenbring, Jörg Thomas
Universität des Saarlandes
Lehr, Thorsten  
Saarland University
Journal
Clinical Pharmacology and Therapeutics  
Open Access
DOI
10.1002/cpt.3580
Additional link
Full text
Language
English
Fraunhofer-Institut für Biomedizinische Technik IBMT  
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