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Individual prediction of thrombocytopenia at next chemotherapy cycle -- a model comparison
  • Yuri Kheifetz,
  • Markus Scholz
Yuri Kheifetz
Leipzig University
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Markus Scholz
Leipzig University
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Aims: Thrombocytopoenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual’s thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, two dynamical mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semi-mechanistic model and a comprehensive mechanistic model. In this study, we compare the performance of these models. Methods: We consider close-meshed individual time series data of 135 non-Hodgkin’s lymphoma patients treated with six cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models are compared by determining deviations of predicted and observed degrees of thrombocytopoenia in the next cycles. Results: The mechanistic model results in superior fits of individual time series data. Moreover, prediction accuracy of future cycle toxicities by the mechanistic model is higher even if it used data of two cycles, while the semi-mechanistic model used data of five cycles for the corresponding calibrations. Conclusions: We successfully established a quantitative and clinically relevant method for comparing prediction performance of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semi-mechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision making

Peer review status:ACCEPTED

08 Jun 2020Submitted to British Journal of Clinical Pharmacology
09 Jun 2020Submission Checks Completed
09 Jun 2020Assigned to Editor
16 Jun 2020Reviewer(s) Assigned
30 Jul 2020Review(s) Completed, Editorial Evaluation Pending
03 Aug 2020Editorial Decision: Revise Major
24 Sep 20201st Revision Received
29 Sep 2020Assigned to Editor
29 Sep 2020Submission Checks Completed
29 Sep 2020Review(s) Completed, Editorial Evaluation Pending
05 Oct 2020Reviewer(s) Assigned
06 Nov 2020Editorial Decision: Revise Minor
02 Dec 20202nd Revision Received
03 Dec 2020Submission Checks Completed
03 Dec 2020Assigned to Editor
03 Dec 2020Review(s) Completed, Editorial Evaluation Pending
06 Dec 2020Reviewer(s) Assigned
20 Dec 2020Editorial Decision: Accept