Prediction of the Clinical Course of Immune Thrombocytopenia in Children by Platelet Kinetics
Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to p...
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| Published in | HemaSphere Vol. 7; no. 11; pp. e960 - n/a |
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| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Philadelphia, PA
Lippincott Williams & Wilkins
01.11.2023
Lippincott, Williams & Wilkins Wiley |
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| Abstract | Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic–pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The Kheal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the kheal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature. |
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| AbstractList | Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic–pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The K heal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the k heal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature. Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic–pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The Kheal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the kheal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature. Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic–pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The K heal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the k heal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature. Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic–pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The Kheal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the kheal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature. Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic-pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The Kheal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the kheal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature.Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic-pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The Kheal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the kheal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature. |
| Author | Mallebranche, Coralie Grain, Audrey Raoult, Violette Lejeune, Julien Dubrasquet, Mathilde Gyan, Emmanuel Ravalet, Noémie Monceaux, Françoise Ternant, David Herault, Olivier Pellier, Isabelle Bayart, Sophie Chauvin, Romane |
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| Cites_doi | 10.1111/bcp.15334 10.1182/blood-2014-04-570127 10.1182/blood-2008-07-162503 10.1038/psp.2013.14 10.1111/jth.15125 10.1067/mpd.2002.128547 10.1111/j.1651-2227.2005.tb01887.x 10.1111/ped.13505 10.1038/psp.2014.12 10.1097/MOH.0b013e3282ba5552 10.1200/JCO.2002.02.140 10.1182/blood-2014-05-578336 10.1053/j.seminhematol.2013.03.013 10.3390/jcm10040789 10.1002/pbc.28840 10.1111/j.1600-0609.2008.01206.x 10.1111/bjh.16387 10.1038/psp.2012.4 10.5045/br.2016.51.2.95 10.1002/pbc.29023 10.1002/pbc.21978 10.1016/j.clinbiochem.2008.04.001 10.1038/s41598-020-70501-4 |
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| Notes | Supplemental digital content is available for this article. Received: December 23, 2022 / Accepted: August 16, 2023 Supplemental digital content is available for this article. Correspondence: Julien Lejeune (j.lejeune@chu-tour.fr). ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 PMCID: PMC10615561 |
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| Title | Prediction of the Clinical Course of Immune Thrombocytopenia in Children by Platelet Kinetics |
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