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Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study)

BACKGROUND: Risk models of chemotherapy-induced (CIN) and febrile neutropenia (FN) have to date focused on determinants measured at the start of chemotherapy. We extended this static approach with a dynamic approach of CIN/FN risk modeling at the start of each cycle. DESIGN: We applied predictive mo...

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Autores principales: Aapro, M., Ludwig, H., Bokemeyer, C., Gascón, P., Boccadoro, M., Denhaerynck, K., Krendyukov, A., Gorray, M., MacDonald, K., Abraham, I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5091320/
https://www.ncbi.nlm.nih.gov/pubmed/27793849
http://dx.doi.org/10.1093/annonc/mdw309
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author Aapro, M.
Ludwig, H.
Bokemeyer, C.
Gascón, P.
Boccadoro, M.
Denhaerynck, K.
Krendyukov, A.
Gorray, M.
MacDonald, K.
Abraham, I.
author_facet Aapro, M.
Ludwig, H.
Bokemeyer, C.
Gascón, P.
Boccadoro, M.
Denhaerynck, K.
Krendyukov, A.
Gorray, M.
MacDonald, K.
Abraham, I.
author_sort Aapro, M.
collection PubMed
description BACKGROUND: Risk models of chemotherapy-induced (CIN) and febrile neutropenia (FN) have to date focused on determinants measured at the start of chemotherapy. We extended this static approach with a dynamic approach of CIN/FN risk modeling at the start of each cycle. DESIGN: We applied predictive modeling using multivariate logistic regression to identify determinants of CIN/FN episodes and related hospitalizations and chemotherapy disturbances (CIN/FN consequences) in analyses at the patient (‘ever’ during the whole period of chemotherapy) and cycle-level (during a given chemotherapy cycle). Statistical dependence of cycle data being ‘nested’ under patients was managed using generalized estimation equations. Predictive performance of each model was evaluated using bootstrapped c concordance statistics. RESULTS: Static patient-level risk models of ‘ever’ experiencing CIN/FN adverse events and consequences during a planned chemotherapy regimen included predictors related to history, risk factors, and prophylaxis initiation and intensity. Dynamic cycle-level risk models of experiencing CIN/FN adverse events and consequences in an upcoming cycle included predictors related to history, risk factors, and prophylaxis initiation and intensity; as well as prophylaxis duration, CIN/FN in prior cycle, and treatment center characteristics. CONCLUSION(S): These ‘real-world evidence’ models provide clinicians with the ability to anticipate CIN/FN adverse events and their consequences at the start of a chemotherapy line (static models); and, innovatively, to assess risk of CIN/FN adverse events and their consequences at the start of each cycle (dynamic models). This enables individualized patient treatment and is consistent with the EORTC recommendation to re-appraise CIN/FN risk at the start of each cycle. Prophylaxis intensity (under-, correctly-, or over-prophylacted relative to current EORTC guidelines) is a major determinant. Under-prophylaxis is clinically unsafe. Over-prophylaxis of patients administered chemotherapy with intermediate or low myelotoxicity levels may be beneficial, both in patients with and without risk factors, and must be validated in future studies.
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spelling pubmed-50913202016-11-03 Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study) Aapro, M. Ludwig, H. Bokemeyer, C. Gascón, P. Boccadoro, M. Denhaerynck, K. Krendyukov, A. Gorray, M. MacDonald, K. Abraham, I. Ann Oncol Original Articles BACKGROUND: Risk models of chemotherapy-induced (CIN) and febrile neutropenia (FN) have to date focused on determinants measured at the start of chemotherapy. We extended this static approach with a dynamic approach of CIN/FN risk modeling at the start of each cycle. DESIGN: We applied predictive modeling using multivariate logistic regression to identify determinants of CIN/FN episodes and related hospitalizations and chemotherapy disturbances (CIN/FN consequences) in analyses at the patient (‘ever’ during the whole period of chemotherapy) and cycle-level (during a given chemotherapy cycle). Statistical dependence of cycle data being ‘nested’ under patients was managed using generalized estimation equations. Predictive performance of each model was evaluated using bootstrapped c concordance statistics. RESULTS: Static patient-level risk models of ‘ever’ experiencing CIN/FN adverse events and consequences during a planned chemotherapy regimen included predictors related to history, risk factors, and prophylaxis initiation and intensity. Dynamic cycle-level risk models of experiencing CIN/FN adverse events and consequences in an upcoming cycle included predictors related to history, risk factors, and prophylaxis initiation and intensity; as well as prophylaxis duration, CIN/FN in prior cycle, and treatment center characteristics. CONCLUSION(S): These ‘real-world evidence’ models provide clinicians with the ability to anticipate CIN/FN adverse events and their consequences at the start of a chemotherapy line (static models); and, innovatively, to assess risk of CIN/FN adverse events and their consequences at the start of each cycle (dynamic models). This enables individualized patient treatment and is consistent with the EORTC recommendation to re-appraise CIN/FN risk at the start of each cycle. Prophylaxis intensity (under-, correctly-, or over-prophylacted relative to current EORTC guidelines) is a major determinant. Under-prophylaxis is clinically unsafe. Over-prophylaxis of patients administered chemotherapy with intermediate or low myelotoxicity levels may be beneficial, both in patients with and without risk factors, and must be validated in future studies. Oxford University Press 2016-11 2016-10-22 /pmc/articles/PMC5091320/ /pubmed/27793849 http://dx.doi.org/10.1093/annonc/mdw309 Text en © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Articles
Aapro, M.
Ludwig, H.
Bokemeyer, C.
Gascón, P.
Boccadoro, M.
Denhaerynck, K.
Krendyukov, A.
Gorray, M.
MacDonald, K.
Abraham, I.
Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study)
title Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study)
title_full Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study)
title_fullStr Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study)
title_full_unstemmed Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study)
title_short Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study)
title_sort predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (monitor-gcsf study)
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5091320/
https://www.ncbi.nlm.nih.gov/pubmed/27793849
http://dx.doi.org/10.1093/annonc/mdw309
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