| BOUWMEESTER, Walter, Andrew BRIGGS, Ben VAN HOUT, Roman HAJEK, Sebastian GONZALEZ-MCQUIRE, Marco CAMPIONI, Lucy DECOSTA and Lucie BROŽOVÁ. Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting. ONCOLOGY AND THERAPY. NEW YORK: SPRINGER, 2019, vol. 7, No 2, p. 141-157. ISSN 2366-1070. Available from: https://dx.doi.org/10.1007/s40487-019-00100-5. |
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@article{1597478,
author = {Bouwmeester, Walter and Briggs, Andrew and van Hout, Ben and Hajek, Roman and GonzalezandMcQuire, Sebastian and Campioni, Marco and DeCosta, Lucy and Brožová, Lucie},
article_location = {NEW YORK},
article_number = {2},
doi = {http://dx.doi.org/10.1007/s40487-019-00100-5},
keywords = {Algorithm; Multiple myeloma; Prognostic model; Risk; Survival},
language = {eng},
issn = {2366-1070},
journal = {ONCOLOGY AND THERAPY},
title = {Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting},
url = {http://dx.doi.org/10.1007/s40487-019-00100-5},
volume = {7},
year = {2019}
}
TY - JOUR
ID - 1597478
AU - Bouwmeester, Walter - Briggs, Andrew - van Hout, Ben - Hajek, Roman - Gonzalez-McQuire, Sebastian - Campioni, Marco - DeCosta, Lucy - Brožová, Lucie
PY - 2019
TI - Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting
JF - ONCOLOGY AND THERAPY
VL - 7
IS - 2
SP - 141-157
EP - 141-157
PB - SPRINGER
SN - 23661070
KW - Algorithm
KW - Multiple myeloma
KW - Prognostic model
KW - Risk
KW - Survival
UR - http://dx.doi.org/10.1007/s40487-019-00100-5
L2 - http://dx.doi.org/10.1007/s40487-019-00100-5
N2 - Introduction Risk stratification tools provide valuable information to inform treatment decisions. Existing algorithms for patients with multiple myeloma (MM) were based on patients with newly diagnosed disease, and these have not been validated in the relapsed setting or in routine clinical practice. We developed a risk stratification algorithm (RSA) for patients with MM at initiation of second-line (2L) treatment, based on data from the Czech Registry of Monoclonal Gammopathies. Methods Predictors of overall survival (OS) at 2L treatment were identified using Cox proportional hazards models and backward selection. Risk scores were obtained by multiplying the hazard ratios for each predictor. The K-adaptive partitioning for survival (KAPS) algorithm defined four groups of stratification based on individual risk scores. Results Performance of the RSA was assessed using Nagelkerke's R-2 test and Harrell's concordance index through Kaplan-Meier analysis of OS data. Prognostic groups were successfully defined based on real-world data. Use of a multiplicative score based on Cox modeling and KAPS to define cut-off values was effective. Conclusion Through innovative methods of risk assessment and collaboration between physicians and statisticians, the RSA was capable of stratifying patients at 2L treatment by survival expectations. This approach can be used to develop clinical decision-making tools in other disease areas to improve patient management. Funding Amgen Europe GmbH.
ER -
BOUWMEESTER, Walter, Andrew BRIGGS, Ben VAN HOUT, Roman HAJEK, Sebastian GONZALEZ-MCQUIRE, Marco CAMPIONI, Lucy DECOSTA and Lucie BROŽOVÁ. Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting. \textit{ONCOLOGY AND THERAPY}. NEW YORK: SPRINGER, 2019, vol.~7, No~2, p.~141-157. ISSN~2366-1070. Available from: https://dx.doi.org/10.1007/s40487-019-00100-5.
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