Methodology of a Novel Risk Stratification Algorithm for
Patients with Multiple Myeloma in the Relapsed Setting

J 2019

Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting

BOUWMEESTER, Walter, Andrew BRIGGS, Ben VAN HOUT, Roman HAJEK, Sebastian GONZALEZ-MCQUIRE et. al.

Basic information

Original name

Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting

Authors

BOUWMEESTER, Walter (528 Netherlands, guarantor), Andrew BRIGGS (826 United Kingdom of Great Britain and Northern Ireland), Ben VAN HOUT (826 United Kingdom of Great Britain and Northern Ireland), Roman HAJEK (203 Czech Republic), Sebastian GONZALEZ-MCQUIRE (756 Switzerland), Marco CAMPIONI (756 Switzerland), Lucy DECOSTA (826 United Kingdom of Great Britain and Northern Ireland) and Lucie BROŽOVÁ (203 Czech Republic, belonging to the institution)

Edition

ONCOLOGY AND THERAPY, NEW YORK, SPRINGER, 2019, 2366-1070

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30204 Oncology

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

RIV identification code

RIV/00216224:14110/19:00111947

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1007/s40487-019-00100-5

UT WoS

000493779800001

Keywords in English

Algorithm; Multiple myeloma; Prognostic model; Risk; Survival

Abstract

V originále

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.

Citovat

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.

@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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