1. Novel risk stratification algorithm for estimating the risk of death in patients with relapsed multiple myeloma: external validation in a retrospective chart review.
- Author
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Hájek R, Gonzalez-McQuire S, Szabo Z, Delforge M, DeCosta L, Raab MS, Bouwmeester W, Campioni M, and Briggs A
- Subjects
- Algorithms, Europe, France, Germany, Humans, Retrospective Studies, Risk Assessment, Multiple Myeloma
- Abstract
Objectives and Design: A novel risk stratification algorithm estimating risk of death in patients with relapsed multiple myeloma starting second-line treatment was recently developed using multivariable Cox regression of data from a Czech registry. It uses 16 parameters routinely collected in medical practice to stratify patients into four distinct risk groups in terms of survival expectation. To provide insight into generalisability of the risk stratification algorithm, the study aimed to validate the risk stratification algorithm using real-world data from specifically designed retrospective chart audits from three European countries., Participants and Setting: Physicians collected data from 998 patients (France, 386; Germany, 344; UK, 268) and applied the risk stratification algorithm., Methods: The performance of the Cox regression model for predicting risk of death was assessed by Nagelkerke's R
2 , goodness of fit and the C-index. The risk stratification algorithm's ability to discriminate overall survival across four risk groups was evaluated using Kaplan-Meier curves and HRs., Results: Consistent with the Czech registry, the stratification performance of the risk stratification algorithm demonstrated clear differentiation in risk of death between the four groups. As risk groups increased, risk of death doubled. The C-index was 0.715 (95% CI 0.690 to 0.734)., Conclusions: Validation of the novel risk stratification algorithm in an independent 'real-world' dataset demonstrated that it stratifies patients in four subgroups according to survival expectation., Competing Interests: Competing interests: RH has received research funding from Amgen and Celgene, consultancy fees from Amgen, Celgene and Takeda, and honoraria from Amgen, Bristol-Myers Squibb and Janssen. SG-M, ZS and MC are employees of Amgen Europe and stockholders in Amgen. MD has received research funding from Celgene and Janssen, and honoraria from Amgen, Bristol-Myers Squibb, Celgene, Janssen and Takeda. LD is an employee of Amgen and a stockholder in Amgen. MSR has received research funding from Amgen and Novartis, consultancy fees from Amgen, Bristol-Myers Squibb, Celgene, Takeda and Novartis, and has participated in advisory boards for Celgene, Bristol-Myers Squibb, Amgen and Janssen. WB is an employee of Pharmerit International, which received funding from Amgen to conduct this research. AB has received consultancy fees from Amgen in relation to the work reported here., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)- Published
- 2020
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