Novel risk stratification algorithm for estimating the risk of death in patients with relapsed multiple myeloma: external validation in a retrospective chart review
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...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
July 14, 2020
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| In: |
BMJ open
Year: 2020, Volume: 10, Issue: 7, Pages: 1-10 |
| ISSN: | 2044-6055 |
| DOI: | 10.1136/bmjopen-2019-034209 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1136/bmjopen-2019-034209 Verlag, lizenzpflichtig, Volltext: https://bmjopen.bmj.com/content/10/7/e034209 
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| Author Notes: | Roman Hájek, Sebastian Gonzalez-McQuire, Zsolt Szabo, Michel Delforge, Lucy DeCosta, Marc S. Raab, Walter Bouwmeester, Marco Campioni, Andrew Briggs |
MARC
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| 520 | |a 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 R2, 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. | ||
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| 700 | 1 | |a DeCosta, Lucy |e VerfasserIn |4 aut | |
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