Despite data suggesting that individuals with multiple myeloma can benefit from receiving several lines of therapy, and guidelines recommending treatment after relapse, a recent European patient chart review found that only 61% of patients receive second-line treatment. The review found that factors such as old age and previous adverse events lead to physicians deciding not to treat after relapse. However, given the large number of regimens available, treatment can be tailored to individual patients' needs and supportive care measures can help with the management of adverse effects. If approved therapies are not suitable for a patient, guidelines recommend registration in a clinical trial, yet only 7% of patients in the review were participating in such studies. A need for better education on the range of treatments available and their risk-benefit profiles is suggested. Access to new drugs should be examined to maximise the number of patients benefitting from them.

Going the distance: Are we losing patients along the multiple myeloma treatment pathway?

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. 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. Performance of the RSA was assessed using Nagelkerke’s R2 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. 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. Amgen Europe GmbH.

/pdf/methodology-of-a-novel-risk-stratification-algorithm-for-119pus8wgi.pdf

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

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.

https://bmjopen.bmj.com/content/bmjopen/10/7/e034209.full.pdf

Novel risk stratification algorithm for estimating the risk of death in patients with relapsed multiple myeloma: external validation in a retrospective chart review

Background. We aimed to develop and validate a conceptual model of multiple myeloma (MM) that characterizes the attributes affecting disease progression and patient outcomes, and the relationships ...

https://journals.sagepub.com/doi/pdf/10.1177/2381468318814253

Development of an Initial Conceptual Model of Multiple Myeloma to Support Clinical and Health Economics Decision Making.

Multiple myeloma (MM) is a malignancy with varying survival outcomes and drivers of disease progression. Existing MM staging tools were developed using data from newly diagnosed patients. As patient characteristics and disease-related factors change between diagnosis and the initiation of second-line (2L) treatment, an unmet need exists for a tool that can evaluate risk of death at first relapse. We have developed a risk stratification algorithm (RSA) using data from patients with MM who were at 2L. Hazard ratios for independent predictors of overall survival (OS) were derived from a Cox models, and individual patient scores were calculated for total risk. K-adaptive partitioning for survival was used to stratify patients into groups based on their scores. Relative risk doubled with ascending risk group; median OSs for patients in group 1 (lowest risk)-4 (highest risk) were 61·6, 29·6, 14·2 and 5·9 months, respectively. Differences in OS between risk groups were significant. Similar stratification was observed when the RSA was applied to an external validation data set. In conclusion, we have developed a validated RSA that can quantify total risk, frailty risk and disease aggressiveness risk, and stratify patients with MM at 2L into groups with profoundly different survival expectations.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjh.16105

Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma.

Predictors of Overall Survival (OS) in Patients with Multiple Myeloma (MM) Initiating First- and Second-Line Treatment in the Czech Republic

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Predictors of Overall Survival (OS) in Patients with Multiple Myeloma (MM) Initiating First- and Second-Line Treatment in the Czech Republic