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

A draft conceptual model of SLC6A1 neurodevelopmental disorder

Introduction

SLC6A1 Neurodevelopmental Disorder (SLC6A1-NDD), first described in 2015, is a rare syndrome caused by a mutation in the SLC6A1 gene which encodes for the GABA Transporter 1 (GAT-1) protein. Epilepsy is one of the most common symptoms in patients and is often the primary treatment target, though the severity of epilepsy is variable. The impact of seizures and other symptoms of SLC6A1-NDD on patients and caregivers is wide-ranging and has not been described in a formal disease concept study.

Methods

A literature search was performed using the simple search term, "SLC6A1." Papers published before 2015, and those which did not describe the human neurodevelopmental disorder were removed from analysis. Open-ended interviews on lived experiences were conducted with two patient advocate key opinion leaders. An analysis of de-identified conversations between families of people with SLC6A1-NDD on social media was performed to quantify topics of concern.

Results

Published literature described symptoms in all of the following domains: neurological, visual, motor, cognitive, communication, behavior, gastrointestinal, sleep, musculo-skeletal, and emotional in addition to epilepsy. Key opinion leaders noted two unpublished features: altered hand use in infants, and developmental regression with onset of epilepsy. Analysis of social media interactions confirmed that the core symptoms of epilepsy and autistic traits were prominent concerns, but also demonstrated that other symptoms have a large impact on family life.

Discussion

For rare diseases, analysis of published literature is important, but may not be as comprehensive as that which can be gleaned from spontaneous interactions between families and through qualitative interviews. This report reflects our current understanding of the lived experience of SLC6A1-NDD. The discrepancy between the domains of disease reported in the literature and those discussed in patient conversations suggests that a formal qualitative interview-based disease concept study of SLC6A1-NDD is warranted.

Developing a fall prevention intervention economic model

PURPOSE

Model-based economic evaluations require conceptualization of the model structure. Our objectives were to identify important health states, events, and patient attributes to be included in a model-based cost-effectiveness analysis of fall prevention interventions, to develop a model structure to examine cost-effectiveness of fall prevention interventions, and to assess the face validity of the model structure.

METHODS

An expert panel comprising clinicians, health service researchers, health economists, a patient partner, and policy makers completed two rounds of online surveys to gain consensus on health states, events, and patient attributes important for fall prevention interventions. The surveys were informed by a literature search on fall prevention interventions for older adults (≥65 years) including economic evaluations and clinical practice guidelines. The results of the Delphi surveys and subsequent discussions can support the face validity of a state-transition model for an economic evaluation of fall prevention interventions.

RESULTS

In total, 11 experts rated 24 health states/events and 41 patient attributes. Consensus was achieved on 14 health states/events and 26 patient characteristics. The proposed model structure incorporated 12 of the 14 selected health states/events. Panelists confirmed the face validity of the model structure during teleconferences.

CONCLUSIONS

There is a dearth of studies presenting the model conceptualization process; consequently, this study involving multiple end user partners with opportunities for input at several stages adds to the literature as another case study. This process is an example of how a fall prevention economic model was developed using a modified Delphi process and assessed for face validity.

Management of Patients with Relapsed and/or Refractory Multiple Myeloma Treated with Novel Combination Therapies in Routine Clinical Practice in Germany

INTRODUCTION

Multiple myeloma remains an incurable plasma cell malignancy which, despite improvements in overall survival over the last decade, is characterized by recurrent relapse and is associated with a poor prognosis. This study investigates the use of novel agents in current real-world clinical practice in the management of relapsed and/or refractory multiple myeloma (RRMM) in Germany over different lines of therapy.

METHODS

A retrospective chart review was conducted for patients with RRMM treated at multiple centers across Germany between May 2017 and June 2018. Variables included patient demographics and clinical characteristics, current and prior treatment regimens, treatment response, cytogenetic abnormalities, testing methodology, and resource utilization.

RESULTS

Data were analyzed from 484 patients from 47 centers across Germany (60% male; average age over 70 years; majority at International Staging System stage 2 or 3). Bone pain and anemia were the most common symptoms at diagnosis, with 63% of patients receiving osteoprotective drugs. Approximately one-third (32%) of patients had received autologous stem cell transplantation and approximately 70% underwent cytogenetic testing. After failure to respond to first-line treatment, most patients received regimens containing second-generation proteasome inhibitors and monoclonal antibodies, with overall response rates greater than 90% in second line (95% and 90% for daratumumab-based and carfilzomib-based therapies, respectively). The incidence of unplanned hospitalization ranged from 11% to 16% across all treatment lines, with longer hospital stays required for treatment administration than for treatment-related toxicity.

CONCLUSION

Although treatment patterns for RRMM in Germany differ by line of therapy and are adapted as disease progresses, patients mostly receive combination regimens with carfilzomib or daratumumab in second and third lines, with high overall response rates achieved in all lines.

Management of patients with multiple myeloma beyond the clinical-trial setting: understanding the balance between efficacy, safety and tolerability, and quality of life

Treatment options in multiple myeloma (MM) are increasing with the introduction of complex multi-novel-agent-based regimens investigated in randomized clinical trials. However, application in the real-world setting, including feasibility of and adherence to these regimens, may be limited due to varying patient-, treatment-, and disease-related factors. Furthermore, approximately 40% of real-world MM patients do not meet the criteria for phase 3 studies on which approvals are based, resulting in a lack of representative phase 3 data for these patients. Therefore, treatment decisions must be tailored based on additional considerations beyond clinical trial efficacy and safety, such as treatment feasibility (including frequency of clinic/hospital attendance), tolerability, effects on quality of life (QoL), and impact of comorbidities. There are multiple factors of importance to real-world MM patients, including disease symptoms, treatment burden and toxicities, ability to participate in daily activities, financial burden, access to treatment and treatment centers, and convenience of treatment. All of these factors are drivers of QoL and treatment satisfaction/compliance. Importantly, given the heterogeneity of MM, individual patients may have different perspectives regarding the most relevant considerations and goals of their treatment. Patient perspectives/goals may also change as they move through their treatment course. Thus, the 'efficacy' of treatment means different things to different patients, and treatment decision-making in the context of personalized medicine must be guided by an individual's composite definition of what constitutes the best treatment choice. This review summarizes the various factors of importance and practical issues that must be considered when determining real-world treatment choices. It assesses the current instruments, methodologies, and recent initiatives for analyzing the MM patient experience. Finally, it suggests options for enhancing data collection on patients and treatments to provide a more holistic definition of the effectiveness of a regimen in the real-world setting.

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

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 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.

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.

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 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.

New Markers of Renal Failure in Multiple Myeloma and Monoclonal Gammopathies

* Correspondence: kasiajanda@op [...].

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

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.