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.

Management of patients with acute lymphoblastic leukemia in routine clinical practice: Minimal residual disease testing, treatment patterns and clinical outcomes in Belgium, Greece and Switzerland.

OBJECTIVES: To describe real-world management and clinical and economic outcomes of patients with B-cell precursor acute lymphoblastic leukemia (ALL) in Belgium, Greece and Switzerland. METHODS: This descriptive, retrospective medical chart review collected patient-level data in 2018 from adults with ≥1 minimal residual disease (MRD) test during front-line ALL treatment. Data were stratified by MRD status. RESULTS: Eighty-two patients were included (median age 44 years, 23 % Philadelphia chromosome-positive; MRD-positive: n = 17, MRD-negative: n = 50, MRD result unknown: n = 15). HyperCVAD (32 %) and HOVON (26 %) were the most frequently used front-line treatment protocols; 22 % of patients received stem cell transplantation. Overall, 76 % of ALL patients were hospitalized (mean 1.1 hospitalization/month). Complete hematological response (CRh) occurred in 66/82 patients (80 %). Median relapse-free survival from CRh was 32.7 months (MRD-positive: 11.7 months; MRD-negative: 33.3 months). Median overall survival from diagnosis was 28.9 months (MRD-positive: 15.3 months; MRD-negative: not reached). Most patients (88 %) were MRD tested during induction; testing rates considerably decreased thereafter (39 % during consolidation). CONCLUSIONS: B-cell precursor ALL represents a clinical burden and impacts healthcare resources; MRD-positive patients have worse prognosis than MRD-negative patients. Efforts should be made to adhere to recommendations for MRD testing in clinical guidelines.

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.

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

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 between them. Methods. Systematic and targeted literature reviews identified disease- and patient-specific attributes of MM that affect disease progression and outcomes. These attributes were validated by a Delphi panel of four international MM experts, and a physician-validated model was constructed. Real-world clinical data from the Czech Registry of Monoclonal Gammopathies (RMG) was used to confirm the relationships between attributes using pairwise correlations and multiple Cox regression analysis. Results. The Delphi panel reached consensus that most cytogenetic abnormalities influenced disease activity, which results in symptoms and complications and affects overall survival (OS). Comorbidities and complications also affect OS. The entire panel agreed that quality of life was influenced by comorbidities, age, complications, and symptoms. Consensus was not reached in some cases, in particular, the influence of del(17p) on complications. The relationships between attributes were confirmed using pairwise analysis of real-world data from the Czech RMG; most of the correlations identified were statistically significant and the strength of the correlations changed with successive relapses. Czech RMG data were also used to confirm significant predictors of OS included in the model, such as age, Eastern Cooperative Oncology Group performance status, and extramedullary disease. Conclusions. This validated conceptual model can be used for economic modeling and clinical decision making. It could also inform the development of disease-based models to explore the impact of disease progression and treatment on outcomes in patients with MM.

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.

Real-world Outcomes of Multiple Myeloma: Retrospective Analysis of the Czech Registry of Monoclonal Gammopathies.

INTRODUCTION: Real-world data on patient outcomes and treatment patterns in multiple myeloma (MM) are limited. MATERIALS AND METHODS: The present noninterventional, observational, retrospective analysis of prospectively collected Czech patient medical record data from the Registry of Monoclonal Gammopathies estimated real-world outcomes in adults with a diagnosis of symptomatic MM made between May 2007 and June 2014. RESULTS: In total, 2446 patients had initiated first-line treatment. The median overall survival since the diagnosis (primary endpoint) was 50.3 months (95% confidence interval, 46.1-54.5 months) and decreased with each successive treatment line. A similar trend was observed for progression-free survival and the depth of response. In line with European guidelines and clinical practice, bortezomib-, thalidomide-, and lenalidomide-based regimens were most commonly used across all treatment lines (42.3%, 28.9%, and 18.4%, respectively). In the first line, bortezomib and thalidomide were used most often, with lenalidomide the most commonly used agent in the relapse setting (second to fourth lines). Exploratory analyses revealed that younger age (≤ 65 years), lower international staging system stage, and previous stem cell transplantation were associated with significant improvements in overall and progression-free survival, especially in the early treatment lines. CONCLUSION: The present study is the first analysis of Czech data from the Registry of Monoclonal Gammopathies, and it provides important insights into the real-world management of MM for physicians and healthcare providers.

Optimizing choice of oral interferon-free treatment for genotype 1 hepatitis C virus using testing for NS5A resistance: a cost-utility analysis from the perspective of the Italian National Health Service.

BACKGROUND: Patients with genotype-1 hepatitis C virus infection who have failed to respond to standard therapy or who relapse following treatment may be considered for an interferon-free regimen incorporating a nonstructural protein 5A (NS5A) inhibitor. Sustained virologic response (SVR) with these regimens is typically >90%, but this is reduced in patients with NS5A resistance. European Association for Study of the Liver guidelines recommend simeprevir + sofosbuvir ± ribavirin (SMV+SOF±R) for re-treating patients failing an NS5A inhibitor-containing regimen. An alternative strategy would be to test for NS5A resistance prior to treatment, with therapy optimized based on the results. This study investigates the cost-effectiveness of this strategy. MATERIALS AND METHODS: A Markov model was used to estimate disease progression for treatment-experienced genotype 1 patients with severe fibrosis or compensated cirrhosis. Targeted treatment with either SMV+SOF±R or sofosbuvir + ledipasvir ± ribavirin (SOF+LDV±R) based on pretreatment NS5A resistance testing was compared to routine SOF+LDV±R without testing. Treatment duration was 12 or 24 weeks for patients with severe fibrosis or compensated cirrhosis (Metavir F3/F4). SVR data for the treatment options were based on the results of published clinical trials. The analysis was carried out from the perspective of the Italian National Health Service. RESULTS: Optimized treatment using NS5A resistance testing yielded 0.163 additional QALYs and increased costs of €2,789 per patient versus no testing. The incremental cost-effectiveness ratio (ICER) was €17,078/QALY. Sensitivity analysis identified the SVR attributable to each of the treatment regimens as the most sensitive determinant of ICER (range: €10,055/QALY-€43,501/QALY across plausible range). Probabilistic sensitivity analysis demonstrated that, at a willingness-to-pay threshold of €30,000/QALY, the probability that NS5A-directed treatment will be cost-effective is 81.4%. CONCLUSION: Optimizing therapy with either SMV+SOF±R or SOF+LDV±R based on pretreatment NS5A resistance testing was cost-effective from the perspective of the Italian National Health Service, in treatment-experienced patients with severe fibrosis or compensated cirrhosis.

Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist.

Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the article for the Editors' Summary.

Prediction models for clustered data: comparison of a random intercept and standard regression model.

BACKGROUND: When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. METHODS: Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. RESULTS: The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. CONCLUSION: The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Reporting and methods in clinical prediction research: a systematic review.

BACKGROUND: We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures. METHODS AND FINDINGS: We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%). Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively. CONCLUSIONS: The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability.

Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews.

BACKGROUND: The interest in prognostic reviews is increasing, but to properly review existing evidence an accurate search filer for finding prediction research is needed. The aim of this paper was to validate and update two previously introduced search filters for finding prediction research in Medline: the Ingui filter and the Haynes Broad filter. METHODOLOGY/PRINCIPAL FINDINGS: Based on a hand search of 6 general journals in 2008 we constructed two sets of papers. Set 1 consisted of prediction research papers (n = 71), and set 2 consisted of the remaining papers (n = 1133). Both search filters were validated in two ways, using diagnostic accuracy measures as performance measures. First, we compared studies in set 1 (reference) with studies retrieved by the search strategies as applied in Medline. Second, we compared studies from 4 published systematic reviews (reference) with studies retrieved by the search filter as applied in Medline. Next--using word frequency methods--we constructed an additional search string for finding prediction research. Both search filters were good in identifying clinical prediction models: sensitivity ranged from 0.94 to 1.0 using our hand search as reference, and 0.78 to 0.89 using the systematic reviews as reference. This latter performance measure even increased to around 0.95 (range 0.90 to 0.97) when either search filter was combined with the additional string that we developed. Retrieval rate of explorative prediction research was poor, both using our hand search or our systematic review as reference, and even combined with our additional search string: sensitivity ranged from 0.44 to 0.85. CONCLUSIONS/SIGNIFICANCE: Explorative prediction research is difficult to find in Medline, using any of the currently available search filters. Yet, application of either the Ingui filter or the Haynes broad filter results in a very low number missed clinical prediction model studies.

Quality of low back pain guidelines improved.

STUDY DESIGN: Systematic review of clinical guidelines. OBJECTIVE: To assess the methodological quality of clinical guidelines for the management of acute and chronic low back pain (LBP) in primary care and compare their recommendations. SUMMARY OF BACKGROUND DATA: A guideline evaluation performed in 2004 concluded that the quality and transparency of the development process and consistency in the reporting of primary care guidelines for LBP need to be improved. At present, several guidelines have been revised and new guidelines are published. We evaluated if the quality of guidelines has improved. METHODS: Guidelines published since 2004 were selected by electronically searching in MEDLINE, Cochrane Back Review Group database, Guideline Clearing House, Google, and contacting experts. The methodological quality of the guidelines was assessed by 2 authors independently, using the Appraisal of Guidelines, Research, and Evaluation in Europe instrument. Also, the diagnostic and therapeutic recommendations were compared. RESULTS: Fourteen guidelines were included. In general, the quality was satisfactory. The guidelines had best scores on clarity and presentation. The domain scores of scope and purpose were often moderate due to the absence of description of the clinical questions. The domain of stakeholder involvement scored moderate, mostly because guidelines were not tested among target users. Domains that had generally low scores were applicability and editorial independence. Four guidelines scored low on the rigor of development, but the other guidelines scored high on this domain.The diagnostic and therapeutic recommendations in the guidelines for acute LBP were mainly comparable while the recommendations for the management of chronic LBP varied widely. CONCLUSION: Compared to the quality assessment performed in 2004, the average quality of guidelines has improved. However, guideline developers should still improve the quality transparency of the development process. Especially the applicability of guidelines and the editorial independence need to be ensured in future guidelines.