Time to progression of disease and outcomes with second-line BTK inhibitors in relapsed/refractory mantle cell lymphoma.

Time to progression of disease (POD) after first-line (1L) therapy is prognostic in mantle cell lymphoma (MCL), although studies have included a broad range of 1L, second-line (2L), and subsequent lines of therapy. The purpose of this study was to evaluate the factors predicting outcomes in patients with relapsed/refractory (R/R) MCL exclusively initiating 2L Bruton's tyrosine kinase inhibitors (BTKis) after 1L rituximab-containing therapy. Patients were accrued from 8 international centers (7 main, 1 validation cohort). Multivariable models evaluating the association between time to POD and clinical/pathologic factors were constructed and converted into nomograms and prognostic indexes predicting outcomes in this population. A total of 360 patients were included, including 160 in the main cohort and 200 in the validation cohort. Time to POD, Ki67 ≥ 30%, and MCL International Prognostic Index (MIPI) were associated with progression-free survival (PFS2) and overall survival (OS2) from the start of 2L BTKis. C-indexes were consistently ≥0.68 in both cohorts. Web/application-based calculators based on nomograms and prognostic indexes to estimate PFS2 and OS2 were constructed. The 2L BTKi MIPI identifies 3 groups with distinct 2-year PFS2, including high risk (14%), intermediate risk (50%), and low risk (64%). Time to POD, Ki67, and MIPI are associated with survival outcomes in patients with R/R MCL receiving 2L BTKis. Simple clinical models incorporating these variables may assist in planning for alternative therapies such as chimeric antigen receptor T-cell therapy, allogeneic stem cell transplantation, or novel agents with alternative mechanisms of action.

Direct costs of antineoplastic and supportive treatment for progressive multiple myeloma in a tax-based health system.

Aim: To estimate current real-world costs of drugs and supportive care for the treatment of multiple myeloma in a tax-based health system. Methods: Forty-one patients were included from a personalized medicine study (2016-2019). Detailed information was collected from patient journals and hospital registries to estimate the total and mean costs using inverse probability weighting of censored data. Results: Total observed (censored) costs for the 41 patients was €8.84 million during 125 treatment years, with antineoplastic drugs as the main cost driver (€5.6 million). Individual costs showed large variations. Mean 3-year cost per patient from first progression was €182,103 (€131,800-232,405). Conclusion: Prediction of real-world costs is hindered by the availability of detailed costing data. Micro-costing analyses are needed for budgeting and real-world evaluation of cost-effectiveness.

Lay abstract In recent years, there has been a dramatic improvement in the treatment of multiple myeloma due to the introduction of new drugs. These drugs have significantly increased survival but have also had an immense impact on healthcare budgets. In this study, we used detailed treatment information for multiple myeloma patients in combination with billing data from the hospital pharmacy at a Danish hospital to calculate individual cost histories for both drugs and supportive care. Using these data, we estimated the mean 3-year cost of a multiple myeloma patient to be €182.103, but we also found large variation between patients, causing an uncertainty of €50.000 in either direction. We believe that detailed costing studies, similar to the present one, are necessary for evaluation of cost-effectiveness of drugs in clinical practice.

Optimizing Outcome Prediction in Diffuse Large B-Cell Lymphoma by Use of Machine Learning and Nationwide Lymphoma Registries: A Nordic Lymphoma Group Study.

PURPOSE: Prognostic models for diffuse large B-cell lymphoma (DLBCL), such as the International Prognostic Index (IPI) are widely used in clinical practice. The models are typically developed with simplicity in mind and thus do not exploit the full potential of detailed clinical data. This study investigated whether nationwide lymphoma registries containing clinical data and machine learning techniques could prove to be useful for building modern prognostic tools. PATIENTS AND METHODS: This study was based on nationwide lymphoma registries from Denmark and Sweden, which include large amounts of clinicopathologic data. Using the Danish DLBCL cohort, a stacking approach was used to build a new prognostic model that leverages the strengths of different survival models. To compare the performance of the stacking approach with established prognostic models, cross-validation was used to estimate the concordance index (C-index), time-varying area under the curve, and integrated Brier score. Finally, the generalizability was tested by applying the new model to the Swedish cohort. RESULTS: In total, 2,759 and 2,414 patients were included from the Danish and Swedish cohorts, respectively. In the Danish cohort, the stacking approach led to the lowest integrated Brier score, indicating that the survival curves obtained from the stacking model fitted the observed survival the best. The C-index and time-varying area under the curve indicated that the stacked model (C-index: Denmark [DK], 0.756; Sweden [SE], 0.744) had good discriminative capabilities compared with the other considered prognostic models (IPI: DK, 0.662; SE, 0.661; and National Comprehensive Cancer Network-IPI: DK, 0.681; SE, 0.681). Furthermore, these results were reproducible in the independent Swedish cohort. CONCLUSION: A new prognostic model based on machine learning techniques was developed and was shown to significantly outperform established prognostic indices for DLBCL. The model is available at https://lymphomapredictor.org .