Real-world evidence of multiple myeloma treated from 2013 to 2019 in the Hospital District of Helsinki and Uusimaa, Finland.

Background: The rapid development of multiple myeloma (MM) management underscores the value of real-world data. In our study we examined 509 adult MM patients treated with immunochemotherapy (ICT) with/without stem cell transplantation (SCT) from 2013 to 2019 in the Hospital District of Helsinki and Uusimaa, Finland. Materials & methods: Our study was based on computational analyses of data integrated into the hospital data lake. Results: After 2017, treatment pattern diversity increased with improved access to novel treatments. 5-year survivals were 74.4% (95% CI: 65.5-84.5) in SCT-eligible and 44.0% (95% CI: 37.6-51.4) in non-SCT subgroups. In the SCT-eligible subgroup, high first-year hospitalization costs were followed by stable resource requirements. Conclusion: Hospital data lakes can be adapted to carry out complex analysis of large MM cohorts.

To better understand how multiple myeloma (a type of blood cancer) is clinically managed, we examined 509 adult patients using advanced computer analysis and data stored in the Hospital District of Helsinki and Uusimaa information system. Our study found that after 2017, there was more variety in treatments due to better access to new therapies. Compared with a nontransplant group (44.0%), patients eligible for stem cell transplantation had a better 5-year survival rate (74.4%) and used higher levels of healthcare resources. Our study highlights the potential of hospital data systems to study large groups of multiple myeloma patients and inform strategies to tackle the burden associated with the treatment costs of multiple myeloma.

Real-world data on diffuse large B-cell lymphoma in 2010-2019: usability of large data sets of Finnish hospital data lakes.

Background: Real-world data on diffuse large B-cell lymphoma (DLBCL) has remained incomplete. In Finland, health record data originally recorded in different hospital data record systems are collectively available via data lake technology, enabling efficient extraction and analysis of large data sets. The usability of Finnish data lake data in the assessment of DLBCL was evaluated. Methods: Adult DLBCL patients diagnosed between 2010 and 2019, home municipality in the Hospital District of Southwest Finland and data available in respective data lake were included. Results: The algorithmic determination of treatment lines and respective survival was successful. Patient characterization was feasible, albeit partly incomplete because of limited data content/availability and coverage. Stage, International Prognostic Index and cell of origin were available for 63.0, 68.3 and 28.4% of patients, respectively. Genetic aberrations were not structurally available or feasible to extract without a manual chart review. Conclusion: Finnish data lakes represent an efficient way to analyze large DLBCL data sets. The current study provides a tool for developing recording practices in routine care.

A State-of-the-Art Roadmap for Biomarker-Driven Drug Development in the Era of Personalized Therapies.

Advances in biotechnology have enabled us to assay human tissue and cells to a depth and resolution that was never possible before, redefining what we know as the "biomarker", and how we define a "disease". This comes along with the shift of focus from a "one-drug-fits-all" to a "personalized approach", placing the drug development industry in a highly dynamic landscape, having to navigate such disruptive trends. In response to this, innovative clinical trial designs have been key in realizing biomarker-driven drug development. Regulatory approvals of cancer genome sequencing panels and associated targeted therapies has brought personalized medicines to the clinic. Increasing availability of sophisticated biotechnologies such as next-generation sequencing (NGS) has also led to a massive outflux of real-world genomic data. This review summarizes the current state of biomarker-driven drug development and highlights examples showing the utility and importance of the application of real-world data in the process. We also propose that all stakeholders in drug development should (1) be conscious of and efficiently utilize real-world evidence and (2) re-vamp the way the industry approaches drug development in this era of personalized medicines.