PF640 MULTIPLE MYELOMA TREATMENT LANDSCAPE FROM 2011 TO 2017 IN ALBERTA, CANADA: RESULTS FROM THE POPULATION‐BASED “IDENTIFYING OUTCOMES IN REAL‐WORLD MULTIPLE MYELOMA” (INFORMM) STUDY
Bibliographic record
Abstract
Background: The treatment landscape of multiple myeloma (MM) is rapidly evolving with the availability of new therapeutic options leading to improved responses and survival rates. The INFORMM study, an ongoing, province‐wide study in Alberta, Canada (population of 4.3 million in 2018), is examining treatment patterns in a real‐world setting using population‐based administrative data to better understand management of newly diagnosed multiple myeloma (NDMM) and outcomes in this era of novel therapies. Aims: Our goal was to analyze baseline characteristics and pre‐existing comorbidities, treatment patterns (including autologous stem cell transplantation [ASCT] and lines of therapy [LOT]), and treatment attrition rates in patients with NDMM, with or without ASCT. Methods: The NDMM population was derived using patient‐level data sources (Discharge Abstract Database, National Ambulatory Care Reporting System, and Practitioner Claims databases), and verified by clinical input from hematologists. Inclusion criteria were age ≥ 18 years, diagnosis of MM between April 2011 and March 2017, ≥ 1 LOT, and data available for a 1‐year period prior to the diagnosis date. Medication information was obtained from the Pharmaceutical Information Network database and patients receiving ASCT were identified using associated procedure codes from health services data sets. Treatment regimens were determined based on treatment availability during the study period, and classified as lenalidomide (LEN)‐based, bortezomib (BOR)‐based, LEN+BOR‐based, or other. Treatment lines were derived, based on a previously published algorithm for administrative data (Song et al. Curr Med Res Opin . 2016;32:95–103), and modified to align with MM treatment guidelines in Alberta. Results: Our study cohort consisted of 1,377 patients (828 men, 549 women). The mean (± standard deviation [SD]) age at diagnosis was 68.9 ± 12.2 years and mean (± SD) follow‐up time was 2.3 ± 1.6 years; 942 (68.4%) patients had a Charlson Comorbidity Index of ≥ 3 and 1,127 (81.8%) patients did not have diabetes at baseline. Overall, regardless of ASCT status, 45.8% (n = 630) of the 1,377 patients in the study cohort received more than one LOT, 47.3% (n = 298 or 21.6% of the overall cohort) went on to receive a third LOT, and 59.1% (n = 176 or 12.8% of the overall cohort) received additional LOTs. Within the first year of diagnosis, 328 (23.8%) patients underwent ASCT. Of these patients, 255 (77.7%) received ASCT as first‐line therapy; the remaining 73 (22.3%) patients received ASCT as second‐line therapy. Overall, higher attrition rates in subsequent LOTs were observed in the ASCT group compared with the non‐ASCT group (Table). Most patients had BOR‐based regimens in first‐line therapy, with increased use of LEN‐based and LEN+BOR‐based regimens observed in subsequent LOTs. Of patients undergoing ASCT (n = 328), 54.0% (n = 177) received maintenance therapy (LEN or BOR monotherapy), regardless of the baseline treatment regimen. Patients undergoing ASCT were younger compared with patients who did not receive ASCT (mean [± SD] age at MM diagnosis was 57.9 ± 7.3 vs 72.3 ± 11.4 years, respectively). Summary/Conclusion: To our knowledge, this is the first population‐based study utilizing administrative health data to examine the treatment landscape in an NDMM population in Alberta, Canada in the current era of novel therapies. High treatment attrition rates emphasize the importance of optimizing first‐line treatment opportunities. image
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".