Early mortality following diagnosis of multiple myeloma from 2011 to 2016 in Alberta, Canada: Initial results from the population-based Identifying Outcomes in Real-World Multiple Myeloma (INFORMM) study.
Bibliographic record
Abstract
e19509 Background: The clinical outcomes of patients with newly diagnosed multiple myeloma (NDMM) have improved. However, early mortality (EM) post-diagnosis remains a concern, with incidence ranging from 13% at 2 months to 29% at 1 year after diagnosis. EM can be influenced by patient age, comorbidities, performance status, therapy, and disease biology. Our aim is to examine EM incidence in patients with NDMM and to describe variables that may influence EM in the era of novel chemotherapeutic agents using the INFORMM Study, an ongoing, province-wide real-world evidence study in Alberta, Canada. Methods: Using administrative health data in Alberta (population 4.32 million in 2018), we identified our study population from a validated algorithm of International Classification of Diseases-9 and -10 diagnostic codes. Additionally, the NDMM cohort was confirmed using bone marrow biopsy records, and patients who received therapy for myeloma were derived from the province-wide Pharmaceutical Information Network database. EM was defined as death by any cause within 90 days of diagnosis. Further analyses will be performed to explore potential risk factors associated with EM using regression modeling. Results: We identified 1,729 patients diagnosed with NDMM between 2011 and 2016. EM occurred in 185 patients (10.7%). Of the 185 patients who experienced EM, 156 (84.3%) were aged ≥ 65 years, 174 (94.1%) had a Charlson Comorbidity Index score ≥ 3, and 48 (25.9%) had infectious complications within 90 days of diagnosis. In addition, 99 (53.5%) patients received ≥ 1 anti-myeloma therapy regimen (lenalidomide-based = 5, bortezomib-based = 22, other = 72), whereas 86 (46.5%) did not receive any anti-myeloma therapy. Conclusions: Despite advances in chemotherapeutics and supportive care, patients with NDMM still have a high risk of EM. The higher EM incidence observed in older patients could be attributable to treatment toxicity, comorbidities, or issues surrounding delivery of suitable treatments. Clinicians should consider potential factors associated with EM in their care plan, including in serious illness conversations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".