Real-World Costs of Azacitidine Treatment in Patients With Higher-Risk Myelodysplastic Syndromes/Low Blast–Count Acute Myeloid Leukemia
Bibliographic record
Abstract
PURPOSE: Azacitidine (AZA) is a standard of care for higher-risk myelodysplastic syndrome (MDS)/low blast–count acute myeloid leukemia (AML). Despite this, there is a paucity of data on the real-world health care resource utilization costs of AZA in this population. METHODS: We linked the Ontario AZA MDS registry—higher-risk MDS/low blast–count AML—to population-based health system administrative databases. Patients were observed for 24 months after first AZA and censored at the earliest of 90 days after last AZA, date of death, time of AML induction/stem-cell transplantation, or March 31, 2016. Costs (2015 Canadian dollars) were expressed as standardized mean and median 28-day costs. Univariable quantile regression was used to explore the association of baseline patient and disease characteristics and median cost. Multivariable quantile regression was used to explore predictors of median costs. RESULTS: Among 877 patients in the registry, mean standardized 28-day cost per patient was $17,638 (median, $15,272; interquartile range [IQR], $11,869-$19,580) and $13,450 (median, $11,043; IQR, $7,981-$14,882) excluding the cost of AZA. Major nondrug drivers of cost were cancer clinic visits and inpatient care (mean standardized 28-day cost, $4,631; median, $1,558; IQR, $238-$4,961). Transfusion dependence at AZA initiation ( P = .001) and greater comorbid disease burden ( P = .009) were independently associated with increased cost. CONCLUSION: Our cohort of patients with uniformly higher-risk MDS/low blast–count AML treated with AZA demonstrates substantial costs of care above and beyond the cost of AZA alone. These results provide insight into the costs of AZA in the real world with implications for resource allocation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".