Burden of illness in patients with acute myeloid leukemia aged ò65 years ineligible for intensive chemotherapy.
Bibliographic record
Abstract
e18529 Background: Management of older patients with acute myeloid leukemia (AML) is challenging due to a higher comorbidity burden, poorer performance status and less favorable biology. This study assessed treatment patterns and healthcare resource utilization (HRU) in the US in patients diagnosed with AML aged ≥65 years who did not receive intensive chemotherapy. Methods: Patients aged ≥65 years with ≥2 diagnoses for AML were identified from the Truven Health MarketScan Analytics Databases (01/01/2011-07/31/2016). Patients had ≥1 bone marrow diagnosis procedure (BX; first AML diagnosis following BX defined as the index date), ≥12 months of continuous eligibility pre-index, no treatment with intensive chemotherapy at any time, no diagnosis for AML relapse or remission or stem cell transplant before BX, and <2 diagnoses for other blood cancers pre-index. Post-index treatment patterns and HRU were reported. Results: 1,492 patients with AML were identified (mean [standard deviation] age: 76.8 [7.0] years; 61% males). Mean post-index follow-up was 212 (255) days and 43% were treated with antineoplastic agents (AA). Most common first-line treatments were azacitidine (35%), decitabine (32%) and hydroxyurea (16%). 4% received low-dose cytarabine. Patients with ≥1 blood transfusion (61%) received 8.9 (9.5) transfusions per month during 177 (244) days on average. A total of 3% received stem cell transplant. Patients had a mean of 3.7 (5.3; pre-index: 0.4 [0.7]) days of hospitalization, 0.2 (1.4; pre-index: 0.0 [0.2]) days of hospice care, and 5.2 (4.5; pre-index: 2.6 [2.4]) office visits per month post-index. Compared to treated patients, untreated patients (32%; i.e., patients with no AA, blood transfusion or stem cell transplant) had fewer days of post-index follow-up (106 vs. 263), more days of hospitalization (4.8 vs. 3.2), and of hospice care (0.4 vs. 0.1), and fewer office visits (3.8 vs. 5.8) per month (all P<0.01). Conclusions: Patients ≥65 years diagnosed with AML not receiving intensive chemotherapy incurred more HRU after AML diagnosis. About a third was untreated and had higher HRU than treated patients. This suggests major unmet needs for well-tolerated treatment options for these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".