Incidence of Tumour Lysis Syndrome in Patients with Acute Myeloid Leukemia During Initiation of Therapy with Azacitidine and Venetoclax: A Retrospective Chart Review from a Canadian Single-Centre Perspective
Bibliographic record
Abstract
Azacitidine and venetoclax (Aza-Ven) are part of a new standard of care for elderly patients with Acute Myeloid Leukemia (AML) [In line with recommendations, patients with AML at our centre were routinely admitted during initiation of Aza-Ven for close monitoring for tumour lysis syndrome (TLS). However, hospitalization impacts patient experience and is a significant resource burden. The objectives of this study were to evaluate the incidence of TLS in this population and identify patients who could safely initiate therapy in our outpatient facility. Of the 48 patients who commenced Aza-Ven as inpatients, the incidence of TLS was 25% using Cairo–Bishop (CB) diagnostic criteria but was mostly due to transient increases in uric acid, phosphate or potassium that remained within the normal laboratory reference range. Using Howard diagnostic criteria, TLS incidence was only 2%. Patients who developed CB TLS had a significantly higher baseline white blood count (WBC; p = 0.01). Patients with WBC of less than 30 × 109/L subsequently completed outpatient initiation of Aza-Ven (n = 15). Only one of these patients developed mild, transient TLS by CB criteria but not by Howard criteria. Our results demonstrate that a significant portion of patients could safely initiate Aza-Ven in our outpatient facility and avoid unnecessary hospitalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".