Clinical Characteristics, Outcomes and Prognostic Factors for Patients with Acute Myeloid Leukemia Admitted to Intensive Care Settings
Bibliographic record
Abstract
Abstract Abstract 4320 Introduction Acute myeloid leukemia (AML) is a life-threatening, hematological malignancy that is characterized by a rapid clonal proliferation of myeloblasts. Patients with AML often present with compromised bone marrow function, increasing their risk for treatment-related morbidity and mortality, which may be complicated by patient-related factors such as age and co-morbid medical conditions. The objectives of our study were to determine the clinical characteristics, outcomes and prognostic factors of patients with acute myeloid leukemia (AML) admitted to intensive care settings (intensive care unit ICU or coronary care unit CCU). Methods We conducted a retrospective chart analysis of patients diagnosed and treated for AML at Hamilton Health Sciences (in Hamilton, Ontario, Canada) between January 2004-December 2011. Inpatients admitted to hospital were included in the analysis. We included patients with de novo, secondary and relapsed AML. Treatment for AML included: induction or consolidation chemotherapy, palliative and supportive measures. All patients admitted to intensive care settings were age (within three years) and gender matched to a control group of 24 patients with AML who did not require admission to an intensive care setting. We also looked at the number of co-morbid medical conditions between the two groups. Results A total of 115 patient charts were analyzed retrospectively. 24 patients (20.8%) required ICU/CCU admission during their treatment for AML. 11 patients (46%) were male. The most common reason for admission to an intensive care setting was respiratory distress (79.1%). The majority of patients requiring admission had de novo AML (66.7%) with intermediate risk cytogenetics (63.6%). Of the 24 patients requiring admission to an intensive care setting, 9 patients (37.5%) did not survive to be discharged from hospital. All of the remaining 15 patients survived at least 30 days post discharge from hospital. Using univariate analysis, white blood cell count (WBC) at diagnosis (p=0.01), type of AML (p=0.002), AML treatment (p=0.001) and admission to an intensive care setting (p=0.032) affected overall survival for the entire cohort. Using multivariate analysis, WBC at diagnosis (hazard ratio 1.007; 95% CI 1.002 to 1.012) and type of AML (hazard ratio for the comparison of denovo AML vs other categories is 0.059; 95% CI 0.009 to 0.367) affected overall survival. Using univariate analysis, denovo AML vs other types of AML (p=0.04) and percentage of peripheral blood blasts at diagnosis (borderline p=0.06) predicted admission to an intensive care setting. The number of co-morbid medical conditions did not predict admission to an intensive care setting. Conclusions The majority of AML patients do not require admission to an intensive care setting, however, the mortality rate of AML patients admitted to an intensive care setting is high. In this small retrospective dataset admission to an intensive care setting was associated with a significant difference in overall survival, but despite admission, long term survival is possible (overall survival for entire cohort 75%). Also, although a number of factors were significant on univariate analysis, (WBC at diagnosis, intensive care setting admission, type and treatment of AML), on multivariate analysis the only factors affecting overall survival were WBC at diagnosis and the type of AML. Type of AML predicted admission to an intensive care setting, there was no significant effect of the number of co-morbid medical conditions on admission to an intensive care setting. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".