Percutaneous coronary intervention and in‐hospital outcomes in patients with leukemia: a nationwide analysis
Bibliographic record
Abstract
OBJECTIVES: To examine the association between current leukemia diagnosis and in-hospital clinical outcomes in patients undergoing percutaneous coronary intervention (PCI) in the United States. BACKGROUND: Leukemia is the most common hematological malignancy and is associated with an increased risk of thrombotic and bleeding complications in patients undergoing PCI. There are limited data around clinical outcomes of leukemia patients undergoing PCI. METHODS: We used the National Inpatient Sample to investigate the outcomes of leukemia patients undergoing PCI between 2004 and 2014. Patients were then subdivided into diagnoses of acute myeloid leukemia (AML) or chronic myeloid leukemia and acute lymphoid leukemia or chronic lymphoid leukemia (CLL). Multiple logistic regressions were used to study the association of a leukemia diagnosis with in-hospital outcomes: mortality, bleeding, vascular and cardiac complications, and stroke. RESULTS: There were 6,561,445 records of patients who underwent PCI during the study time, of which 15,789 patients had a diagnosis of leukemia. The most common leukemia subtype was CLL accounting for 75% of the cohort (n = 10,800). After multivariable adjustment, a leukemia diagnosis was associated with significantly increased odds of in-hospital mortality (odds ratio [OR]: 1.41; 95% confidence interval [CI]: [1.11-1.79]) and bleeding (OR: 1.87; 95% CI: [1.56-2.09]), whereas patients with AML had a fivefold increase of in-hospital mortality (OR: 5.38; 95% CI: [2.94-9.76]). CONCLUSIONS: Patients with current diagnosis of leukemia are at increased risk of procedure-related complications following PCI. A multidisciplinary approach is needed among interventional cardiologists, oncologists, and hematologists to minimize procedural complications and improve outcomes in this high-risk cohort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".