Clinical prevalence and outcome of cardiovascular events in the first 100 days postallogeneic hematopoietic stem cell transplant
Bibliographic record
Abstract
INTRODUCTION: Recent advances in allogeneic hematopoietic stem cell transplant (HSCT) have allowed us to offer HSCT to older, advanced disease patients with more co-morbidities. Cardiovascular toxicity post-transplant is a major concern due to the increased risk of mortality. Few studies have examined the prevalence of CV events including CAD (MI, angina, PCI, CABG, CHF, arrhythmias), HTN, stroke/TIA, and death in the first 100 days post-transplant. PATIENTS: We assessed the impact of pretransplant MUGA results in predicting postallogeneic HSCT CV events and overall survival in the first 100 days, and whether or not transient anthracycline-induced cardiomyopathy or cumulative anthracycline dose affected overall survival. This retrospective, cohort study included 665 patients with a median age of 52 years who underwent HSCT from 2009 to 2015. RESULTS: The most frequent CV event in the first 100 days post-HSCT was arrhythmia seen in 2.9% of patients followed up by CHF (12.3%), MI (9%), and angina (8%). Two patients had PCI, and both survived the first 100 days. Cardiovascular risk factors predict for a poor MUGA scan but not survival. Higher dose anthracycline pretransplant predicted for a poor outcome. CONCLUSION: A history of CV disease, MI, or CAD was the most important predictive of CV events, P-value = .00002. 88.6% survived the first 100 days. Patients with an EF < 50% had a significant likelihood of having a CV event compared to patients with an EF > 60% (OR = 5.3, 95% CI [1.6-18.1], P = .0219). Cumulative anthracycline dose did not have a significant impact on overall survival.
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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 | 0.001 |
| 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) | 0.002 | 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".