COVID-19 severity and cardiovascular outcomes in SARS-CoV-2-infected patients with cancer and cardiovascular disease
Bibliographic record
Abstract
Data regarding outcomes among patients with cancer and co-morbid cardiovascular disease (CVD)/cardiovascular risk factors (CVRF) after SARS-CoV-2 infection are limited. To compare Coronavirus disease 2019 (COVID-19) related complications among cancer patients with and without co-morbid CVD/CVRF. Retrospective cohort study of patients with cancer and laboratory-confirmed SARS-CoV-2, reported to the COVID-19 and Cancer Consortium (CCC19) registry from 03/17/2020 to 12/31/2021. CVD/CVRF was defined as established CVD or no established CVD, male ≥ 55 or female ≥ 60 years, and one additional CVRF. The primary endpoint was an ordinal COVID-19 severity outcome including need for hospitalization, supplemental oxygen, intensive care unit (ICU), mechanical ventilation, ICU or mechanical ventilation plus vasopressors, and death. Secondary endpoints included incident adverse CV events. Ordinal logistic regression models estimated associations of CVD/CVRF with COVID-19 severity. Effect modification by recent cancer therapy was evaluated. Among 10,876 SARS-CoV-2 infected patients with cancer (median age 65 [IQR 54–74] years, 53% female, 52% White), 6253 patients (57%) had co-morbid CVD/CVRF. Co-morbid CVD/CVRF was associated with higher COVID-19 severity (adjusted OR: 1.25 [95% CI 1.11–1.40]). Adverse CV events were significantly higher in patients with CVD/CVRF (all p<0.001). CVD/CVRF was associated with worse COVID-19 severity in patients who had not received recent cancer therapy, but not in those undergoing active cancer therapy (OR 1.51 [95% CI 1.31–1.74] vs. OR 1.04 [95% CI 0.90–1.20], pinteraction <0.001). Co-morbid CVD/CVRF is associated with higher COVID-19 severity among patients with cancer, particularly those not receiving active cancer therapy. While infrequent, COVID-19 related CV complications were higher in patients with comorbid CVD/CVRF. (COVID-19 and Cancer Consortium Registry [CCC19]; NCT04354701).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".