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Record W4408187759 · doi:10.1016/j.lana.2025.101038

Associations of COVID-19 vaccination with risks for post-infectious cardiovascular complications: an international cohort study in cancer patients with SARS-CoV-2 infection

2025· article· en· W4408187759 on OpenAlexaboutno aff
Emily Pei-Ying Lin, Chih–Yuan Hsu, Sanjay Mishra, Elizabeth A. Griffiths, Brahm H. Segal, Clara Hwang, Sunny R. K. Singh, Nino Balanchivadze, Chinmay Jani, Melissa G. Mariano, Padmanabh Bhatt, Kendra Vieira, Peter Paul Yu, Eric J. Oligino, Trisha M. Wise‐Draper, Elizabeth K Ferrara, Rana R. McKay, Taylor K. Nonato, Chris Labaki, Eddy Saad, R.M. Saliby, Alicia K. Morgans, Anju Nohria, Matthew Puc, Melissa Accordino, Brianne E Bodin, Rahul Nanchal, Harpreet Singh, Stephanie Berg, Blanche H. Mavromatis, Hannah D. McManus, Susan Halabi, Toni K. Choueiri, Jeremy L. Warner, Yu Shyr

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2025
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institutes of HealthTaipei Medical UniversityMinistry of Science and Technology, TaiwanNational Center for Advancing Translational SciencesFoundation for the National Institutes of HealthMinistry of Science and Technology of the People's Republic of ChinaTaipei Medical University HospitalVanderbilt Institute for Clinical and Translational ResearchRhode Island HospitalNational Science and Technology Council
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCohortVaccinationCancerCohort studyVirologyInfectious disease (medical specialty)Internal medicineOutbreakDisease

Abstract

fetched live from OpenAlex

Background: Whether COVID-19 vaccination is associated with risks for cardiovascular complications after SARS-CoV-2 infection in patients with cancer is unknown. The objective of this study was to investigate the associations between the two. Methods: This registry (COVID-19 and Cancer Consortium)-based retrospective cohort study included patients with laboratory-confirmed SARS-CoV-2 infection from the United States, Canada, and Mexico between April 2021 and December 2022. Patients without COVID-19 vaccination were assigned to the unvaccinated group and patients with ≥2 doses of COVID-19 vaccination were assigned to the fully-vaccinated group. The primary outcome was a composite of post-infectious cardiac complications, including acute myocardial infarction, other ischemic heart disease, atrial fibrillation, ventricular fibrillation, other arrhythmias, cardiomyopathy, and congestive heart failure. The secondary outcome was a composite measure of post-infectious cardiovascular events, comprising of the cardiac complications along with pulmonary embolism, deep vein thrombosis, superficial vein thrombosis, other thrombosis, and cerebrovascular stroke. Multivariable logistic regression was used for data analysis. Findings: A total of 2729 patients were included for analyses, with 1382 in the unvaccinated group and 1347 in the fully-vaccinated group. The median age of the study population was 65 (interquartile range (IQR), 55-74) years. Overall, 1534 (56.0%) were women; 1272 (47%) were never smokers; 1639 (60%) were not obese; 2043 (75%) had stable cancer, and 446 (16%) took anticoagulants at baseline. The primary and secondary analyses showed lower risks of cardiac complications and cardiovascular events in the fully-vaccinated group, with adjusted odds ratios (aOR) of 0.66 (95% confidence interval (CI), 0.48-0.89) and 0.76 (95% CI, 0.59-0.99), respectively. The protective trend with COVID-19 vaccination was observed across infections with different dominant SARS-CoV-2 strains and in patients with or without anticoagulant use. Interpretation: COVID-19 vaccination was associated with a reduced risk of cardiac complications and cardiovascular events by 34% and 24%, respectively, after SARS-CoV-2 infection in patients with cancer. Funding: National Institutes of Health USA; National Science and Technology Council of Taiwan.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.152
GPT teacher head0.483
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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