Emergency Visits or Hospitalizations for Cardiovascular Diagnoses in the Post-Acute Phase of COVID-19
Bibliographic record
Abstract
Background: Prior studies of COVID-19 cardiovascular sequelae include diagnoses made within 4 weeks, but the World Health Organization definition for "postacute phase" is >3 months. Objectives: The purpose of this study was to determine which cardiovascular diagnoses in the postacute phase of COVID-19 are associated with SARS-CoV-2 infection. Methods: Retrospective cohort study of all adults in Alberta who had a positive SARS-CoV-2 reverse transcription polymerase chain reaction test between March 1, 2020 and June 30, 2021, matched (by age, sex, Charlson Comorbidity score, and test date) with controls who had a negative reverse transcription polymerase chain reaction test. Results: The 177,892 patients with laboratory confirmed SARS-CoV-2 infection (mean age 42.7 years, 49.7% female) were more likely to visit an emergency department (5.7% vs 3.3%), be hospitalized (3.4% vs 2.1%), or die (1.3% vs 0.4%) within 1 month than matched test-negative controls. After 3 months, cases were significantly more likely than controls to have an emergency department visit or hospitalization for diabetes mellitus (1.5% vs 0.7%), hypertension (0.6% vs 0.4%), heart failure (0.2% vs 0.1%), or kidney injury (0.3% vs 0.2%). In the 6,030 patients who had survived a hospitalization for COVID-19, postacute phase risks were substantially greater for diabetes mellitus (9.5% vs 3.0%, adjusted odds ratio [aOR]: 3.16 [95% CI: 2.43-4.12]), hypertension (3.5% vs 1.4%, aOR: 2.89 [95% CI: 1.97-4.23]), heart failure (2.1% vs 0.7%, aOR: 3.16 [95% CI: 1.88-5.29]), kidney injury (3.1% vs 0.8%, aOR: 2.70 [95% CI: 1.71-4.28]), bleeding (1.5% vs 0.5%, aOR: 3.40 [95% CI: 1.83-6.32]), and venous thromboembolism (0.8% vs 0.3%, aOR: 3.60 [95% CI: 1.59-8.13]). Conclusions: Clinicians should screen COVID-19 survivors for diabetes mellitus, hypertension, heart failure, and kidney dysfunction in the postacute phase.
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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 | 0.001 |
| 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 | 0.001 |
| 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".