Association Between the 10-Year ASCVD Risk Score and COVID-19 Complications Among Healthy Adults (Analysis from the National Cohort COVID Collaborative)
Bibliographic record
Abstract
COVID-19 complications have been linked to worse outcomes among patients with established atherosclerotic cardiovascular disease (ASCVD). Less is known about the cumulative consequences of multiple ASCVD risk factors on COVID-19 outcomes. We evaluated the dose-response associations between 10-year ASCVD risk scores and COVID-19 complications. The National COVID-19 Cohort Collaborative collects electronic health record data from over 70 US health systems. Our analysis was limited to patients with positive COVID-19 tests without documented ASCVD events at the time of the first positive test. We evaluated the dose-response associations between 10-year ASCVD risk scores, categorized into categorized as low (<7.5%), intermediate (7.5% to 20.0%), or high (>20.0%), and COVID-19 complications, including hospitalizations and mortality. We reported the outcomes using multivariable-adjusted hazard ratios and 95% confidence intervals (CIs). Our cohort included 120,335 patients with documented positive COVID-19 test results who were free of ASCVD events. The mean age was 51.9 ± 16.1 years, 59.4% were women, 15.3% were Black, and 13.7% were Hispanic/Latino. Overall, 15,363 patients (12.8%) were hospitalized and 2,058 (1.7%) died. Patients at intermediate risk of developing ASCVD were had a 1.49 (95% CI 1.41 to 1.56) increased risk of hospitalization and 1.77 (95% CI 1.76 to 1.79) increased risk of mortality compared with patients at low risk. Patients at high risk had a 2.23 (95% CI 2.10 to 2.38) increased risk of hospitalization and a 5.98 (95% CI 5.93 to 6.03) increased risk of mortality. In conclusion, patients in this nationwide cohort at high risk of developing ASCVD are at substantially greater risk of COVID-19 complications. COVID-19 mitigation efforts should focus on these patient populations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".