Short Report: Hospitalization for New-Onset Heart Failure in Survivors of Hospitalized COVID-19
Bibliographic record
Abstract
AIMS: Previous studies have reported an incidence of new-onset heart failure (HF) among COVID-19 survivors ranging from 0.7 to 8.5 per 100 person-years, but they relied on administrative data for outcome ascertainment. Given the public health implications, a more accurate characterization of the HF burden post-COVID-19 is important. METHODS AND RESULTS: We conducted a prospective cohort study of survivors of hospitalized COVID-19 and tracked the incidence of new-onset HF hospitalizations over the 12-month period following the index COVID-19 episode. Outcome ascertainment was based on a combination of chart reviews, patient interviews, and pre-specified clinical, radiographic and laboratory criteria. We identified 2140 survivors of COVID-19 hospitalization that were free of HF at the time of discharge. Their mean age was 67 years and 48% were Black/African-Americans. The incidence rate of hospitalized new-onset HF was 0.5 per 100 person-years. Higher BMI and dialysis dependency at baseline were significantly associated with HF development. The 1-year mortality rate was 3%. CONCLUSIONS: The incidence of new-onset HF post-COVID-19 in our study was lower than in previous reports, despite involving an older population with more comorbidities and/or more severe COVID-19 overall. Reliance on administrative data for outcome adjudication in prior studies may have led to an overestimation of the HF burden post-COVID-19.
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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 | 0.001 | 0.006 |
| 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) | 0.004 | 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".