Substantial decline in hospital admissions for heart failure accompanied by increased community mortality during COVID-19 pandemic
Bibliographic record
Abstract
AIMS: We hypothesized that a decline in admissions with heart failure during COVID-19 pandemic would lead to a reciprocal rise in mortality for patients with heart failure in the community. METHODS AND RESULTS: We used National Heart Failure Audit data to identify 36 974 adults who had a hospital admission with a primary diagnosis of heart failure between February and May in either 2018, 2019, or 2020. Hospital admissions for heart failure in 2018/19 averaged 160/day but were much lower in 2020, reaching a nadir of 64/day on 27 March 2020 [incidence rate ratio (IRR): 0.40, 95% confidence interval (CI): 0.38-0.42]. The proportion discharged on guideline-recommended pharmacotherapies was similar in 2018/19 compared to the same period in 2020. Between 1 February-2020 and 31 May 2020, there was a 29% decrease in hospital deaths related to heart failure (IRR: 0.71, 95% CI: 0.67-0.75; estimated decline of 448 deaths), a 31% increase in heart failure deaths at home (IRR: 1.31, 95% CI: 1.24-1.39; estimated excess 539), and a 28% increase in heart failure deaths in care homes and hospices (IRR: 1.28, 95% CI: 1.18-1.40; estimated excess 189). All-cause, inpatient death was similar in the COVID-19 and pre-COVID-19 periods [odds ratio (OR): 1.02, 95% CI: 0.94-1.10]. After hospital discharge, 30-day mortality was higher in 2020 compared to 2018/19 (OR: 1.57, 95% CI: 1.38-1.78). CONCLUSION: Compared with the rolling daily average in 2018/19, there was a substantial decline in admissions for heart failure but an increase in deaths from heart failure in the community. Despite similar rates of prescription of guideline-recommended therapy, mortality 30 days from discharge was higher during the COVID-19 pandemic period.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".