Outcomes of Heart Failure in COVID-19 Patients: An Appalachian Experience
Bibliographic record
Abstract
Background: The Southeastern rural areas of the USA have a higher prevalence of heart failure (HF). Coronavirus disease 2019 (COVID-19) infection is associated with poor outcomes in patients with HF. Our study aimed to compare the outcomes of hospitalized HF patients with and without COVID-19 infection specifically in rural parts of the USA. Methods: We conducted a retrospective cohort study of HF patients with and without COVID-19 hospitalized in Southeastern rural parts of the USA by using the Appalachian Regional Healthcare System. Analyses were stratified by waves from April 1, 2020 to May 31, 2021, and from June 1, 2021 to October 19, 2021. Results: Of the 14,379 patients hospitalized with HF, 6% had concomitant COVID-19 infection. We found that HF patients with COVID-19 had higher mortality rate compared to those without COVID-19 (21.8% versus 3.8%, respectively, P < 0.01). Additionally, hospital resource utilization was significantly higher in HF patients with COVID-19 compared to HF patients without COVID-19 with intensive care unit (ICU) utilization of 21.6% versus 13.8%, P < 0.01, mechanical ventilation use of 17.3% versus 6.2%, P < 0.01, and vasopressor/inotrope use of 16.8% versus 7.9%, P < 0.01. A lower percentage of those with COVID-19 were discharged home compared to those without a COVID-19 diagnosis (63.4% versus 72.0%, respectively). There was a six-fold greater odds of dying in the first wave and seven-fold greater odds of dying in the second wave. Conclusions: Our study confirms previous findings of poor outcome in HF patients with COVID-19. There is a need for review of healthcare resources in rural hospitals which already face numerous healthcare challenges.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".