Impact of Heart Failure on the Clinical Course and Outcomes of Patients Hospitalized for COVID-19. Results of the Cardio-COVID-Italy Multicentre Study
Bibliographic record
Abstract
Abstract Aims To assess the prognostic value of a history of heart failure (HF) in patients with coronavirus disease 2019 (COVID-19). Methods and results We enrolled 692 consecutive patients admitted for COVID-19 in 13 Italian cardiology centres between 1 March and 9 April 2020. Mean age was 67.4 ± 13.2 years, 69.5% of patients were males, 90 (13.0%) had a history of HF, median hospitalization length was 14 days (interquartile range 9–24). In-hospital death occurred in 37 of 90 patients (41.1%) with HF history vs. 126 of those with no HF history (20.9%). The increased risk of death associated with HF history remained significant after adjustment for clinical variables related to COVID-19 and HF severity, including comorbidities, oxygen saturation, lymphocyte count and plasma troponin [adjusted hazard ratio (HR) for death: 2.25; 95% confidence interval (CI) 1.26–4.02; P = 0.006 at multivariable Cox regression model including 404 patients]. Patients with a history of HF also had more in-hospital complications including acute HF (33.3% vs. 5.1%, P < 0.001), acute renal failure (28.1% vs. 12.9%, P < 0.001), multiorgan failure (15.9% vs. 5.8%, P = 0.004) and sepsis (18.4% vs. 8.9%, P = 0.006). Other independent predictors of outcome were age, sex, oxygen saturation and oxygen partial pressure at arterial gas analysis/fraction of inspired oxygen ratio (PaO2/FiO2). In-hospital treatment with corticosteroids and heparin had beneficial effects (adjusted HR for death: 0.46; 95% CI 0.29–0.74; P = 0.001; n = 404 for corticosteroids, and adjusted HR 0.41; 95% CI 0.25–0.67; P < 0.001; n = 364 for heparin). Conclusions Hospitalized patients with COVID-19 and a history of HF have an extremely poor outcome with higher mortality and in-hospital complications. HF history is an independent predictor of increased in-hospital mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".