Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID‐19
Bibliographic record
Abstract
INTRODUCTION: To reduce mortality in hospitalized patients with COVID-19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID-19. METHODS: This study was conducted on 660 hospitalized patients with CVD and COVID-19 recruited between January 2020 and January 2021 in Iran. All patients were diagnosed with the previous history of CVD like angina, myocardial infarction, heart failure, cardiomyopathy, abnormal heart rhythms, and congenital heart disease before they were hospitalized for COVID-19. We collected data on patient's signs and symptoms, clinical and paraclinical examinations, and any underlying comorbidities. t test was used to determine the significant difference between the two deceased and alive groups. In addition, the relation between pairs of symptoms and pairs of comorbidities has been determined via correlation computation. RESULTS: Our findings suggest that signs and symptoms such as fever, cough, myalgia, chest pain, chills, abdominal pain, nausea, vomiting, diarrhea, and anorexia had no impact on patients' mortality. There was a significant correlation between COVID-19 cardiovascular patients' mortality rate and symptoms such as headache, loss of consciousness (LOC), oxygen saturation less than 93%, and need for mechanical ventilation. CONCLUSIONS: Our results might help physicians identify early symptoms, comorbidities, and risk factors related to mortality in CVD patients hospitalized for 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".