Association of chronic diseases with mortality among hospitalized patients with COVID-19 treated with convalescent plasma: Evidence from a single center – Iraq
Bibliographic record
Abstract
Objective/Background: While there is no universal agreement on the benefits of convalescent plasma (CP) in the treatment of COVID-19 infection, very little is known about whether CP affects the association between chronic diseases and mortality. It is known that chronic diseases are associated with worse outcomes, but it is not clear whether the transfusion of CP impacts this association. This study aims to evaluate this impact considering that few studies have discussed the treatment of COVID-19 patients with CP. Methods: A cross-sectional study was conducted among COVID-19 patients treated with CP at the main hospital for treatment and isolation of COVID-19 in Anbar/Iraq during the period of May to August 2020. A questionnaire on socio-demographic characteristics, smoking, and contact with COVID-19 was conducted. The presence of comorbidity, especially hypertension, heart failure, coronary artery disease, diabetes mellitus, chronic pulmonary diseases, asthma, and the hospital outcome (discharged home alive or cure/death) were included. The Chi-square test (or Fisher's exact test) was used to measure the association between comorbidity with the outcome. Logistic regression was used to assess the main predictors for cure (discharged home alive) and death outcome. Results: The mean age of the subjects was (53.05 ± 15.16), with a higher death rate among the older age group, especially those over 60-year with non-survivors at age of 65 or more. There was a statistically significant association between the presence of comorbidity and the mortality rate (P < 0.001). Subjects with chronic disease had a 5 times higher risk for death due to COVID-19 in comparison to those without the chronic disease (OR = 5.8, C.I. = 2.01-16.96, P = 0.001), hypertension showed the highest predictor (C.I = 1.43- 22.29, p = 0.013). Smoking and gender were not associated with mortality. Conclusion: The present study found a high mortality rate among COVID-19 patients treated with CP, specially for those with age over 60, or with comorbidities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".