Abstract 14483: Correlating Blood Type, Infection Risk, and Illness Severity in Patients With COVID-19
Bibliographic record
Abstract
Introduction: There have been over 13 million cases of COVID-19 cases with a 2% mortality rate [1]. Over the past year there has been a significant progress in the understanding and management of COVID-19 infections. There are still many factors and complications of COVID-19 that have yet to be elucidated. There have been multiple studies looking at the possible link between ABO group type and increased risk of mortality. Many studies from China, Europe and Canada indicate that blood group “O” had lower risk of susceptibility and severe infection[2-4]. We wanted to identify weather this trend was applicable within our hospital network in Eastern Pennsylvania. We wanted to identify through retrospective analysis if there is an association between ABO type, Race, Gender, and mortality at 30-day or 90 days. Methods: We identified n=568 patients between February 2020 to June 2020 that were admitted to the hospital for treatment of COVID-19 that also had historical data regarding ABO grouping. Demographics, length of stay, intubations, and smoking history. Once data was collected, statistical analysis was conducted with SPSS for means, standard deviations, and Chi-squared test for non-parametric testing. Results: The breakdown of blood groups in our populations was [ A: 35.1%, B: 11.6%, AB: 7.9%, O:45.3% ]. The average age of patients by blood group was [A:70.9±6 , B: 68.6±15, AB:66.3±12, O: 65±17], The percentage of females in each group is [A:45%, B: 35%, AB:67%, O:50%]. The mean BMI for all groups were 31±7 (p>0.05) and there was no significant difference between blood groups with length of stay [A:8±6.1 , B: 10.5±9.6, AB:8.3±6.7, O: 9±8 (p>0.05)] There was no significant difference in mortality rate among the blood groups during admission [ A: 26%, B: 16%, AB: 13%, O:24% (p=0.17)] or the rate of intubation [ A: 10%, B: 15%, AB: 11%, O:12% (p=0.72)]. Conclusion: Our findings indicate there is no significant difference between mortality, inpatient length of stay, or proportion of intubations between different blood groups. Our population of blood groups was distributed close to distribution of blood group in the general population. While there have been studies that indicate there is a protective nature of type O blood group, we did not see this in our cohort.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".