Analysis of ABO and Rh Blood Type Association With Acute COVID-19 Infection in Hospitalized Patients: A Superficial Association Among a Multitude of Established Confounders
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide, and considerable effort is focused on identifying certain populations at increased risk. ABO blood types have been associated with disease susceptibility; however, evidence remains limited. Our aim was to determine the association between ABO/Rh blood type with disease susceptibility and mortality among admitted COVID-19 patients. METHODS: A retrospective analysis of patients with COVID-19 requiring admission was undertaken. Demographics and pertinent medical history were analyzed with respect to ABO/Rh blood type: between the cases and a control population; as well as with respect to mortality in the COVID-19 population in univariate analysis. Potential confounding factors were evaluated by multivariate models. The main outcomes were disease susceptibility by comparison of blood type prevalence between populations, and mortality within the COVID-19 population. RESULTS: A total of 825 cases (admitted with confirmed COVID-19 infection by reverse transcriptase-polymerase chain reaction (RT-PCR)) and 396 controls (seen at the same institution during the calendar year of 2019) were included. The COVID-19 population was older with male predominance. It was heavily represented by blood types O-positive (53%) and A-positive (23%), while lower representation was observed in groups B-positive (odds ratio (OR): 0.61, P = 0.013) and AB-positive (OR: 0.46, P = 0.014). Neither relationship remained significant in pairwise analysis. Within the COVID-19 population, no mortality difference was appreciated between ABO groups (P = 0.312), but higher mortality was observed in Rh negative group (P = 0.01). The latter of which was significantly confounded by age (P < 0.001), sex (P = 0.022), body mass index (BMI) (P = 0.001), and hemoglobin A1c (HbA1c) (P < 0.001) in multivariate analysis. CONCLUSIONS: While type A blood appears to be weakly more prevalent with respect to B and AB types in hospitalized patients, strong confounders of age and sex dilute this significance. Rh-negative patients appear to have a higher mortality, although this too is strongly confounded. Overall, ABO and Rh blood types do not have a significant relationship with susceptibility and mortality with COVID-19 infection in our population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".