COVID-19 in patients affected by red blood cell disorders, results from the European registry ERN-EuroBloodNet
Bibliographic record
Abstract
Background Despite several publications covering patients from multiple centers, no international registry covered all patients with red blood cell diseases (RBCD) affected by COVID-19. The ERN-EuroBloodNet’s registry provided real-time registration of SARS-CoV-2 patients with RBCD, promoting timely disease-specific knowledge sharing during the pandemic’s early stages. Procedures The study evaluated patient distribution, the infection across different RBBDs, and severity risk factors across similar healthcare systems, using data collected from the ERN-EuroBloodNet’s REDCap platform. Results From April 2020 to April 2023, 681 infections were recorded among 663 patients, of which 373 had transfusion-dependent thalassemia or non-transfusion-dependent thalassemia (TDT/NTDT), and 269 had sickle cell disease (SCD). SCD patients had a higher incidence of COVID-19 than those with TDT/NTDT (10.5 vs. 4.8 COVID/100 patients). Notably, 92% of the cases were mild, with neither age nor the specific RBCD affecting severity. The number of comorbidities, notably obesity and hypertension, that patients had prior to infection was associated with more severe COVID-19. During the infection, the presence of vaso-occlusive crises, acute chest syndrome, kidney failure, and ground-glass opacities on chest tomography scans were associated with a more severe clinical picture. The vaccination rate (32%) mirrored that of the general population and showed a protective effect against severe COVID-19. The observed mortality rate was 0.7%, aligning with Europe’s general population. Conclusion: SARS-CoV-2 infection in SCD and TDT/NTDT patients is mild and without higher mortality than the general population. The ERN-Eurobloodnet’s registry collaborative structure exemplifies the power of international cooperation in tackling rare diseases, especially during health emergencies
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".