COVID-19 outcomes in patients with sickle cell disease and sickle cell trait compared with individuals without sickle cell disease or trait: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Clinical manifestations and severity of SARS-CoV-2 infection in individuals with sickle cell disease (SCD) and sickle cell trait (SCT) are not well understood yet. Methods We performed a systematic review and meta-analysis to assess COVID-19 outcomes in individuals with SCD or SCT compared to individuals without sickle cell disease or trait. An electronic search on PubMed, Embase, and Cochrane Library was performed on August 3, 2023. Two authors (IFM and ISP) independently screened (IFM and ISP) and extracted data (IFM and ILC) from included studies. Main exclusion criterion was the absence of the non-SCD/SCT group. Exposure effects for binary endpoints were compared using pooled odds ratio (OR) with 95% confidence intervals (CI). I 2 statistics was used to assess the heterogeneity and DerSimonian and Laird random-effects models were applied for all analyses to minimize the impact of differences in methods and outcomes definitions between studies. The overall quality of evidence was assessed using the GRADE system. Review Manager 5.4 and R software (v4.2.2) were used for statistical analyses. Registered with PROSPERO, CRD42022366015. Findings Overall, 22 studies were included, with a total of 1892 individuals with SCD, 8677 individuals with SCT, and 1,653,369 individuals without SCD/SCT. No difference in all-cause mortality was seen between SCD/SCT and non-SCD/SCT (OR 1.18; 95% CI 0.78–1.77; p = 0.429; I 2 = 82%). When considering only studies adjusted for confounders (8 studies), patients with SCD/SCT were shown to be at increased risk of death (OR 1.86; 95% CI 1.30–2.66; p = 0.0007; I 2 = 34%). No significant difference was seen between individuals with SCD and SCT (p = 0.863). The adjusted for confounders analysis for hospitalisation revealed higher rates for the SCD (OR 5.44; 95% CI 1.55–19.13; p = 0.008; I 2 = 97%) and the SCT groups (OR 1.31; 95% CI 1.10–1.55; p = 0.002; I 2 = 0) compared to the non-SCD/SCT population. Moreover, it was significantly higher for the SCD group (test for subgroup difference; p = 0.028). Interpretation Our findings suggest that patients with SCD or SCT may present with a higher mortality and hospitalisation rates due to COVID-19 infection. Funding None.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".