Risk of Severe Outcomes From COVID-19 in Immunocompromised People During the Omicron Era: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Key Points Question: What are the risks of severe outcomes from COVID-19 in people with immunocompromising/immunosuppressive (IC/IS) conditions in the Omicron era? Findings: This systematic review and meta-analysis found increased risk of severe outcomes for people with IC/IS conditions (e.g., autoimmunity, cancer, liver disease, renal disease, transplant) compared with people without the respective conditions. Of all meta-analyzed conditions, transplant recipients had the highest risk of severe COVID-19 outcomes, compared with non-transplant recipients or the general population. Meaning: People with IC/IS conditions remain at increased risk of severe outcomes from COVID-19 during the Omicron era; continued preventative measures and personalized care are crucial. Importance This is the first meta-analysis to investigate the risk of severe outcomes for individuals with immunocompromising/immunosuppressive (IC/IS) conditions specifically in the Omicron era. Objective To assess the risk of mortality and hospitalization from COVID-19 in people with IC/IS conditions compared with people without IC/IS conditions during the Omicron era. Data Sources A systematic search of Embase, MEDLINE, PubMed, Europe PMC, Latin American and Caribbean Health Sciences Literature, Cochrane COVID-19 Study Register, and WHO COVID-19 Database was performed to identify studies published between 1 January 2022 and 13 March 2024. Study Selection Inclusion criteria were observational studies that included people (all ages) with at least 1 of the following conditions: IC/IS unspecified groups, transplant (solid organ, stem cells, or bone marrow), any malignancy, autoimmune diseases, any liver diseases, chronic or end-stage kidney disease, and advanced/untreated HIV. In total, 72 studies were included in the review, of which 66 were included in the meta-analysis. Data Extraction and Synthesis Data were extracted by one reviewer and verified by a second. Studies were synthesized quantitively (meta-analysis) using random-effect models. PRISMA guidelines were followed. Main Outcomes and Measures Evaluated outcomes were risks of death, hospitalization, intensive care unit (ICU) admission, and any combination of these outcomes. Odds ratios, hazard ratios, and rate ratios were extracted; pooled relative risk (RR) and 95% confidence intervals (CI) were calculated. Results Minimum numbers of participants per IC/IS condition ranged from 12 634 to 3 287 816. Risks of all outcomes were increased in people with all meta-analyzed IC/IS conditions compared with people without the respective conditions. Of all meta-analyzed IC/IS conditions, transplant recipients had the highest risk of death (RR, 6.78; 95% CI, 4.41-10.43; P <.001), hospitalization (RR, 6.75; 95% CI, 3.41-13.37; P <.001), and combined outcomes (RR, 8.65; 95% CI, 4.01-18.65; P <.001), while participants in the unspecified IC/IS group had the highest risk of ICU admission (RR, 3.38; 95% CI, 2.37-4.83; P <.001) compared with participants without the respective IC/IS conditions or general population. Conclusions In the Omicron era, people with IC/IS conditions have a substantially higher risk of death and hospitalization from COVID-19 than people without these conditions.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".