Viral reactivations and co-infections in COVID-19 patients: a systematic review
Bibliographic record
Abstract
Abstract Background Viral reactivations and co-infections have been reported among COVID-19 patients. However, studies on the clinical outcomes of different viral reactivations and co-infections are currently in limit. Thus, the primary purpose of this review is to perform an overarching investigation on the cases of latent virus reactivation and co-infection in COVID-19 patients to build collective evidence contributing to improving patient health. The aim of the study was to conduct a literature review to compare the patient characteristics and outcomes of reactivations and co-infections of different viruses. Methods Our population of interest included confirmed COVID-19 patients who were diagnosed with a viral infection either concurrently or following their COVID-19 diagnosis. We extracted the relevant literature through a systematic search using the key terms in the online databases including the EMBASE, MEDLINE, Latin American Caribbean Health Sciences Literature (LILACS), from inception onwards up to June 2022. The authors independently extracted data from eligible studies and assessed the risk of bias using the Consensus-based Clinical Case Reporting (CARE) guidelines and the Newcastle–Ottawa Scale (NOS). Main patient characteristics, frequency of each manifestation, and diagnostic criteria used in studies were summarized in tables. Results In total, 53 articles were included in this review. We identified 40 reactivation studies, 8 coinfection studies, and 5 studies where concomitant infection in COVID-19 patients was not distinguished as either reactivation or coinfection. Data were extracted for 12 viruses including IAV, IBV, EBV, CMV, VZV, HHV-1, HHV-2, HHV-6, HHV-7, HHV-8, HBV, and Parvovirus B19. EBV, HHV-1, and CMV were most frequently observed within the reactivation cohort, whereas IAV and EBV within the coinfection cohort. In both reactivation and coinfection groups, patients reported cardiovascular disease, diabetes, and immunosuppression as comorbidities, acute kidney injury as complication, and lymphopenia and elevated D-dimer and CRP levels from blood tests. Common pharmaceutical interventions in two groups included steroids and antivirals. Conclusion Overall, these findings expand our knowledge on the characteristics of COVID-19 patients with viral reactivations and co-infections. Our experience with current review indicates a need for further investigations on virus reactivation and coinfection among COVID-19 patients.
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 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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".