PD34 Neuron-specific Biomarkers Associated With Neurological Manifestations In COVID-19: An Evidence Mapping Systematic Review
Bibliographic record
Abstract
Introduction We aimed to map and synthesize the available evidence on neuron-specific biomarkers related to COVID-19. Methods A systematic review and qualitative evidence mapping synthesis was performed (PROSPERO-CRD42021266995). Searches were conducted in PubMed and Scopus, and complemented by manual search (July 2021). We included observational studies of any design assessing neurological biomarkers in adult patients (>18 years; with or without neurological comorbidities) diagnosed with COVID-19. Methodological quality of nonrandomized studies (case-control, cohorts) was assessed using the Newcastle-Ottawa Scale. Results Overall, 14 studies (n=485 patients) conducted in Sweden (n=4 articles, 28.5%), Germany (n=3; 21.4%), USA (n=3; 21.4%), Canada, France, Italy and Norway (n=1 study each) were included. The most reported neurological symptoms (n=13 studies, 92.8%) were headache, confusion, general weakness, loss of smell/taste, cognitive impairments and behavioral changes. Prevalent neurological conditions included encephalopathies, neuropathies, myopathies, and delirium; most critical cases presented cerebrovascular events (n=4 studies, 28.5%). Hypertension, diabetes, obesity, dyslipidemia, and chronic lung disease were the most reported comorbidities. Eight different neuron-specific biomarkers were found in primary studies: neurofilament-light chain – NfL (n=10 studies; 71.4%), glial fibrillary acidic-protein – GFAp (n=5; 35.7%), tau protein (n=5; 35.7%), neurofilament-heavy chain – NfH, S100B protein, ubiquitin C-terminal hydrolase L1 - UCH-L1, neuron-specific enolase and beta protein-amyloid – Aβ (n=1 study each). These biomarkers were found both in cerebrospinal fluid and blood/plasma samples even without an evident cytokine storm. In patients with COVID-19, NfL and GFAp can act as sensitive indicators of neuroaxonal and astrocytic damages, respectively. Increased levels of NfL were significantly associated with severe COVID-19, unconsciousness and longer stay in the intensive care unit (p<0.05). Studies had an overall poor to moderate methodological quality. Conclusions We identified eight neuron-specific biomarkers that should be further studied as prognostic factors of COVID-19. These findings can also guide the development of targeted therapies against SARS-CoV-2. Additional well-designed clinical trials are needed to strengthen this evidence and help understand the mechanisms of neurological symptoms and sequelae after COVID-19 infection.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".