The Utility of Blood Based Biomarkers in Detecting Neurological Complications of COVID‐19 in Critically Ill Patients
Bibliographic record
Abstract
Abstract Background Neurological and neuropsychiatric complications have been documented in patients with COVID‐19. This study aims to investigate the utility of two neurological blood‐based biomarkers to predict neurological complications and mortality due to COVID‐19 in the intensive care unit (ICU). Neurofilament light (NF‐L) is a marker of axonal damage and glial fibrillary acidic protein (GFAP) is a marker of astrocytic activation. Methods Patients with respiratory failure were prospectively enrolled from the ICU at Vancouver General Hospital. COVID‐19 patients were excluded if the diagnosis was an incidental secondary finding upon ICU admission or if their enrollment was >10 days after ICU admission. Control patients were excluded if their enrollment was >4 days after ICU admission or if their primary diagnosis was non‐respiratory. Plasma samples were collected upon admission study enrollment, with additional samples collected on day 7 and day 14 from COVID‐19 patients. Plasma NF‐L and GFAP were quantified using the Quanterix Simoa HD‐X analyzer. Group comparisons were performed using a Mann‐Whitney test. Trajectory analysis was performed using a Wilcoxon test or Friedman one‐way ANOVA. Area under receiver operating curve (AUROC) analysis was calculated to predict neurological complications and mortality during ICU stay. Results Of the 242 patients enrolled, 209 were confirmed positive for SARS‐CoV‐2 while 33 served as ICU controls. Median age was 61 years for the COVID‐19 group, 64 years for controls. Upon ICU admission, NF‐L was 32% lower and GFAP was 24% lower in those with COVID‐19 compared to controls after correcting for age. NF‐L concentrations increase by doubling each week of ICU stay, while GFAP remained stable. Over the course of their ICU stay, 16% COVID‐19 patients were diagnosed with a neurological complication and 17% died. Plasma NF‐L and GFAP demonstrated a moderate to strong ability to predict neurological complications (AUROC: NF‐L=0.702; GFAP=0.722) and mortality (AUROC: NF‐L=0.815; GFAP=0.809) during ICU stay. Conclusions Upon ICU admission, NF‐L and GFAP were lower in patients with COVID‐19 compared to controls. NF‐L, but not GFAP, increased over the course of ICU stay in patients with COVID‐19. Both markers were able to predict neurological complication or ICU mortality with moderate to strong accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".