Long-Term Neurological Sequelae Among Severe COVID-19 Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Few studies have thoroughly evaluated the neuro-invasive effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which may contribute to a wide range of sequelae from mild long-term effects like headaches and fatigue to severe events like stroke and arrhythmias. Our study aimed to evaluate the long-term neurological effects of coronavirus disease 2019 (COVID-19) among patients discharged from the hospital. In this systematic review and meta-analysis, we assessed the long-term neurocognitive effects of COVID-19. Post-COVID-19 neurological sequelae were defined as persistent symptoms of headache, fatigue, myalgia, anosmia, dysgeusia, sleep disturbance, issues with concentration, post-traumatic stress disorder (PTSD), suicidality, and depression long after the acute phase of COVID-19. Data from observational studies describing post-COVID-19 neurocognitive sequelae and severity of COVID-19 from September 1, 2019, to the present were extracted following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol with a consensus of three independent reviewers. A systematic review was performed for qualitative evaluation and a meta-analysis was performed for quantitative analysis by calculating log odds of COVID-19 neurocognitive sequelae. The odds ratio (OR) and 95% confidence interval (CI) were obtained and forest plots were created using random effects models. We found seven studies, out of which three were used for quantitative synthesis of evidence. Of the 3,304 post-COVID-19 patients identified, 50.27% were male with a mean age of 56 years; 20.20% had post-COVID-19 symptoms more than two weeks after the acute phase of infection. Among persistence symptoms, neurocognitive symptoms like headache (27.8%), fatigue (26.7%), myalgia (23.14%), anosmia (22.8%), dysgeusia (12.1%), sleep disturbance (63.1%), confusion (32.6%), difficulty to concentrate (22%), and psychiatric symptoms like PTSD (31%), feeling depressed (20%), and suicidality (2%) had a higher prevalence. In meta-analysis, COVID-19 patients with severe symptoms had higher odds of headache (pooled OR: 4.53; 95% CI: 2.37-8.65; p<0.00001; I2: 0%) and myalgia (pooled OR: 3.36; 95% CI: 2.71-4.17; p<0.00001; I2: 0%). Anosmia, fatigue, and dysgeusia had higher but non-significant odds following COVID-19. Although we had sufficient data for headache and fatigue to identify higher rates and associations following COVID-19, we could not establish relationships with other post-COVID-19 neurocognitive séqueles. Long-term follow-up may mitigate the neurocognitive effects among COVID-19 patients as these symptoms are also associated with a poor quality of life.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.006 | 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".