Cross-sectional study of the risk of cognitive impairment associated with COVID-19 and a prognostic scale for psychopharmacotherapy selection in outpatient settings
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a widespread issue that affects patients’ quality of life and social functioning. Such impairments may result from neurological and psychiatric disorders, as well as from somatic diseases such as diabetes and hypertension, and from COVID-19 and post-acute COVID-19 syndrome. In recent years, increasing attention has been paid to the relationship between cognitive impairment and pharmacotherapy, particularly the use of antipsychotic medications, which may worsen cognitive deficits. However, data enabling prediction of such impairments, necessary for developing recommendations for pharmacotherapy adjustment based on cognitive-risk profiles, remain insufficient. AIM: This study aimed to identify quantitative associations between risk factors and the development of cognitive impairment in patients with COVID-19. METHODS: This cross-sectional study included patients from outpatient medical services in Saint Petersburg and the Leningrad Region who sought assistance for COVID-19 between December 2020 and May 2021. Inclusion required written informed consent and the ability to complete all required study procedures. Exclusion criteria were acute medical conditions, severe adverse effects of pharmacotherapy, and preexisting cognitive impairment. Current cognitive functioning was assessed using the Montreal Cognitive Assessment (MoCA) with a diagnostic threshold of 25 points. Predictors included sociodemographic data, medical history, and respiratory rate. RESULTS: The study involved 66 patients (38 women and 28 men). The median age was 39.5 years [35; 60], and 14% of participants demonstrated clinically significant cognitive impairment. Logistic regression analysis showed that lower educational attainment, older age, endocrine disorders, and a respiratory rate above 22 breaths per minute were associated with an increased risk of cognitive impairment. The model demonstrated a sensitivity of 98.4% and specificity of 90%. A scoring scale was developed to estimate risk without computing the logit function, thereby facilitating clinical usability. CONCLUSION: Social-demographic and clinical risk factors associated with cognitive impairment were identified in patients with COVID-19. The most significant predictors were lower educational attainment and older age, whereas endocrine comorbidity and elevated respiratory rate increased the likelihood of cognitive impairment. These findings may be considered when selecting psychopharmacotherapeutic strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".