Delirium and Inflammation in Older Adults Hospitalized for COVID-19: A Cohort Study
Bibliographic record
Abstract
PURPOSE: The occurrence and predictors of delirium in older adults hospitalized for coronavirus disease 2019 (COVID-19) have not been well described. Highlighting the association with inflammatory markers may be useful for identifying delirium. This study aimed to determine the prevalence and incidence of delirium and explore its association with the C-reactive protein (CRP). PATIENTS AND METHODS: This cohort study of adults aged 65 and older with a COVID-19 diagnosis took place at an academic healthcare institution between April and May 2020. COVID-19 was diagnosed by positive nasopharyngeal swab. Serum levels of CRP were collected as a marker of systemic inflammation. The primary outcome was the prevalence and incidence of delirium. Delirium was diagnosed primarily during a patient's stay in hospital based on the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5). To ensure that no delirium diagnosis was missed during hospital stay, clinical records were reviewed by clinicians with geriatric medicine training for retrospective diagnoses. RESULTS: A total of 127 patients aged 65 and older were hospitalized with a diagnosis of COVID-19. The median age was 82 years (IQR: 74-88), with 54 (43%) females. Overall, delirium was present in 62 (49%) patients: manifestations of delirium were present on the first day of hospitalization in 53 of these cases (86%), while 9 cases (14%) developed delirium during hospitalization. After controlling for age and sex, the mean CRP value over the first 3 days since arrival was associated with a higher risk of delirium (OR 1.35; 95% CI: 1.01-1.85) for every 50 mg/L increase. CONCLUSION: In this cohort of older adults hospitalized for COVID-19, delirium was highly prevalent. An early increase in CRP levels should raise suspicion about the occurrence of delirium and could improve its diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".