0300 A prospective follow-up study of Covid’s effects on Sleep and Cognition in post-acute period in a primarily minority population
Bibliographic record
Abstract
Abstract Introduction Long Covid, the post-acute sequelae of COVID-19, has evolved in recent months into a recognized spectrum of manifestations that persist after acute Covid-19 illness. Despite over 2 years of information since the start of the pandemic, data remains limited on the effect Long Covid may have on sleep and cognition. This study aims to assess the association between COVID-19 infection and sleep impairment in the post-acute period. Methods A total of 747 patients were identified from hospitalized COVID-19 patient records at Howard University Hospital between Feb 2020 and May 2021. 285 of these patients were interviewed by the research team 6-12 months after their initial infection. Sleep symptoms were collected via a questionnaire from 125 patients (64% African American, 34% Hispanic) to include the following four data points: difficulty falling asleep, worsened sleep quality, daytime somnolence (since COVID-19 occurrence) and the number of hours of sleep in 24 hours. In addition, a cognitive assessment was also conducted via a Mini-MOCA questionnaire(n=103 patients, 59% African American, 35% Hispanic). Of note, all sleep questionnaire participants did not report pre-COVID-19 sleep issues concerns. We looked for any associations between multiple clinical variables, sleep disturbance, and abnormal MOCA scores using proper statistical methods such as t-tests, chi-square tests, univariate and multivariable logistic regression. Results Out of the 285 respondents to the interview, 28 of 125 completing sleep assessments declared sleep disturbance symptoms, and 79 of 103 completing Mini-Moca assessments had an abnormal Mini-MOCA score. Patients with sleep disturbance symptoms were further analyzed and a significant association was identified between sleep disturbance and the following variables: increase in age (p=0.046), Diabetes Miletus diagnosis (p=0.009), elevated troponin level on admission (p< 0.05), ARB use during hospitalization (p=0.006), ICU admission during hospitalization (p< 0.05), and exposure to ECMO (p=0.011). Not surprisingly, sleep disturbance had a significant association with reduced total sleeping hours in 24 hr period (p< 0.0001). Conclusion Preliminary findings indicate the presence of sleep disturbance on longitudinal follow-up of patients previously infected with SARS-CoV-2. Additionally, we found a significant association between sleep disturbance in COVID-19 patients with many variables. Larger studies are required to further analyze these associations. Support (if any)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".