Excessive sedation as a risk factor for delirium: a comparison between two cohorts of critically-ill patients with and without COVID-19
Bibliographic record
Abstract
Abstract Background: Excessive sedation has been associated with poor outcome in critically-ill patients with acute respiratory Distress Syndrome (ARDS). The on-going pandemic has seen many critically-ill COVID-19 with ARDS, yet the incidence of excessive sedation and its association to delirium in these patients has to date not been assessed. We aimed at comparing the incidence and outcome of excessive sedation and delirium in two cohorts of critically-ill patients. Methods: This was an international, dual center retrospective analysis of prospectively collected data from two cohorts of critically ill patients, with and without COVID-19 disease, pertaining to two different hospital settings. Depth of sedation was monitored through processed EEG and delirium through the Confusion Assessment Method for the ICU(CAM-ICU). The main outcomes were the incidence of excessive sedation and delirium between the two cohorts, and secondary outcomes were length of ICU and hospital stay and mechanical ventilation duration.Results: Fifty-seven non-COVID-19 and 21 COVID-19 patients were included, 38(49%) of whom had ARDS. Twenty-seven(47.3%) non-COVID-19 and 11(52.3%) COVID-19 patients fulfilled the criteria for excessive sedation. Excessively sedated patients were older(p=0.034) and had delirium more frequently(p<0.001). There was a trend in excessive sedation in ARDS patients, while there was no correlation between excessive sedation and COVID-19 diagnosis. COVID-19 with ARDS was related to delirium at the limit of significance. On adjusted analysis excessive sedation was independently related to delirium(p=0.008). Patients with delirium had longer MV duration, ICU-LOS and H-LOS. In the adjusted analysis, delirium was an independent predictor of ICU-LOS(p=0.005) and MV duration(p=0.039). SAPS II was higher in the non-COVID-19 patients when compared to COVID-19 patients. Despite this, COVID-19 patients remained ventilated for a longer period of time, had a longer ICU and H-LOS. Conclusion: Besides age, excessive sedation might represent an important risk factor for delirium in COVID-19 and non-COVID-19 critically ill patients, which may lead to an increased ICU-LOS, H-LOS and MV duration. The use of continuous EEG-based monitoring for quantification of sedation depth, along with frequent delirium assessment in critically-ill COVID-19 patients is warranted along with larger prospective trials aimed at verifying weather the use of EEG-based monitoring leads to improved outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".