Pharmacological treatment of hyperactive delirium in people with COVID-19: rethinking conventional approaches
Bibliographic record
Abstract
People with coronavirus disease (COVID-19) might have several risk factors for delirium, which could in turn notably worsen the prognosis. Although pharmacological approaches for delirium are debated, haloperidol and other first-generation antipsychotics are frequently employed, particularly for hyperactive presentations. However, the use of these conventional treatments could be limited in people with COVID-19, due to the underlying medical condition and the risk of drug-drug interactions with anti-COVID treatments. On these premises, we carried out a rapid review in order to identify possible alternative medications for this particular population. By searching PubMed and the Cochrane Library, we selected the most updated systematic reviews of randomised trials on the pharmacological treatment of delirium in both intensive and non-intensive care settings, and on the treatment of agitation related to acute psychosis or dementia. We identified medications performing significantly better than placebo or haloperidol as the reference treatment in each population considered, and assessed the strength of association according to validated criteria. In addition, we collected data on other relevant clinical elements (i.e. common adverse events, drug-drug interactions with COVID-19 medications, daily doses) and regulatory elements (i.e. therapeutic indications, contra-indications, available formulations). A total of 10 systematic reviews were included. Overall, relatively few medications showed benefits over placebo in the four selected populations. As compared with placebo, significant benefits emerged for quetiapine and dexmedetomidine in intensive care unit (ICU) settings, and for none of the medications in non-ICU settings. Considering also data from indirect populations (agitation related to acute psychosis or dementia), aripiprazole, quetiapine and risperidone showed a potential benefit in two or three different populations. Despite limitations related to the rapid review methodology and the use of data from indirect populations, the evidence retrieved can pragmatically support treatment choices of frontline practitioners involved in the COVID-19 outbreak, and indicate future research directions for the treatment of delirium in particularly vulnerable populations.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".