Advancing Delirium Treatment Trials in Older Adults: Recommendations for Future Trials From the Network for Investigation of Delirium: Unifying Scientists (NIDUS)
Bibliographic record
Abstract
OBJECTIVES: To summarize the delirium treatment trial literature, identify the unique challenges in delirium treatment trials, and formulate recommendations to address each in older adults. DESIGN: A 39-member interprofessional and international expert working group of clinicians (physicians, nurses, and pharmacists) and nonclinicians (biostatisticians, epidemiologists, and trial methodologists) was convened. Four expert panels were assembled to explore key subtopics (pharmacological/nonpharmacologic treatment, methodological challenges, and novel research designs). METHODS: To provide background and context, a review of delirium treatment randomized controlled trials (RCTs) published between 2003 and 2023 was conducted and evidence gaps were identified. The four panels addressed the identified subtopics. For each subtopic, research challenges were identified and recommendations to address each were proposed through virtual discussion before a live, full-day, and in-person conference. General agreement was reached for each proposed recommendation across the entire working group via moderated conference discussion. Recommendations were synthesized across panels and iteratively discussed through rounds of virtual meetings and draft reviews. RESULTS: We identified key evidence gaps through a systematic literature review, yielding 43 RCTs of delirium treatments. From this review, eight unique challenges for delirium treatment trials were identified, and recommendations to address each were made based on panel input. The recommendations start with design of interventions that consider the multifactorial nature of delirium, include both pharmacological and nonpharmacologic approaches, and target pathophysiologic pathways where possible. Selecting appropriate at-risk patients with moderate vulnerability to delirium may maximize effectiveness. Targeting patients with at least moderate delirium severity and duration will include those most likely to experience adverse outcomes. Delirium severity should be the primary outcome of choice; measurement of short- and long-term clinical outcomes will maximize clinical relevance. Finally, plans for handling informative censoring and missing data are key. CONCLUSIONS: By addressing key delirium treatment challenges and research gaps, our recommendations may serve as a roadmap for advancing delirium treatment research in older adults.
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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 | 0.571 | 0.684 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.017 | 0.018 |
| Research integrity | 0.035 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".