EFFECTIVE TREATMENT OF DELIRIUM IS DIFFICULT BUT NOT IMPOSSIBLE
Bibliographic record
Abstract
To the Editor: Aside from drug trials, there are few rigorous studies of treatment of full-blown delirium.1-5 We want to congratulate Marcantonio and colleagues for conducting such an ambitious trial in this difficult field.5 Their pragmatic, randomized trial was implemented in postacute skilled nursing facilities, and the intervention consisted of important elements to abate the consequences of delirium: recognition of delirium, assessment and correction of treatable causes, prevention of complications, and restoration of function. Still, the overall outcome of the intervention was disappointing. Although delirium was recognized more often in the intervention than in the control facilities, the intervention had no effect on persistence of delirium or on mortality. However, the important steps of delirium management were not fully executed, although the investigators made great effort to train the staff in the intervention facilities. Paradoxically, treating the causes of delirium seemed to succeed even worse in the intervention than in the control settings. We agree with Marcantonio and colleagues5 that prevention of delirium is currently more effective than treatment of full-blown delirium, but we disagree with the statement that no model would succeed in shortening the duration of delirium. There are several reasons to believe that the management of delirium has developed during the last few years. Canadian studies suggested that a geriatrician's consultation improved cognition of patients with delirium more rapidly than usual care,1 although hard end points such as institutionalizations or mortality were not affected.1, 2 A trial in Finland showed that comprehensive geriatric assessment and treatment can shorten the duration of delirium and improve patients' cognition and quality of life without increasing costs.3, 6 That trial did not affect mortality or institutionalizations either. A Swedish study showed shortened delirium duration and hospital stay and less mortality, although the good results may be due to the preventive nature of this particular study.4 Further development of interventions requires several dimensions. The treatment should be based on comprehensive geriatric assessment and a fully trained geriatric team, which has been shown to be effective in several contexts.7 Sole consultations without a trained geriatric team may be insufficient.1, 2 Despite well-trained geriatricians in the previous trial, the variations in geriatric teams implementing the intervention in different wards were wide, possibly diminishing effectiveness.3, 6 Both well-trained team and geriatrician may have been instrumental for the good results in the Swedish study. It may be that patients prone to delirium need a special ward with trained staff and possibilities for emergency actions, because the environment of ordinary emergency units with their noise and bustle may be especially detrimental. Development of valid triage tools is also needed to evaluate which of these patients benefit from active treatment and which should have palliative care. Many patients are frail, and recognition of their special requirements is important. These critically ill patients are often cared for in various settings where they have been admitted by chance. Delirium indicates a truly poor prognosis; one in three is deceased within 1 year.8 The important elements of treating delirium are those that Marcantonio and colleagues suggest,5 but basing the treatment of these demanding patients solely on nursing staff is not enough, because diagnostic and prognostic skills are vital for effective medical management of complicated syndromes such as delirium.9 Marcantonio's and previous studies3, 6 suffered from similar obstacles—constant staff turnover, understaffing, lack of facility leadership, and use of agency personnel—which can make the implementation of intervention for delirium frustrating. Conflict of Interest Disclosures: The authors report professional cooperation with various companies which, to our knowledge, contribute no conflict of interest relevant to this paper. Dr. Pitkälä reports having professional cooperation including lecturing fees from pharmaceutical and other healthcare companies (including Janssen-Cilag, Leiras, Lundbeck, MSD Finland, Novartis, Pfizer, Nestle) and having participated in clinical trials funded by pharmaceutical companies. Dr. Strandberg reports having professional cooperation including consulting and lecturing fees from pharmaceutical and other healthcare companies (including AstraZeneca, Boehringer Ingelheim, Leiras, MSD Finland, Novartis, Pfizer, and Servier) and having participated in clinical trials funded by pharmaceutical companies. Dr. Tilvis has received lecturing fees from AstraZeneca, Boehringer Ingelheim, Jansen-Cilag, Lundbeck, MSD Finland, Novartis, Orion Pharma, Pfizer, and Sanofi-Aventis. Dr. Laurila reports having received consulting or lecturing fees from Janssen-Cilag, Lundbeck, Mundipharma, MSD Finland, Pfizer, and Sanofi-Aventis. Author Contributions: This was a comment letter to a previous article in this journal. It does not include data. Drafting or critically revising the manuscript for important intellectual content: KHP, TES, RST, JVL. Approval of the final manuscript: KHP, TES, RST, JVL. KHP is the guarantor. Sponsor's Role: The Finnish delirium trial was supported by the Lions Organization (Punainen Sulka—Red Feather), Helsinki University Central Hospital, Helsinki City, and the Academy of Finland (Grant 48613). The sponsors did not have any role in the study design or analysis or interpretation of data, in writing the report, or in the decision to submit this article for publication. The authors were independent researchers not associated with the funders.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.017 | 0.042 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".