ArticleThe patient2020
Population Preferences for Treatments When Critically Ill: A Discrete Choice Experiment.
Article in The patient, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review.PharmacoEconomics · 2025Pooled it
- Behaviour artificial intelligence technology to support the decision-making process of continuation or withdrawal of life-sustaining therapy.Intensive care medicine experimental · 2026Article
- Intensive care doctors and nurses personal preferences for Intensive Care, as compared to the general population: a discrete choice experiment.Critical care (London, England) · 2021Observational
- Appropriateness of intensive care treatments near the end of life during the COVID-19 pandemic.Breathe (Sheffield, England) · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany patients in the intensive care unit are too unwell to participate in shared decision making or have not previously documented their wishes. In these situations, understanding the values of the general population could help doctors provide appropriate guidance to surrogate decision makers.
methodsUsing a discrete choice experiment design, we conducted an online survey using an Australian panel. Participants were asked about their willingness to accept treatments, faced with a variety of possible outcomes and probabilities (low, moderate or high). The outcomes were across four domains: loss of functional autonomy, pain, cognitive disability and degree of burden on others. Demographic details, prior experience of intensive care unit and current health conditions were also collected. Data were analysed using logistic regression, predicting whether respondents choose to continue active treatment or not.
resultsNine hundred and eighty-four respondents, representative of age and sex completed the web-based survey. With the increasing likelihood of negative post-intensive care unit sequelae, there was a higher probability of the respondent preferring to stop ongoing active treatment, with the largest coefficients being on caring assistance and the need for full-time residential care. Those who identified as very religious, were younger or who had children under 5 years of age were more likely to choose to continue active treatment.
conclusionsRespondents valued their independence as the most important factor in deciding whether to receive ongoing medical treatments in the intensive care unit. When clinicians are unable to obtain specific patient information, they should consider framing their decision making around the likelihood of the patient achieving functional independence rather than survival.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.