Observational studyBMJ open2018
How do palliative care doctors recognise imminently dying patients? A judgement analysis.
Observational study in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic evaluation in patients with advanced cancer in the last months of life: ESMO Clinical Practice Guideline.ESMO open · 2023Guideline
- Online training improves medical students' ability to recognise when a person is dying: The ORaClES randomised controlled trial.Palliative medicine · 2020Trial
- Recognising dying: Will artificial intelligence (AI) help improve clinical accuracy?Future healthcare journal · 2026Article
- Proposed intensive care unit triage models lack predictive validity for hospital mortality to deal with catastrophes: insights from a cohort study.Critical care science · 2026Article
- Early detection of ICU-acquired infections using high-frequency electronic health record data.BMC medical informatics and decision making · 2025Article
- How to talk about dying? The development of an evidence-based model for communication with patients in their last days of life and their family caregivers.PEC innovation · 2024Article
- Recognising dying in motor neurone disease: A scoping review.Palliative medicine · 2024Article
- Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system.NPJ digital medicine · 2024Article
- 'How long do you think?' Unresponsive dying patients in a specialist palliative care service: A consecutive cohort study.Palliative medicine · 2024Article
- Identifying cancer patients who received palliative care using the SPICT-LIS in medical records: a rule-based algorithm and text-mining technique.BMC palliative care · 2024Article
- Utilization of End-of-Life Care Rooms by Patients Who Died in a Single Hospice Unit at a National University Hospital in South Korea.Journal of hospice and palliative care · 2023Article
- Imminent death: clinician certainty and accuracy of prognostic predictions.BMJ supportive & palliative care · 2022Article
- Observational
- Specialist palliative medicine physicians and nurses accuracy at predicting imminent death (within 72 hours): a short report.BMJ supportive & palliative care · 2020Article
- Prognosticating for Adult Patients With Advanced Incurable Cancer: a Needed Oncologist Skill.Current treatment options in oncology · 2020Review
- The (un)availability of prognostic information in the last days of life: a prospective observational study.BMJ open · 2019Observational
- Prognostication in palliative care.Clinical medicine (London, England) · 2019Article
- An online international comparison of thresholds for triggering a negative response to the "Surprise Question": a study protocol.BMC palliative care · 2019Article
Corrections and comments
- Erratum issuedCorrection:2019
Authors and funding
11 authors.
Funding
Abstract
objectivesTo identify a group of palliative care doctors who perform well on a prognostic test and to understand how they make their survival predictions.
designProspective observational study and two cross-sectional online studies.
settingPhase I: an online prognostic test, developed from a prospective observational study of patients referred to palliative care. Phase II: an online judgement task consisting of 50 hypothetical vignettes.
participantsAll members of the Association of Palliative Medicine (APM) were eligible (n=~1100). 99 doctors completed the prognostic test and were included in the phase I analysis. The top 20% were invited to participate in phase II; 14/19 doctors completed the judgement task and were included in the phase II analysis. MEASURES: Phase I: participants were asked to give a probability of death within 72 hours (0%-100%) for all 20 cases. Accuracy on the prognostic test was measured with the Brier score which was used to identify the 'expert' group (scale range: 0 (expert)-1 (non-expert)). Phase II: participants gave a probability of death within 72 hours (0%-100%). A mixed model regression analysis was completed using the percentage estimate as the outcome and the patient information included in the vignettes as the predictors.
resultsThe mean Brier score of all participants was 0.237 (95% CI 0.235 to 0.239). The mean Brier score of the 'experts' was 0.184 (95% CI 0.176 to 0.192). Six of the seven prognostic variables included in the hypothetical vignettes were significantly associated with clinician predictions of death. The Palliative Performance Score was identified as being the most influential in the doctors' prognostic decision making (β=0.48, p<0.001).
conclusionsThis study identified six clinical signs and symptoms which influenced the judgement policies of palliative care doctors. These results may be used to teach novice doctors how to improve their prognostic skills.
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