ArticleBMC medical informatics and decision making2023
Contribution of information about acute and geriatric characteristics to decisions about life-sustaining treatment for old patients in intensive care.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 6 papers.
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.
The Very Old Intensive Care Patient: A Multinational Prospective Observation Study
Development and Validation of a Prognostic Score in the Very Old ICU Patients (≥80 Years)
Who cites it
6 citing papers in PubMed, 28 citations in OpenAlex.
- Prognosticating the outcome of intensive care in older patients-a narrative review.Annals of intensive care · 2024Review
- Critical care beyond organ support: the importance of geriatric rehabilitation.Annals of intensive care · 2024Review
- Long-stay ICU patients with frailty: mortality and recovery outcomes at 6 months.Annals of intensive care · 2024Article
- The trajectory of very old critically ill patients.Intensive care medicine · 2024Review
- Frailty's influence on 30-day mortality in old critically ill ICU patients: a bayesian analysis evaluating the clinical frailty scale.Annals of intensive care · 2023Article
- The role of clinical phenotypes in decisions to limit life-sustaining treatment for very old patients in the ICU.Annals of intensive care · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 8 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLife-sustaining treatment (LST) in the intensive care unit (ICU) is withheld or withdrawn when there is no reasonable expectation of beneficial outcome. This is especially relevant in old patients where further functional decline might be detrimental for the self-perceived quality of life. However, there still is substantial uncertainty involved in decisions about LST. We used the framework of information theory to assess that uncertainty by measuring information processed during decision-making.
methodsDatasets from two multicentre studies (VIP1, VIP2) with a total of 7488 ICU patients aged 80 years or older were analysed concerning the contribution of information about the acute illness, age, gender, frailty and other geriatric characteristics to decisions about LST. The role of these characteristics in the decision-making process was quantified by the entropy of likelihood distributions and the Kullback-Leibler divergence with regard to withholding or withdrawing decisions.
resultsDecisions to withhold or withdraw LST were made in 2186 and 1110 patients, respectively. Both in VIP1 and VIP2, information about the acute illness had the lowest entropy and largest Kullback-Leibler divergence with respect to decisions about withdrawing LST. Age, gender and geriatric characteristics contributed to that decision only to a smaller degree.
conclusionsInformation about the severity of the acute illness and, thereby, short-term prognosis dominated decisions about LST in old ICU patients. The smaller contribution of geriatric features suggests persistent uncertainty about the importance of functional outcome. There still remains a gap to fully explain decision-making about LST and further research involving contextual information is required.
trial registrationVIP1 study: NCT03134807 (1 May 2017), VIP2 study: NCT03370692 (12 December 2017).
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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.