Factors associated with treatment limitations in two Swedish intensive care units: Prevalence and patient involvement-Reference-Cited by-同舟云学术

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Factors associated with treatment limitations in two Swedish intensive care units: Prevalence and patient involvement

Published:2023-01-04 Issue:3 Volume:67 Page:339-346
ISSN:0001-5172
Container-title:Acta Anaesthesiologica Scandinavica
language:en
Short-container-title:Acta Anaesthesiol Scand

Author:

Jönsson Nino¹,Pettersson Niklas¹,Asplund Peter¹,Bremer Anders²³⁴^ORCID,Lehtipalo Stefan¹,Hessulf Fredrik¹⁵⁶

Affiliation:

1. Department of Anesthesiology and Intensive Care Medicine Halland Hospital Halmstad Halmstad Sweden

2. Faculty of Health and Life Sciences Linnaeus University Växjö Sweden

3. Centre of Interprofessional Collaboration within Emergency care (CICE) Linnaeus University Växjö Sweden

4. Department of Ambulance Service, Region Kalmar County Kalmar Sweden

5. Department of Anaesthesiology and Intensive Care Medicine Sahlgrenska University Hospital Mölndal Sweden

6. Department of Molecular and Clinical Medicine, Institution of Medicine, Sahlgrenska Academy University of Gothenburg Gothenburg Sweden

Funder

Research and Development

Publisher

Wiley

Subject

Anesthesiology and Pain Medicine,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/aas.14185

Reference20 articles.

1. Patient experiences in intensive care units: a systematic review

2. Global variability in withholding and withdrawal of life-sustaining treatment in the intensive care unit: a systematic review

3. Age, SAPS 3 and female sex are associated with decisions to withdraw or withhold intensive care

4. SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

5. Feature Selection with theBorutaPackage

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ICURE: Intensive care unit (ICU) risk evaluation for 30‐day mortality. Developing and evaluating a multivariable machine learning prediction model for patients admitted to the general ICU in Sweden;Acta Anaesthesiologica Scandinavica;2024-07-21

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