Literature DB >> 30025938

Terminally Ill Cancer Patients' Concordance Between Preferred Life-Sustaining Treatment States in Their Last Six Months of Life and Received Life-Sustaining Treatment States in Their Last Month: An Observational Study.

Fur-Hsing Wen1, Jen-Shi Chen2, Po-Jung Su2, Wen-Cheng Chang2, Chia-Hsun Hsieh2, Ming-Mo Hou2, Wen-Chi Chou2, Siew Tzuh Tang3.   

Abstract

CONTEXT/
OBJECTIVE: The extent to which patients' preferences for end-of-life (EOL) care are honored may be distorted if preferences are measured long before death, a common approach of existing research. We examined the concordance between cancer patients' states of life-sustaining treatments (LSTs) received in their last month and LST preference states assessed longitudinally over their last six months.
METHODS: We examined states of preferred and received LSTs (cardiopulmonary resuscitation, intensive care unit care, chest compression, intubation with mechanical ventilation, intravenous nutrition, and nasogastric tube feeding) in 271 cancer patients' last six months by a transition model with hidden Markov modeling (HMM). The extent of concordance was measured by a percentage and a kappa value.
RESULTS: HMM identified four LST preference states: life-sustaining preferring, comfort preferring, uncertain, and nutrition preferring. HMM identified four LST states received in patients' last month: generally received LSTs, LSTs uniformly withheld, selectively received LSTs, and received intravenous nutrition only. LSTs received concurred poorly with patients' preferences estimated right before death (39.5% and kappa value: 0.06 [95% CI: -0.02, 0.13]). Patients in the life-sustaining-preferring, uncertain, and nutrition-preferring states primarily received no LSTs, and patients in three of four states received intravenous nutrition against their preferences. Concordance was strongest for comfort-preferring patients.
CONCLUSIONS: Concordance was poor between patients' preferred and received LST states. Interventions are needed to clarify patients' EOL care goals and to facilitate their understanding about LST's ineffectiveness in prolonging life at EOL. Such interventions might increase patients' comfort preference and ensure concordance between their preferred and received EOL care.
Copyright © 2018 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Value concordance; agreement; cancer; end-of-life care; life-sustaining treatments; oncology; preferences

Mesh:

Year:  2018        PMID: 30025938     DOI: 10.1016/j.jpainsymman.2018.07.003

Source DB:  PubMed          Journal:  J Pain Symptom Manage        ISSN: 0885-3924            Impact factor:   3.612


  4 in total

1.  Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge cluster randomized controlled trial.

Authors:  Christopher R Manz; Ravi B Parikh; Chalanda N Evans; Corey Chivers; Susan H Regli; Justin E Bekelman; Dylan Small; Charles A L Rareshide; Nina O'Connor; Lynn M Schuchter; Lawrence N Shulman; Mitesh S Patel
Journal:  Contemp Clin Trials       Date:  2020-01-23       Impact factor: 2.226

2.  Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study.

Authors:  Ravi B Parikh; Christopher R Manz; Maria N Nelson; Chalanda N Evans; Susan H Regli; Nina O'Connor; Lynn M Schuchter; Lawrence N Shulman; Mitesh S Patel; Joanna Paladino; Judy A Shea
Journal:  Support Care Cancer       Date:  2022-01-30       Impact factor: 3.603

Review 3.  Conceptualizing and Counting Discretionary Utilization in the Final 100 Days of Life: A Scoping Review.

Authors:  Paul R Duberstein; Michael Chen; Michael Hoerger; Ronald M Epstein; Laura M Perry; Sule Yilmaz; Fahad Saeed; Supriya G Mohile; Sally A Norton
Journal:  J Pain Symptom Manage       Date:  2019-10-19       Impact factor: 3.612

4.  Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial.

Authors:  Samuel U Takvorian; Justin Bekelman; Rinad S Beidas; Robert Schnoll; Alicia B W Clifton; Tasnim Salam; Peter Gabriel; E Paul Wileyto; Callie A Scott; David A Asch; Alison M Buttenheim; Katharine A Rendle; Krisda Chaiyachati; Rachel C Shelton; Sue Ware; Corey Chivers; Lynn M Schuchter; Pallavi Kumar; Lawrence N Shulman; Nina O'Connor; Adina Lieberman; Kelly Zentgraf; Ravi B Parikh
Journal:  Implement Sci       Date:  2021-09-25       Impact factor: 7.327

  4 in total

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