Literature DB >> 34775181

The Edmonton Symptom Assessment System: A narrative review of a standardized symptom assessment tool in head and neck oncology.

Christopher W Noel1, David Forner2, Douglas B Chepeha3, Elif Baran4, Kelvin K W Chan5, Ambica Parmar5, Zain Husain6, Irene Karam6, Julie Hallet7, Natalie G Coburn8, Antoine Eskander9.   

Abstract

OBJECTIVE: Symptom burden is common in head and neck cancer patients though it frequently remains undetected and untreated. The Edmonton Symptom Assessment System - revised version (ESAS-r) is a generic symptom scale deployed in many outpatient settings worldwide. The ESAS-r is meant to improve symptom detection and management. We sought to review the ESAS-r and its psychometric properties in a head and neck oncology population.
METHODS: Narrative Review.
RESULTS: Over the past 30 years, the ESAS-r has emerged as one of the most used symptom scales for cancer patients. Its psychometric properties in a heterogenous cancer population are well supported, proving to be reliable and valid in a variety of settings. The linking of ESAS-r scores with Ontario administrative health data has led to a detailed assessment of validity in head and neck cancer. The ESAS-r can discriminate between high and low levels of symptom burden and is responsive to change over time in this patient population. ESAS-r scores have also been shown to be a strong predictor of future emergency department use and unplanned hospitalization in head and neck cancer patients.
CONCLUSIONS: The ESAS-r is reliable and valid in the head and neck cancer population and may serve as a useful clinical endpoint in research studies.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Dyspnea; Edmonton symptom assessment system; Fatigue; Head and neck cancer; Pain; Patient reported outcomes; Surveys and questionnaires; Symptom assessment

Mesh:

Year:  2021        PMID: 34775181     DOI: 10.1016/j.oraloncology.2021.105595

Source DB:  PubMed          Journal:  Oral Oncol        ISSN: 1368-8375            Impact factor:   5.337


  2 in total

1.  Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer.

Authors:  Christopher W Noel; Rinku Sutradhar; Lesley Gotlib Conn; David Forner; Wing C Chan; Rui Fu; Julie Hallet; Natalie G Coburn; Antoine Eskander
Journal:  JAMA Otolaryngol Head Neck Surg       Date:  2022-08-01       Impact factor: 8.961

2.  Enhancing Outpatient Symptom Management in Patients With Head and Neck Cancer: A Qualitative Analysis.

Authors:  Christopher W Noel; Yue Jennifer Du; Elif Baran; David Forner; Zain Husain; Kevin M Higgins; Irene Karam; Kelvin K W Chan; Julie Hallet; Frances Wright; Natalie G Coburn; Antoine Eskander; Lesley Gotlib Conn
Journal:  JAMA Otolaryngol Head Neck Surg       Date:  2022-04-01       Impact factor: 8.961

  2 in total

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