A Preliminary Validation of an Optimal Cutpoint in Total Number of Patient-Reported Symptoms in Head and Neck Cancer for Effective Alignment of Clinical Resources with Patients' Symptom Burden.
Cancer Care Res Online
; 4(1)2024 Jan.
Article
en En
| MEDLINE
| ID: mdl-38586274
ABSTRACT
Background:
Patients with head and neck cancer (HNC) often experience high symptom burden leading to lower quality of life (QoL).Objective:
This study aims to conceptually model optimal cutpoint by examining where total number of patient-reported symptoms exceeds patients' coping capacity, leading to a decline in QoL in patients with HNC.Methods:
Secondary data analysis of 105 individuals with HNC enrolled in a clinical usefulness study of the NYU Electronic Patient Visit Assessment (ePVA)©, a digital patient-reported symptom measure. Patients completed ePVA and European Organization for Research and Treatment of Cancer (EORTC©) QLQ-C30 v3.0. The total number of patient-reported symptoms was the sum of symptoms as identified by the ePVA questionnaire. Analysis of variance (ANOVA) was used to define optimal cutpoint.Results:
Study participants had a mean age of 61.5, were primarily male (67.6%), and had Stage IV HNC (53.3%). The cutpoint of 10 symptoms was associated with significant decline of QoL (F= 44.8, P<.0001), dividing the population into categories of low symptom burden (< 10 symptoms) and high symptom burden (≥ 10 symptoms). Analyses of EORTC© function subscales supported the validity of 10 symptoms as the optimal cutpoint (Physical F=28.3, P<.0001; Role F=21.6, P<.0001; Emotional F=9.5, P=.003; Social F=33.1, P<.0001).Conclusions:
In HNC, defining optimal cutpoints in the total number of patient-reported symptoms is feasible. Implications for Practice Cutpoints in the total number of patient-reported symptoms may identify patients experiencing a high symptom burden from HNC. Foundational Using optimal cutpoints of the total number of patient-reported symptoms may help effectively align clinical resources with patients' symptom burden.
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Base de datos:
MEDLINE
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En
Revista:
Cancer Care Res Online
Año:
2024
Tipo del documento:
Article