Hostname: page-component-74656f9884-rdgjk Total loading time: 0 Render date: 2026-11-07T03:20:38.370Z Has data issue: false hasContentIssue false
  • English
  • Français

Patients’ self-reported overall wellbeing correlates with concurrent reported symptoms: analysis of the Edmonton Symptom Assessment System

Published online by Cambridge University Press:  16 October 2023

Catherine B. McKenna ,
Ernest Osei Open the ORCID record for Ernest Osei [Opens in a new window] ,
Brooklynn Fleury ,
Stephanie Swanson ,
Christabel Oghinan  and
Johnson Darko
Catherine B. McKenna
Affiliation:
Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada Department of Medical Physics, Grand River Regional Cancer Centre, Kitchener, ON, Canada
Ernest Osei*
Affiliation:
Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada Department of Medical Physics, Grand River Regional Cancer Centre, Kitchener, ON, Canada Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
Brooklynn Fleury
Affiliation:
Department of Medical Physics, Grand River Regional Cancer Centre, Kitchener, ON, Canada Department of Psychology, Wilfrid Laurier University, Waterloo, ON, Canada
Stephanie Swanson
Affiliation:
Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada Department of Medical Physics, Grand River Regional Cancer Centre, Kitchener, ON, Canada
Christabel Oghinan
Affiliation:
Department of Medical Physics, Grand River Regional Cancer Centre, Kitchener, ON, Canada
Johnson Darko
Affiliation:
Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada Department of Medical Physics, Grand River Regional Cancer Centre, Kitchener, ON, Canada Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
*
Corresponding author: Ernest Osei; Email: ernest.osei@grhosp.on.ca
Rights & Permissions [Opens in a new window]

Abstract

Background:

The primary intent of cancer treatment is either curative, prolongation of patient life, or to improve patient quality of life; however, treatments are associated with various side effects that may impact patient wellbeing. Thus, understanding the patients’ wellbeing from the patient’s perspective is essential as it could help enable the provision of the necessary support for patients throughout their cancer journey.

Materials and Method:

We analysed Edmonton Symptom Assessment System (ESAS) questionnaire responses completed by 19,288 patients over 201,839 visits to our Cancer Centre. As part of their routine and standard of care, patients completing the questionnaire are asked to score 6 physical and 2 psychological symptoms as well as overall wellbeing using an 11-point numerical rating scale ranging from 0 to 10, where 0 means complete absence of the symptom or best overall wellbeing and 10 means worst possible symptom or worst overall wellbeing. We used the ESAS responses to characterise the relationship between the overall wellbeing score and concurrent symptoms scored by cancer patients.

Results:

Patients reported tiredness and nausea as the physical symptom causing the most and least distress respectively. Patients that reported severe (7–10) wellbeing also scored high mean scores for tiredness (6·2 ± 2·7), drowsiness (4·7 ± 3·1) and lack of appetite (4·4 ± 3·4). Univariate and multivariable logistic regression analysis suggests higher odds for patients to report moderate-to-severe (4–10) wellbeing when they report moderate-to-severe concurrent symptoms compared to none-to-mild concurrent symptoms.

Conclusions:

Our findings suggest that patients’ overall wellbeing as reported by the ESAS system is influenced by a number of concurrent symptoms. Tiredness was found to impact patients’ overall wellbeing to a greater extent than other concurrent symptoms. The sum of physical or psychological symptom scores was stronger indicators of a patient’s overall wellbeing compared to the scores of individual symptoms.

Keywords

cancer treatmentESASphysical symptomspsychological symptomswellbeing

Information

Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 22 , 2023 , e110
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Introduction

The primary intent of cancer treatment is either curative, prolongation of patient life, or to improve patient quality of life. However, cancer treatments are associated with diverse side effects that may impact patient quality of life and depending on the severity may burden the overall, physical and psychological wellbeing of patients. Moreover, the future of psycho-social oncology care is impacted by our ability to understand the importance of patients’ wellbeing and how it is affected by other clinical and psycho-social factors. According to Visser, Reference Visser, Garssen and Vingerhoets1 worsening physical symptoms can make it difficult for some cancer patients to find meaning in life, which may result in an increased likelihood for depression and poor wellbeing. Understanding patients’ wellbeing from the patient’s perspective is essential as it could help enable the provision of the necessary support for patients throughout their cancer journey. Consequently, several patient-reported outcome tools such as the Symptom Distress Scale (SDS), Reference Stapleton, Holden, Epstein and Wilkie2–Reference Yoon, Scarton, Duckworth, Yao, Ezenwa and Suarez4 the Hospital Anxiety and Depression Scale (HADS) Reference Park, Gelber, Rosenberg, Seah, Schapira and Come5–Reference Nipp, Fuchs, El-Jawahri, Mario, Troschel and Greer7 and the Edmonton Symptom Assessment Scale (ESAS) Reference Hui and Bruera8–Reference Bubis, Davis, Canaj, Gupta, Jeong and Barbera11 have become a valuable method for patients to effectively communicate the adverse effects they are experiencing and their overall wellbeing to their healthcare providers. The ESAS is used for self-assessment and evaluation of cancer symptom burden and captures information about nine common symptoms experienced by cancer patients including their overall wellbeing. It is a validated standardised patient-centred symptom assessment tool that is used to evaluate the severity of six physical and two psychological symptoms of distress as well as overall wellbeing related to cancer. Reference Hui and Bruera8,Reference Osei, McKenna, Darko, McKnight and Peters9,Reference Yokomichi, Morita, Nitto, Takahashi, Miyamoto and Nishie12–Reference Delgado-Guay, Parsons, Li, Palmer and Bruera42

The scoring of patient symptom burden using patient-reported outcomes is very subjective, and several studies have attempted to identify associations between the significance of the numerical symptom scores and various thresholds for intervention. Reference McKenzie, Zhang, Chan, Zaki, Razvi and Tsao16,Reference Selby, Cascella, Gardiner, Do, Moravan and Myers22,Reference Butt, Wagner, Beaumont, Paice, Peterman and Shevrin43 McKenzie et al. Reference McKenzie, Zhang, Chan, Zaki, Razvi and Tsao16 used the ESAS responses to characterise the relationship between dyspnoea (i.e., shortness of breath), and concurrent symptoms experienced by advanced cancer patients. Other studies Reference DeMiglio, Murdoch, Ivison, Fageria and Voutsadakis17,Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44–Reference Hamer, McDonald, Zhang, Verma, Leahey and Ecclestone49 have also investigated factors associated with wellbeing among cancer patients. A patient’s ESAS overall wellbeing score can be interpreted as their perceived state of overall health Reference Hui and Bruera8 and may be related to the experience and severity of specific symptoms. Reference DeMiglio, Murdoch, Ivison, Fageria and Voutsadakis17,Reference Cheung, Barmala, Zarinehbaf, Rodin, Le and Zimmermann33,Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44–Reference Abu-Helalah, Mustafa, Alshraideh, Alsuhail, Almousily and Al-Abdallah46,Reference Basch, Deal, Kris, Scher, Hudis and Sabbatini50 Several studies have reported that the physical (pain, tiredness, nausea, shortness of breath, drowsiness, lack of appetite) and psychological (depression and anxiety) symptoms put a burden on patients’ overall wellbeing and that higher physical and psychological symptom scores are associated with worse overall wellbeing. Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23,Reference Cheung, Le and Zimmermann32,Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44,Reference Subbiah, Charone, Roszik, Haider, Vidal and Wong51–Reference Whitford, Olver and Peterson53 According to Lien et al. Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23 , higher scores for lack of appetite, fatigue and drowsiness significantly correlate to worse overall wellbeing. Yennurajalingam et al. Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44 also observed that worse overall wellbeing scores are associated with higher scores for fatigue, anorexia and anxiety. Thus, the goal of this study is to analyse patients’ self-reported responses to the ESAS questionnaire and to investigate any correlation between self-reported ESAS overall wellbeing and the other concurrent symptoms experienced and reported on the ESAS questionnaire in our cancer patient population. This will ensure that clinicians, nurses, allied healthcare professionals, etc., are able to understand wellbeing from the patient’s perspective and thus be able to provide the necessary support for patients throughout their cancer journey.

Materials and Methods

In this single-centre retrospective study, we reviewed 201,839 ESAS responses completed by 19,288 cancer patients. Patients visiting the cancer centre complete the ESAS questionnaire as a routine component of their clinic visits to monitor patient-reported symptoms over the course of treatments and follow-ups. Completing the ESAS is considered part of the standard of care, which means that each patient during each out-patient treatment or clinic visit will complete the ESAS questionnaire as a basis for tailoring care. At each visit to the centre, patients are prompted to score the severity of two psychological symptoms (depression and anxiety), six physical symptoms (nausea, shortness of breath, lack of appetite, pain, drowsiness and tiredness) and their overall wellbeing on a numeric scale from 0 to 10, where 0 means complete absence of the symptom or best overall wellbeing and 10 means worst possible symptom or overall wellbeing. Patients’ responses to the ESAS questionnaires from April 1, 2014, to September 30, 2020, were electronically obtained from the OH-CCO database. Patients’ demographics consisting of age, gender, primary disease site, stage and clinical visit type were also collected. Symptom and wellbeing scores were stratified into four categories of no symptom or best wellbeing (0), mild (1–3), moderate (4–6) and severe symptom or worst wellbeing (7–10) consistent with other studies. Reference Hui and Bruera8,Reference Osei, McKenna, Darko, McKnight and Peters9,Reference Bubis, Davis, Canaj, Gupta, Jeong and Barbera11,Reference McKenzie, Zhang, Chan, Zaki, Razvi and Tsao16,Reference Dai, Beca, Guo, Isaranawatchai, Schwartz and Naipaul18–Reference Gill, Daines and Selby26,Reference Salvo, Zeng, Zhang, Leung, Khan and Presutti30,Reference Goyal, Riegert, Davuluri, Ong, Yi and Dougherty36,Reference Tran, Zomer, Chadder, Earle, Fung and Liu39,Reference Newcomb, Nipp, Waldman, Greer, Lage and Hochberg40,Reference Akgün, Krishnan, Feder, Tate, Kutner and Crothers54,Reference Barbera, Seow, Howell, Sutradhar, Earle and Liu55 We calculated the ESAS physical (ESAS-PHY, range: 0–60) symptom distress score by adding the individual scores from each of the six physical symptoms. Similarly, the individual scores from the two psychological symptoms were also summed to generate the ESAS psychological (ESAS-PSY, range: 0–20) symptom distress score. Furthermore, we used the concept of the scoring stratification of 0, 1–3, 4–6 and 7–10 to stratify the summated ESAS symptom scores into low (ESAS-PHY: 0–5, ESAS-PSY: 0–1), mild, (ESAS-PHY: 6–23, ESAS-PSY: 2–7), moderate (ESAS-PHY: 24–41, ESAS-PSY: 8–13) and severe (ESAS-PHY: 42–60, ESAS-PSY: 14–20), consistent with other studies. Reference Osei, McKenna, Darko, McKnight and Peters9,Reference Battaglia, Zerbinati, Piazza, Martino, Provenzano and Esposito13,Reference Dai, Beca, Guo, Isaranawatchai, Schwartz and Naipaul18,Reference Newcomb, Nipp, Waldman, Greer, Lage and Hochberg40,Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44

Statistical analysis

Statistical analyses were performed using the IBM SPSS Statistics v. 28.0. To ensure data completeness, we excluded 54,853 ESAS forms (1,471 patients) with incomplete patient responses or if patients entered scores of zero for all the symptoms. Descriptive statistics including means, standard deviations and frequencies were used to summarise patient-reported symptom prevalence and severity. Spearman correlation analyses were conducted among the nine ESAS symptoms to determine strength of correlation between overall wellbeing score and the other eight concurrent ESAS symptoms, with p-value<0·0001 considered statistically significant. The correlation between wellbeing and any concurrent symptoms is considered weak, moderate or strong when the Spearman correlation coefficients are <0·3, 0·3–0·5 or >0·5, respectively, consistent with Cheung et al. Reference Cheung, Le and Zimmermann32 classification of strength of correlations. Univariate and multivariable binary logistic regression analyses were conducted to determine the correlation between concurrent symptoms stratified as none-to-mild or moderate-to-severe and wellbeing stratified as none-to-mild or moderate-to-severe. We tested the multicollinearity between ESAS symptoms by evaluating whether their variance inflation factors were less than 5·0 as reported by Kim Reference Kim56 to ensure the correlation between symptoms is acceptable for statistical analysis and reported the odds ratios (OR) and 95% confidence intervals (CI), with p-value < 0·05 considered statistically significant. ORs greater than 1 indicate higher odds of reporting moderate-to-severe wellbeing when a concurrent symptom is scored at moderate-to-severe, compared to reporting none-to-mild for the concurrent symptom. The multivariable analysis was repeated with concurrent symptoms stratified as none, mild, moderate or severe and wellbeing stratified as none-to-mild or moderate-to-severe and also with wellbeing stratified as none-to-moderate or severe. Furthermore, the receiver operating characteristic (ROC) analysis was also used to identify symptoms that had high diagnostic ability for moderate-to-severe wellbeing score. We reported the area under the ROC curve (AUROC) for each symptom to measure the ability of the symptom to identify a moderate-to-severe score for wellbeing. AUROC < 0·5, 0·5 ≤ AUROC < 0·7, 0·7 ≤ AUROC < 0·9 and AUROC ≥ 0·9 indicate no, low, moderate and high diagnostic ability respectively, consistent with Akobeng. Reference Akobeng57 We conducted principal component analysis (PCA) with varimax rotation and an exploratory factor analysis (EFA) with varimax rotation on the symptoms to identify symptoms which cluster with wellbeing and also to examine inter-relationships among wellbeing and the concurrent symptoms. Components or factors are retained for analysis if their eigenvalue was >0·80 and accounted for ≥10% of the total variance, consistent with other studies. Reference McKenzie, Zhang, Zaki, Chan, Ganesh and Razvi15,Reference Cheung, Le and Zimmermann32,Reference Ganesh, Zhang, Chan, Wan, Drost and Tsao37,Reference Chow, Wan, Pidduck, Zhang, DeAngelis and Chan45,Reference Khan, Cramarossa, Chen, Nguyen, Zhang and Tsao58–Reference Cheung, Le, Gagliese and Zimmermann60 We present the loading scores for each component or factor of each symptom, and symptoms are considered part of a cluster if their loading scores are >0·60, consistent with other studies. Reference Cheung, Le and Zimmermann32,Reference Cheung, Le, Gagliese and Zimmermann60 We used Cronbach’s alpha coefficient to assess the internal consistency and reliability of the derived symptom clusters, where a higher value implies better consistency. Reference McKenzie, Zhang, Zaki, Chan, Ganesh and Razvi15,Reference Ganesh, Zhang, Chan, Wan, Drost and Tsao37,Reference Khan, Cramarossa, Chen, Nguyen, Zhang and Tsao58,Reference Chen, Nguyen, Cramarossa, Khan, Zhang and Tsao61

Results

Table 1 shows a summary of the statistical analysis of the symptom severity stratified by wellbeing severity scores of none (0), mild (1–3), moderate (4–6) and severe (7–10). Correlation between wellbeing and the concurrent ESAS symptoms was calculated via the Spearman rank correlation coefficient and is also shown in Table 1. The OR with 95% CI from the univariate and multivariable binary logistic regression analysis comparing wellbeing (none-to-mild or moderate-to-severe) and concurrent symptoms (none-to-mild or moderate-to-severe) are shown in Table 2. Similarly, the OR with 95% CI from the multivariable binary logistic regression analysis comparing wellbeing (none-to-mild or moderate-to-severe, and none-to-moderate or severe) and concurrent symptoms (none, mild, moderate, or severe) are shown in Table 3. Table 4 shows the AUROC with asymptotic 95% CI for reporting moderate-to-severe wellbeing scores for all cancer patients and Table 5 shows the results of the PCA and EFA clustering analyses. The distribution of symptom burden, including prevalence and severity of symptoms for the entire cohort of patients is shown as a stacked bar chart in Figure 1. Figure 2 shows boxplots of the OR with 95% CI from univariate and multivariable binary logistic regression analysis for reporting moderate-to-severe wellbeing when moderate-to-severe is reported for concurrent symptoms compared to reporting none-to-mild for the symptom. The ROC curves for reporting moderate-to-severe wellbeing scores for all patients and the biplots from PCA and EFA are shown in Figures 3 and 4 respectively.

Table 1.

A summary of patient-reported ESAS symptom severity for entire cohort of patients stratified by wellbeing severity (0, 1–3, 4–6 and 7–10) and the Spearman rank correlation coefficient. 0 indicates best possible wellbeing, and 10 indicates worst possible wellbeing. Spearman’s rank correlation coefficients <0·3, 0·3–0·5 and >0·5 indicate a weak, moderate and strong correlation, respectively (Cheung et al., 2009a). The p-values for all of the Spearman correlations are < 0·0001. ESAS-PHY: ESAS total physical symptom score; ESAS-PSY: ESAS total psychological symptom score

Table 2.

The odds ratios (OR) and 95% confidence intervals (CI) from univariate and multivariable binary logistic regression analysis for reporting moderate-to-severe wellbeing when a concurrent symptom is scored as moderate-to-severe compared to reporting none-to-mild for all patients. ORs > 1 indicate higher odds of reporting moderate-to-severe wellbeing when a concurrent symptom is reported as moderate-to-severe. Score of 0–3 (no symptom presence to mild) was used as the reference to calculate the OR for scoring the concurrent symptom as moderate-to-severe. ESAS symptom scores are stratified by none-to-mild (0–3) and moderate-to-severe (4–10). ESAS-PHY scores are stratified by low-to-mild (0–23) and moderate-to-severe (24–60). ESAS-PSY scores are stratified by low-to-mild (0–7) and moderate-to-severe (8–20)

Table 3.

The odds ratios (OR) and 95% confidence intervals (CI) from the multivariable binary logistic regression analysis for reporting moderate-to-severe wellbeing or severe wellbeing when a concurrent symptom is scored as mild, moderate or severe compared to reporting none for all patients. ORs > 1 represent higher odds of reporting moderate-to-severe or severe wellbeing. ORs = 1 represents no effect on the odds of reporting moderate-to-severe or severe wellbeing. Score of 0 (no symptom presence) was used as the reference to calculate the OR for scoring the concurrent symptom as mild, moderate or severe. ESAS symptom scores are stratified by none (0), mild (1–3), moderate (4–6) and severe (7–10). ESAS-PHY scores are stratified by low (0–5), mild (6–23), moderate (24–41) and severe (42–60). ESAS-PSY scores are stratified by low (0–1), mild (2–7), moderate (8–13) and severe (14–20)

* Non-significant (p-value > 0·05).

Table 4.

The area under the ROC (AUROC) curve with asymptomatic 95% confidence interval (CI) for detection of moderate-to-severe wellbeing for all cancer patients. AUROC < 0·5, 0·5 ≤ AUROC < 0·7, 0·7 ≤ AUROC < 0·9, and AUROC ≥ 0·9 indicate no diagnostic ability, low diagnostic ability, moderate diagnostic ability and high diagnostic ability respectively consistent with Akobeng (2006)

Table 5.

Results from the principal component analysis (PCA) and exploratory factor analysis (EFA) with varimax rotation. Factor loadings and final communality from the PCA and EFA of ESAS symptoms are also shown. Components or factors are retained for analysis if their eigenvalue > 0·80 and accounted for ≥10% of the total variance. Symptoms are considered part of a cluster if their loading scores are >0·60

* Symptoms in the same cluster based on highest loading factor of > 0·6.

† Symptoms in the same cluster based on highest loading factor of > 0·6.

‡ Symptoms in the same cluster based on highest loading factor of > 0·6.

⁑ Symptoms in the same cluster based on highest loading factor of > 0·6.

Figure 1.

Stacked bar chart of prevalence and severity of ESAS symptoms, ESAS-PHY and ESAS-PSY from patient responses. ESAS symptom scores are stratified as none (0), mild (1–3), moderate (4–6) and severe (7–10). ESAS-PHY scores are stratified as low (0–5), mild (6–23), moderate (24–41) and severe (42–60). ESAS-PSY scores are stratified as low (0–1), mild (2–7), moderate (8–13) and severe (14–20)

Figure 2.

Boxplots of odds ratios (OR) and 95% confidence interval (CI) from the (a) univariate and (b) multivariable binary logistic regression analysis for reporting moderate-to-severe wellbeing when a concurrent symptom is reported as moderate-to-severe compared to reporting none-to-mild for concurrent symptom

Figure 3.

ROC curves for the detection of moderate-to-severe wellbeing (4–10) score for all cancer patients stratified into (a) all ESAS symptoms, (b) ESAS physical symptom and (c) ESAS psychological symptom

Figure 4.

Biplots from (a) principal component analysis (PCA) with varimax rotation and (b) exploratory factor analysis (EFA) with varimax rotation to identify symptoms which cluster with wellbeing. A higher correlation between symptoms is represented by lines that are longer and closer together. Symptoms are considered part of a cluster if their loading score > 0·60, as indicated by the dotted grey line

Discussion

The systematic collection of patients’ self-reported symptoms using standardised patient-reported outcome questionnaires such as the ESAS is considered an effective patient-centred care to improve symptom control and patient overall wellbeing. However, managing symptoms can represent a challenge to healthcare professionals as they may result from various factors. Lien et al. Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23 reported that tiredness in cancer patients may be a result of the cancer itself, the treatments, or medications used to control other symptoms (such as pain). Thus, there is a fine balance between controlling symptoms causing distress and introducing new medications that introduce or aggravate others. According to Delgado-Guay et al. Reference Delgado-Guay, Parsons, Li, Palmer and Bruera42 , the presence of psychological symptoms, such as depression, may further complicate symptoms assessments as they have been shown to aggravate physical symptoms. Furthermore, the scoring of patient symptom burden using patient-reported outcomes is very subjective, and a patient’s ESAS overall wellbeing score can be interpreted as their perceived state of overall health or could be related to the experience and severity of specific symptoms. However, by identifying the most significant symptoms contributing to patient wellbeing, symptom management strategies can be developed and optimised to address the pressing needs of patients to improve their overall wellbeing. In this study, we used the patients’ ESAS responses to characterise the relationship between patient-reported overall wellbeing scores and concurrent symptoms experienced by cancer patients.

Physical symptoms correlate with wellbeing

In this study, the physical symptom reported as causing most distress to patients is tiredness and the least distressful symptom is nausea (Figures 1 and 2, Table 1). Other physical symptoms such as drowsiness, lack of appetite, pain and shortness of breath are also reported to have a greater impact on patient overall wellbeing (Figures 1 and 2, Table 1). These observations are consistent with similar studies that have been reported in the literature. Reference Osei, McKenna, Darko, McKnight and Peters9,Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44,Reference Yennurajalingam, Palmer, Zhang, Poulter and Bruera62–Reference Goodrose-Flores, Bonn, Klasson, Helde Frankling, Trolle Lagerros and Björkhem-Bergman64 Yennurajalingam et al. Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44 used the ESAS patient-reported outcome questionnaire to investigate factors associated with a feeling of wellbeing among 826 advanced lung or non-colonic gastrointestinal cancer patients and observed that patient-reported wellbeing was significantly associated with tiredness. Wilding et al. Reference Wilding, Downing, Wright, Selby, Watson and Wagland63 also investigated the associations between cancer-related symptoms, health-related quality of life and poor psychological outcomes in 13,097 prostate cancer patients treated with androgen deprivation therapy (ADT) and reported that clinically significant tiredness was strongly associated with poor wellbeing in patients.

Measures of central tendency and spearman correlation analysis

The ESAS responses show that patients reporting severe (7–10) wellbeing also scored high mean scores for tiredness (6·2 ± 2·7), drowsiness (4·7 ± 3·1) and lack of appetite (4·4 ± 3·4) (Table 1) as the symptoms causing most impact on their wellbeing. Analysis of the Spearman correlation indicated that all the physical symptoms are significantly (p-value < 0·0001) positively correlated to poor wellbeing, indicative of worsening wellbeing as concurrent symptom severity increases. The Spearman correlation data show that the physical symptoms strongly correlated to patient overall wellbeing are tiredness (0·62) and drowsiness (0·51). We also observed that the ESAS-PHY (0·68) is very strongly correlated with the overall wellbeing (Table 1), suggesting that using the summated ESAS physical symptoms (ESAS-PHY) may be a better indicator of a patient’s overall wellbeing compared to using the individual physical symptoms scores. Several studies Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23,Reference Cheung, Le and Zimmermann32,Reference Ganesh, Zhang, Chan, Wan, Drost and Tsao37,Reference Chow, Wan, Pidduck, Zhang, DeAngelis and Chan45,Reference Chow, Fan, Hadi and Filipczak59 that have investigated factors most predictive of patient wellbeing using the ESAS responses have also reported lack of appetite, tiredness and drowsiness as the symptoms having the strongest correlations to patients’ wellbeing.

Univariate and multivariable analysis

The analysis of the univariate and multivariable binary logistic regression models show OR > 1 (Figure 2, Table 2), indicative of higher odds for patients reporting moderate-to-severe wellbeing when they report moderate-to-severe in concurrent symptoms. When we re-analyse the univariate and multivariable models to determine the odds of reporting severe or moderate-to-severe wellbeing when patients report mild, moderate or severe for concurrent symptoms compared to patients reporting no symptoms, we still found OR > 1 for almost all the symptoms (Table 3). The data show a significant (p-value<0·05) relationship between reporting moderate-to-severe (4–10) wellbeing and moderate-to-severe scores in the ESAS concurrent symptoms (Figure 2, Table 2). In the multivariable model, the most predictive physical symptoms are tiredness (OR = 3·63), pain (OR = 2·87) and lack of appetite (OR = 2·87) (Figure 2, Table 2). Thus, patients reporting ESAS scores ≥4 for tiredness, pain or lack of appetite are more likely to report moderate-to-severe wellbeing. Consequently, the severity (i.e., worse distress) of physical symptoms is indicative of the severity of a patient’s overall wellbeing (i.e., worse wellbeing), and the three strongest predictors of severe wellbeing are tiredness, pain and lack of appetite. These observations are consistent with other similar studies that have been reported in the literature. Reference Osei, McKenna, Darko, McKnight and Peters9,Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23,Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44,Reference Chow, Wan, Pidduck, Zhang, DeAngelis and Chan45,Reference Yennurajalingam, Palmer, Zhang, Poulter and Bruera62–Reference Goodrose-Flores, Bonn, Klasson, Helde Frankling, Trolle Lagerros and Björkhem-Bergman64

Receiver operating characteristics (ROC) analysis

Results from the ROC analysis show the AUROC ranging from 0·644 for nausea to 0·833 for tiredness. The ESAS-PHY had a greater AUROC of 0·862 indicating that the ESAS-PHY may be better at predicting moderate-to-severe wellbeing than using the individual ESAS symptoms scores. Thus, suggesting that the ESAS-PHY could potentially be used to identify patients that are experiencing moderate-to-severe wellbeing. According to Akobeng, Reference Akobeng57 AUROC < 0·5, 0·5 ≤ AUROC < 0·7, 0·7 ≤ AUROC < 0·9 and AUROC ≥ 0·9 indicate no, low, moderate and high diagnostic ability, respectively. Thus, the symptoms with relatively higher predictive power for moderate-to-severe wellbeing are tiredness, drowsiness, lack of appetite and pain. These results suggest that the ESAS nausea score is a poor indicator of patients scoring moderate-to-severe wellbeing and tiredness is an excellent indicator of moderate-to-severe wellbeing consistent with other studies. Reference Osei, McKenna, Darko, McKnight and Peters9,Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23,Reference Yennurajalingam, Kim, Zhang, Park, Arthur and Chisholm44,Reference Chow, Wan, Pidduck, Zhang, DeAngelis and Chan45,Reference Yennurajalingam, Palmer, Zhang, Poulter and Bruera62–Reference Goodrose-Flores, Bonn, Klasson, Helde Frankling, Trolle Lagerros and Björkhem-Bergman64

Psychological symptoms correlate with wellbeing

Psychological symptoms, such as depression and anxiety, are commonly reported in cancer patients, especially those in advanced stages. In this study, we observed that patients reported depression as causing less distress compared with most of the physical symptoms, yet it was still a highly predictive factor of wellbeing, suggesting that even low depression scores as reported by patients in ESAS contribute significantly to overall wellbeing. According to Delgado-Guay et al. Reference Delgado-Guay, Parsons, Li, Palmer and Bruera42 , depressive symptoms can lead to general worsening of wellbeing for patients.

Measures of central tendency and spearman correlation analysis

Analysis of the ESAS responses shows that patients reporting severe (7–10) wellbeing also scored high mean scores for anxiety (4·7 ± 3·3) (Table 1) as the psychological symptoms causing most impact on their wellbeing. Results of the Spearman correlation show that both anxiety and depression are significantly (p-value < 0·0001) correlated to wellbeing, suggesting worsening patient wellbeing as psychological symptom severity increases. However, the Spearman correlation analysis shows that the psychological symptom most strongly correlated to wellbeing is depression (0·52), and anxiety is moderately correlated (0·47) to wellbeing. Similar to the summated ESAS physical symptoms score, the summated ESAS psychological (ESAS-PSY) score (0·53) is shown to be better correlated with wellbeing (Table 1), than the individual depression or anxiety scores, and therefore may be a better predictor of a patient’s overall wellbeing compared to using the individual psychological symptoms scores. Our results are in agreement with other studies Reference DeMiglio, Murdoch, Ivison, Fageria and Voutsadakis17,Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23,Reference Cheung, Le and Zimmermann32,Reference Ganesh, Zhang, Chan, Wan, Drost and Tsao37,Reference Chow, Wan, Pidduck, Zhang, DeAngelis and Chan45,Reference Chow, Fan, Hadi and Filipczak59 that have studied the psychological symptoms most predictive of patient wellbeing and have reported depression followed by anxiety.

Univariate and multivariable analysis

Results from the univariate and multivariable binary logistic regression analysis suggest a highly significant relationship between moderate-to-severe wellbeing score and moderate-to-severe score in the psychological symptoms (Figure 2, Table 2). Both the univariate and the multivariable models show that the most predictive psychological symptom for moderate-to-severe wellbeing is depression followed by anxiety. Thus, patients reporting moderate-to-severe for depression (OR = 2·90) and anxiety (OR = 2·68) are at higher odds for reporting moderate-to-severe overall wellbeing compared to patients who reported those symptoms as none-to-mild. Consequently, the severity of the psychological symptoms is predictive of the severity of a patient’s overall wellbeing. These observations are consistent with similar studies and have been reported in the literature. Reference DeMiglio, Murdoch, Ivison, Fageria and Voutsadakis17,Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23,Reference Salvo, Zeng, Zhang, Leung, Khan and Presutti30,Reference Cheung, Le and Zimmermann32,Reference Ganesh, Zhang, Chan, Wan, Drost and Tsao37,Reference Chow, Wan, Pidduck, Zhang, DeAngelis and Chan45,Reference Chow, Fan, Hadi and Filipczak59

Receiver operating characteristics (ROC) analysis

Results from the ROC analysis show the AUROC for depression, anxiety and ESAS-PSY are 0·750, 0·741 and 0·771, respectively. Thus, depression is a stronger predictor of moderate-to-severe wellbeing compared to anxiety, although the ESAS-PSY with a higher AUROC value may be a better predictor of patient moderate-to-severe wellbeing.

Symptom clusters associated with wellbeing

We also conducted PCA with varimax rotation and EFA with varimax rotation on the symptoms to identify symptoms which cluster with wellbeing and also to examine whether any inter-relationships existed among wellbeing and the concurrent symptoms. The PCA method identified two clusters: the first being nausea, shortness of breath, lack of appetite, drowsiness, pain, tiredness and wellbeing which accounted for 38% of the total variance and the second cluster being depression and anxiety accounting for 24% of the total variance (Figure 4, Table 5). However, the EFA identified tiredness, drowsiness, and wellbeing as one cluster that accounted for 33% of the total variance and a second cluster composed of depression and anxiety which accounted for 20% of the total variance (Table 5). The EFA method did not identify nausea, shortness of breath, lack of appetite and pain as a cluster based on a loading factor threshold of <0·6. The PCA data revealed that wellbeing clustered with all the physical symptoms, whereas the EFA data show that wellbeing clustered mainly with tiredness and drowsiness, consistent with our other analyses in this study (Figure 4, Table 5). The internal reliabilities of the clusters using Cronbach’s alpha coefficient are all acceptable and range from 0·83 to 0·86 (Table 5). These observations are consistent with similar studies and have been reported in the literature. Reference DeMiglio, Murdoch, Ivison, Fageria and Voutsadakis17,Reference Lien, Zeng, Zhang, Nguyen, Di Giovanni and Popovic23,Reference Salvo, Zeng, Zhang, Leung, Khan and Presutti30,Reference Cheung, Le and Zimmermann32,Reference Ganesh, Zhang, Chan, Wan, Drost and Tsao37,Reference Chow, Wan, Pidduck, Zhang, DeAngelis and Chan45,Reference Chow, Fan, Hadi and Filipczak59

Strengths and limitations

Our study had several strengths pertaining to the large number of patients to improve its generalisability with balanced proportions between male and female patients, and a wide range of patients’ ages. Furthermore, the data included patients from various primary disease sites and visits from differing clinical care settings. Moreover, the ESAS is a well-validated patient-reporting tool which has been used successfully in several oncologic settings, and in situations where patients may be computer-inexperienced, volunteers were available to assist in completion of the questionnaire. Our study however had some limitations, as the ESAS responses are a generic assessment and not symptom-specific, and the symptoms are only quantitatively evaluated. The symptom severities in this study were evaluated based on ESAS intensity scores alone, and differences in symptom reporting may not necessarily reflect the true differences in the symptom being experienced by the patient. ESAS completion is also voluntary, so some patients may be more likely to complete it than others depending on factors such as health literacy or ability to self-manage symptoms.

Conclusion

The ESAS symptom assessment scale provides meaningful and clinically useful information regarding symptoms and the overall wellbeing of patients. Our study suggests that patients’ overall wellbeing as reported by the ESAS assessment questionnaire is influenced by a number of concurrent symptoms. The physical symptoms observed to have the most impact on patients’ overall wellbeing are tiredness, pain, drowsiness and lack of appetite, and the symptom which had the least impact on wellbeing is nausea. Our data also suggest that the ESAS-PHY and the ESAS-PSY sums are very strongly correlated with overall patient’s wellbeing, an indication that using the summated ESAS physical and psychological symptoms scores may be better predictors of a patient’s overall wellbeing compared to using the individual symptoms scores. Identifying the most important symptoms affecting patients’ overall wellbeing is very important in cancer care and will enable clinicians and healthcare providers to develop and provide appropriate symptom-directed patient-focused interventions to improve patient’s wellbeing. In our clinical practice, the patient-reported ESAS response is used for symptom screening and monitoring, and the results are reviewed by clinicians to establish baselines in patients’ symptoms and/or to trigger further in-depth patient assessments. The assessments may lead to an intervention at the clinic level or may result in a referral to the appropriate services such as pain and symptom management, psychiatry, registered dietitian, social worker or spiritual care.

Acknowledgements

The authors would like to acknowledge with much gratitude the support from OH-CCO staff for supplying the data. We also would like to express our gratitude to the Medical Physics staff at Grand River Cancer Centre for their support for the students during this study.

Financial support

None.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics approval

This study was approved by the Tri-Hospital Research Ethics Board.

References

Visser, A, Garssen, B, Vingerhoets, A. Spirituality and well-being in cancer patients: a review. Psycho-Oncology. 2010; 19(6): 565–572.CrossRefGoogle ScholarPubMed
Stapleton, S, Holden, J, Epstein, J, Wilkie, D. A systematic review of the symptom distress scale in advanced cancer studies. Cancer Nurs. 2016; 39(4): E9–E23.CrossRefGoogle ScholarPubMed
Hong, F, Blonquist, T M, Halpenny, B, Berry, D L. Patient-reported symptom distress, and most bothersome issues, before and during cancer treatment. Patient Relat Outcome Meas. 2016; 7: 127–135.CrossRefGoogle ScholarPubMed
Yoon, S L, Scarton, L, Duckworth, L, Yao, Y, Ezenwa, M O, Suarez, M L, et al. Pain, symptom distress, and pain barriers by age among patients with cancer receiving hospice care: comparison of baseline data. J Geriatr Oncol. 2021; 12(7): 1068–1075.CrossRefGoogle ScholarPubMed
Park, E M, Gelber, S, Rosenberg, S M, Seah, D S E, Schapira, L, Come, S E, et al. Anxiety and depression in young women with metastatic breast cancer: a cross-sectional study. Psychosomatics. 2018; 59(3): 251–258.CrossRefGoogle ScholarPubMed
Krebber, A M H, Buffart, L M, Kleijn, G, Riepma, I C, de Bree, R, Leemans, C R, et al. Prevalence of depression in cancer patients: a meta-analysis of diagnostic interviews and self-report instruments. Psycho-Oncology. 2014; 23(2): 121–130.CrossRefGoogle ScholarPubMed
Nipp, R D, Fuchs, G, El-Jawahri, A, Mario, J, Troschel, F M, Greer, J A, et al. Sarcopenia is associated with quality of life and depression in patients with advanced cancer. Oncologist. 2018; 23(1): 97–104.CrossRefGoogle ScholarPubMed
Hui, D, Bruera, E. The Edmonton symptom assessment system 25 years later: past, present, and future developments. J Pain Symptom Manage. 2017; 53(3): 630–643.CrossRefGoogle ScholarPubMed
Osei, E, McKenna, C B, Darko, J, McKnight, K, Peters, C. Systematic self-reporting of patients’ symptoms: improving oncologic care and patients’ satisfaction. J Radiother Pract. 2023; 22: e55.CrossRefGoogle Scholar
Barbera, L, Sutradhar, R, Seow, H, Mittmann, N, Howell, D, Earle, C C, et al. The impact of routine Edmonton symptom assessment system (ESAS) use on overall survival in cancer patients: results of a population-based retrospective matched cohort analysis. Cancer Med. 2020; 9(19): 7107–7115.CrossRefGoogle ScholarPubMed
Bubis, L D, Davis, L E, Canaj, H, Gupta, V, Jeong, Y, Barbera, L, et al. Patient-reported symptom severity among 22,650 cancer outpatients in the last six months of life. J Pain Symptom Manage. 2020; 59(1): 58.e4–66.e4.CrossRefGoogle Scholar
Yokomichi, N, Morita, T, Nitto, A, Takahashi, N, Miyamoto, S, Nishie, H, et al. Validation of the Japanese version of the Edmonton symptom assessment system–revised. J Pain Symptom Manage. 2015; 50(5): 718–723.CrossRefGoogle ScholarPubMed
Battaglia, Y, Zerbinati, L, Piazza, G, Martino, E, Provenzano, M, Esposito, P, et al. Screening performance of Edmonton symptom assessment system in kidney transplant recipients. J Clin Med. 2020; 9(4): 995.CrossRefGoogle ScholarPubMed
Ripamonti, C I, Bandieri, E, Pessi, M A, Maruelli, A, Buonaccorso, L, Miccinesi, G. The Edmonton symptom assessment system (ESAS) as a screening tool for depression and anxiety in non-advanced patients with solid or haematological malignancies on cure or follow-up. Support Care Cancer. 2014; 22(3): 783–793.CrossRefGoogle ScholarPubMed
McKenzie, E, Zhang, L, Zaki, P, Chan, S, Ganesh, V, Razvi, Y, et al. Re-analysis of symptom clusters in advanced cancer patients attending a palliative outpatient radiotherapy clinic. Ann Palliat Med. 2019; 8(2): 140–149.CrossRefGoogle ScholarPubMed
McKenzie, E, Zhang, L, Chan, S, Zaki, P, Razvi, Y, Tsao, M, et al. Symptom correlates of dyspnea in advanced cancer patients using the Edmonton symptom assessment system. Support Care Cancer. 2020; 28(1): 87–98.CrossRefGoogle ScholarPubMed
DeMiglio, L, Murdoch, V, Ivison, J, Fageria, S, Voutsadakis, I A. Factors influencing psychological wellbeing of early breast cancer patients. Rep Pract Oncol Radiother. 2020; 25(6): 913–918.CrossRefGoogle ScholarPubMed
Dai, W F, Beca, J, Guo, H, Isaranawatchai, W, Schwartz, D, Naipaul, R, et al. Are population-based patient-reported outcomes associated with overall survival in patients with advanced pancreatic cancer? Cancer Med. 2020; 9(1): 215–224.CrossRefGoogle ScholarPubMed
Cuthbert, C A, Boyne, D J, Yuan, X, Hemmelgarn, BR, Cheung, W Y. Patient-reported symptom burden and supportive care needs at cancer diagnosis: a retrospective cohort study. Support Care Cancer. 2020; 28(12): 5889–5899.CrossRefGoogle ScholarPubMed
Milton, L, Behroozian, T, Li, N, Zhang, L, Lou, J, Karam, I, et al. Symptom burden associated with radiation dermatitis in breast cancer patients undergoing radiotherapy. Clin Breast Cancer. 2022; 22(3): e387–e398.CrossRefGoogle ScholarPubMed
Milton, L, Behroozian, T, Coburn, N, Trudeau, M, Razvi, Y, McKenzie, E, et al. Prediction of breast cancer–related outcomes with the Edmonton symptom assessment scale: a literature review. Support Care Cancer. 2021; 29(2): 595–603.CrossRefGoogle ScholarPubMed
Selby, D, Cascella, A, Gardiner, K, Do, R, Moravan, V, Myers, J, et al. A single set of numerical cutpoints to define moderate and severe symptoms for the Edmonton symptom assessment system. J Pain Symptom Manage. 2010; 39(2): 241–249.CrossRefGoogle ScholarPubMed
Lien, K, Zeng, L, Zhang, L, Nguyen, J, Di Giovanni, J, Popovic, M, et al. Predictive factors for well-being in advanced cancer patients referred for palliative radiotherapy. Clin Oncol. 2012; 24(6): 443–451.CrossRefGoogle ScholarPubMed
Seow, H, Sussman, J, Martelli-Reid, L, Pond, G, Bainbridge, D. Do high symptom scores trigger clinical actions? An audit after implementing electronic symptom screening. J Oncol Pract. 2012; 8(6): e142–e148.CrossRefGoogle ScholarPubMed
Gupta, S, Li, Q, Nathan, P C, D’Agostino, N, Baxter, N N, Fox, C, et al. Prevalence, severity, and predictors of symptom burden among adolescents and young adults with cancer. Cancer Med. 2023; 12(10): 11773–11785.CrossRefGoogle ScholarPubMed
Gill, A, Daines, P, Selby, D. What do symptom scores mean: observations on discrepancies when defining symptoms using words and numbers. Eur J Oncol Nurs. 2010; 14(5): 435–438.CrossRefGoogle ScholarPubMed
Li, B, Mah, K, Swami, N, Pope, A, Lo, C, Rodin, G, et al. Symptom correspondence between the ESAS and QUAL-E in patients with advanced cancer. J Pain Symptom Manage. 2018; 56(6): e60.CrossRefGoogle Scholar
Hui, D, de la Cruz, M, Thorney, S, Parsons, H A, Delgado-Guay, M, Bruera, E. The frequency and correlates of spiritual distress among patients with advanced cancer admitted to an acute palliative care unit. Am J Hosp Palliat Med. 2011; 28(4): 264–270.CrossRefGoogle Scholar
Hannon, B, Dyck, M, Pope, A, Swami, N, Banerjee, S, Mak, E, et al. Modified Edmonton symptom assessment system including constipation and sleep: validation in outpatients with cancer. J Pain Symptom Manage. 2015; 49(5): 945–952.CrossRefGoogle ScholarPubMed
Salvo, N, Zeng, L, Zhang, L, Leung, M, Khan, L, Presutti, R, et al. Frequency of reporting and predictive factors for anxiety and depression in patients with advanced cancer. Clin Oncol. 2012; 24(2): 139–148.CrossRefGoogle ScholarPubMed
Yogananda, M N, Muthu, V, Prasad, K T, Kohli, A, Behera, D, Singh, N. Utility of the revised Edmonton symptom assessment system (ESAS-r) and the patient-reported functional status (PRFS) in lung cancer patients. Support Care Cancer. 2018; 26(3): 767–775.CrossRefGoogle Scholar
Cheung, W Y, Le, L W, Zimmermann, C. Symptom clusters in patients with advanced cancers. Support Care Cancer. 2009; 17(9): 1223–1230.CrossRefGoogle ScholarPubMed
Cheung, W Y, Barmala, N, Zarinehbaf, S, Rodin, G, Le, L W, Zimmermann, C. The association of physical and psychological symptom burden with time to death among palliative cancer outpatients. J Pain Symptom Manage. 2009; 37(3): 297–304.CrossRefGoogle ScholarPubMed
Hallet, J, Davis, L E, Isenberg-Grzeda, E, Mahar, A L, Zhao, H, Zuk, V, et al. Gaps in the management of depression symptoms following cancer diagnosis: a population-based analysis of prospective patient-reported outcomes. Oncologist. 2020; 25(7): e1098–e1108.CrossRefGoogle ScholarPubMed
Graham, J, Gingerich, J, Lambert, P, Alamri, A, Czaykowski, P. Baseline Edmonton symptom assessment system and survival in metastatic renal cell carcinoma. Curr Oncol. 2018; 25(4): 319.CrossRefGoogle ScholarPubMed
Goyal, U D, Riegert, K, Davuluri, R, Ong, S, Yi, S K, Dougherty, S T, et al. Prospective study of use of Edmonton symptom assessment scale versus routine symptom management during weekly radiation treatment visits. JCO Oncol Pract. 2020; 16 (9): e1029–e1035.CrossRefGoogle ScholarPubMed
Ganesh, V, Zhang, L, Chan, S, Wan, B A, Drost, L, Tsao, M, et al. An update in symptom clusters using the Edmonton symptom assessment system in a palliative radiotherapy clinic. Support Care Cancer. 2017; 25(11): 3321–3327.CrossRefGoogle Scholar
Yamaguchi, T, Morita, T, Nitto, A, Takahashi, N, Miyamoto, S, Nishie, H, et al. Establishing Cutoff points for defining symptom severity using the Edmonton symptom assessment system-revised Japanese version. J Pain Symptom Manage. 2016; 51(2): 292–297.CrossRefGoogle ScholarPubMed
Tran, K, Zomer, S, Chadder, J, Earle, C, Fung, S, Liu, J, et al. Measuring patient-reported outcomes to improve cancer care in Canada: an analysis of provincial survey data. Curr Oncol. 2018; 25(2): 176–179.CrossRefGoogle Scholar
Newcomb, R A, Nipp, R D, Waldman, L P, Greer, J A, Lage, D E, Hochberg, E P, et al. Symptom burden in patients with cancer who are experiencing unplanned hospitalization. Cancer. 2020; 126(12): 2924–2933.CrossRefGoogle ScholarPubMed
Pereira, J, Green, E, Molloy, S, Dudgeon, D, Howell, D, Krzyzanowska, M K, et al. Population-based standardized symptom screening: cancer care Ontario’s Edmonton symptom assessment system and performance status initiatives. J Oncol Pract. 2014; 10(3): 212–214.CrossRefGoogle ScholarPubMed
Delgado-Guay, M, Parsons, H A, Li, Z, Palmer, J L, Bruera, E. Symptom distress in advanced cancer patients with anxiety and depression in the palliative care setting. Support Care Cancer. 2009; 17(5): 573–579.CrossRefGoogle ScholarPubMed
Butt, Z, Wagner, L I, Beaumont, J L, Paice, J A, Peterman, A H, Shevrin, D, et al. Use of a single-item screening tool to detect clinically significant fatigue, pain, distress, and anorexia in ambulatory cancer practice. J Pain Symptom Manage. 2008; 35(1): 20–30.CrossRefGoogle ScholarPubMed
Yennurajalingam, S, Kim, Y J, Zhang, Y, Park, J, Arthur, J, Chisholm, G B, et al. Factors associated with patient-reported subjective well-being among advanced lung or non-colonic gastrointestinal cancer patients. Palliat Support Care. 2018; 16(1): 23–31.CrossRefGoogle ScholarPubMed
Chow, S, Wan, B A, Pidduck, W, Zhang, L, DeAngelis, C, Chan, S, et al. Symptom clusters in patients with breast cancer receiving radiation therapy. Eur J Oncol Nurs. 2019; 42: 14–20.CrossRefGoogle ScholarPubMed
Abu-Helalah, M, Mustafa, H, Alshraideh, H, Alsuhail, A, Almousily, O A, Al-Abdallah, R, et al. Quality of life and psychological wellbeing of breast cancer survivors in the Kingdom of Saudi Arabia. Asian Pac J Cancer Prev. 2022; 23(7): 2291–2297.CrossRefGoogle ScholarPubMed
Eymech, O, Brunckhorst, O, Fox, L, Jawaid, A, Van Hemelrijck, M, Stewart, R, et al. An exploration of wellbeing in men diagnosed with prostate cancer undergoing active surveillance: a qualitative study. Support Care Cancer. 2022; 30(6): 5459–5468.CrossRefGoogle ScholarPubMed
Firkins, J, Hansen, L, Driessnack, M, Dieckmann, N. Quality of life in “chronic” cancer survivors: a meta-analysis. J Cancer Surviv. 2020; 14(4): 504–517.CrossRefGoogle ScholarPubMed
Hamer, J, McDonald, R, Zhang, L, Verma, S, Leahey, A, Ecclestone, C, et al. Quality of life (QOL) and symptom burden (SB) in patients with breast cancer. Support Care Cancer. 2017; 25(2): 409–419.CrossRefGoogle Scholar
Basch, E, Deal, A M, Kris, M G, Scher, H I, Hudis, C A, Sabbatini, P, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016; 34(6): 557–565.CrossRefGoogle ScholarPubMed
Subbiah, I M, Charone, M M, Roszik, J, Haider, A, Vidal, M, Wong, A, et al. Association of Edmonton symptom assessment system global distress score with overall survival in patients with advanced cancer. JAMA Netw Open. 2021; 4(7): e2117295.CrossRefGoogle ScholarPubMed
Niedzwiedz, C L, Knifton, L, Robb, K A, Katikireddi, S V, Smith, D J. Depression and anxiety among people living with and beyond cancer: a growing clinical and research priority. BMC Cancer. 2019; 19(1): 943.CrossRefGoogle Scholar
Whitford, H S, Olver, I N, Peterson, M J. Spirituality as a core domain in the assessment of quality of life in oncology. Psycho-oncology. 2008; 17(11): 1121–1128.CrossRefGoogle ScholarPubMed
Akgün, K M, Krishnan, S, Feder, S L, Tate, J, Kutner, J S, Crothers, K. Polypharmacy increases risk of dyspnea among adults with serious, life-limiting diseases. Am J Hosp Palliat Med. 2020; 37(4): 278–285.CrossRefGoogle ScholarPubMed
Barbera, L, Seow, H, Howell, D, Sutradhar, R, Earle, C, Liu, Y, et al. Symptom burden and performance status in a population-based cohort of ambulatory cancer patients. Cancer. 2010; 116(24): 5767–5776.CrossRefGoogle Scholar
Kim, J H. Multicollinearity and misleading statistical results. Korean J Anesthesiol. 2019; 72(6): 558–569.CrossRefGoogle ScholarPubMed
Akobeng, A K. Understanding diagnostic tests 3: receiver operating characteristic curves. Acta Paediatr. 2007; 96(5): 644–647.CrossRefGoogle ScholarPubMed
Khan, L, Cramarossa, G, Chen, E, Nguyen, J, Zhang, L, Tsao, M, et al. Symptom clusters using the Edmonton symptom assessment system in patients with bone metastases: a reanalysis comparing different statistical methods. World J Oncol. 2012; 3(1): 23–32.Google ScholarPubMed
Chow, E, Fan, G, Hadi, S, Filipczak, L. Symptom clusters in cancer patients with bone metastases. Support Care Cancer. 2007; 15(9): 1035–1043.CrossRefGoogle ScholarPubMed
Cheung, W Y, Le, L W, Gagliese, L, Zimmermann, C. Age and gender differences in symptom intensity and symptom clusters among patients with metastatic cancer. Support Care Cancer. 2011; 19(3): 417–423.CrossRefGoogle ScholarPubMed
Chen, E, Nguyen, J, Cramarossa, G, Khan, L, Zhang, L, Tsao, M, et al. Symptom clusters in patients with advanced cancer: sub-analysis of patients reporting exclusively non-zero ESAS scores. Palliat Med. 2012; 26(6): 826–833.CrossRefGoogle ScholarPubMed
Yennurajalingam, S, Palmer, J L, Zhang, T, Poulter, V, Bruera, E. Association between fatigue and other cancer-related symptoms in patients with advanced cancer. Support Care Cancer. 2008; 16(10): 1125–1130.CrossRefGoogle ScholarPubMed
Wilding, S, Downing, A, Wright, P, Selby, P, Watson, E, Wagland, R, et al. Cancer-related symptoms, mental well-being, and psychological distress in men diagnosed with prostate cancer treated with androgen deprivation therapy. Qual Life Res. 2019; 28(10): 2741–2751.CrossRefGoogle ScholarPubMed
Goodrose-Flores, C, Bonn, S, Klasson, C, Helde Frankling, M, Trolle Lagerros, Y, Björkhem-Bergman, L. Appetite in palliative cancer patients and its association with albumin, CRP and quality of life in men and women-cross-sectional data from the palliative D-study. Life. 2022; 12(5): 671.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. A summary of patient-reported ESAS symptom severity for entire cohort of patients stratified by wellbeing severity (0, 1–3, 4–6 and 7–10) and the Spearman rank correlation coefficient. 0 indicates best possible wellbeing, and 10 indicates worst possible wellbeing. Spearman’s rank correlation coefficients <0·3, 0·3–0·5 and >0·5 indicate a weak, moderate and strong correlation, respectively (Cheung et al., 2009a). The p-values for all of the Spearman correlations are < 0·0001. ESAS-PHY: ESAS total physical symptom score; ESAS-PSY: ESAS total psychological symptom score

Figure 1

Table 2. The odds ratios (OR) and 95% confidence intervals (CI) from univariate and multivariable binary logistic regression analysis for reporting moderate-to-severe wellbeing when a concurrent symptom is scored as moderate-to-severe compared to reporting none-to-mild for all patients. ORs > 1 indicate higher odds of reporting moderate-to-severe wellbeing when a concurrent symptom is reported as moderate-to-severe. Score of 0–3 (no symptom presence to mild) was used as the reference to calculate the OR for scoring the concurrent symptom as moderate-to-severe. ESAS symptom scores are stratified by none-to-mild (0–3) and moderate-to-severe (4–10). ESAS-PHY scores are stratified by low-to-mild (0–23) and moderate-to-severe (24–60). ESAS-PSY scores are stratified by low-to-mild (0–7) and moderate-to-severe (8–20)

Figure 2

Table 3. The odds ratios (OR) and 95% confidence intervals (CI) from the multivariable binary logistic regression analysis for reporting moderate-to-severe wellbeing or severe wellbeing when a concurrent symptom is scored as mild, moderate or severe compared to reporting none for all patients. ORs > 1 represent higher odds of reporting moderate-to-severe or severe wellbeing. ORs = 1 represents no effect on the odds of reporting moderate-to-severe or severe wellbeing. Score of 0 (no symptom presence) was used as the reference to calculate the OR for scoring the concurrent symptom as mild, moderate or severe. ESAS symptom scores are stratified by none (0), mild (1–3), moderate (4–6) and severe (7–10). ESAS-PHY scores are stratified by low (0–5), mild (6–23), moderate (24–41) and severe (42–60). ESAS-PSY scores are stratified by low (0–1), mild (2–7), moderate (8–13) and severe (14–20)

Figure 3

Table 4. The area under the ROC (AUROC) curve with asymptomatic 95% confidence interval (CI) for detection of moderate-to-severe wellbeing for all cancer patients. AUROC < 0·5, 0·5 ≤ AUROC < 0·7, 0·7 ≤ AUROC < 0·9, and AUROC ≥ 0·9 indicate no diagnostic ability, low diagnostic ability, moderate diagnostic ability and high diagnostic ability respectively consistent with Akobeng (2006)

Figure 4

Table 5. Results from the principal component analysis (PCA) and exploratory factor analysis (EFA) with varimax rotation. Factor loadings and final communality from the PCA and EFA of ESAS symptoms are also shown. Components or factors are retained for analysis if their eigenvalue > 0·80 and accounted for ≥10% of the total variance. Symptoms are considered part of a cluster if their loading scores are >0·60

Figure 5

Figure 1. Stacked bar chart of prevalence and severity of ESAS symptoms, ESAS-PHY and ESAS-PSY from patient responses. ESAS symptom scores are stratified as none (0), mild (1–3), moderate (4–6) and severe (7–10). ESAS-PHY scores are stratified as low (0–5), mild (6–23), moderate (24–41) and severe (42–60). ESAS-PSY scores are stratified as low (0–1), mild (2–7), moderate (8–13) and severe (14–20)

Figure 6

Figure 2. Boxplots of odds ratios (OR) and 95% confidence interval (CI) from the (a) univariate and (b) multivariable binary logistic regression analysis for reporting moderate-to-severe wellbeing when a concurrent symptom is reported as moderate-to-severe compared to reporting none-to-mild for concurrent symptom

Figure 7

Figure 3. ROC curves for the detection of moderate-to-severe wellbeing (4–10) score for all cancer patients stratified into (a) all ESAS symptoms, (b) ESAS physical symptom and (c) ESAS psychological symptom

Figure 8

Figure 4. Biplots from (a) principal component analysis (PCA) with varimax rotation and (b) exploratory factor analysis (EFA) with varimax rotation to identify symptoms which cluster with wellbeing. A higher correlation between symptoms is represented by lines that are longer and closer together. Symptoms are considered part of a cluster if their loading score > 0·60, as indicated by the dotted grey line