Factors Affecting Health-Related Quality of Life in Patients With Metastatic Breast Cancer

Abstract

Purpose

Improving survival and health-related quality of life (HRQOL), along with symptom relief, is important for the treatment of metastatic breast cancer (MBC). This study measured HRQOL and analyzed its influence on sociodemographic and clinical factors in patients with MBC.

Methods

We interviewed 298 patients with MBC to investigate their sociodemographic characteristics and HRQOL by using EuroQol-5D-5L (EQ-5D) between September and October 2014. We also reviewed medical records to examine the clinical condition of the patients, including disease progression, adverse events, treatments, chronic disease, and metastatic areas. The distribution of the EQ-5D index was compared between different clinical conditions by using the Kruskal-Wallis test. We also conducted multiple regression analyses to identify the factors affecting HRQOL in patients with MBC.

Results

The mean EQ-5D index was 0.79 for all patients surveyed. The mean EQ-5D index score was significantly lower in patients in the progressed state than in those in the progression-free survival state (0.73 vs. 0.80, p = 0.0002). The HRQOL of patients treated with chemotherapy alone was significantly lower than that of patients treated with hormonal or targeted therapy (0.76 vs. 0.82 or 0.85; p = 0.0020). Regression analysis revealed that the clinical factors associated with lower HRQOL were progressed state, chemotherapy, and adverse events, such as hair loss or stomatitis. Finally, young age, high income, and employment were the sociodemographic factors that were positively associated with better HRQOL.

Conclusion

This study provides new information on the health utility of MBC patients on the basis of various patient characteristics and offers insights that can assist medical professionals in treating patients with MBC and help policymakers implement cancer strategies. Further research is needed to reflect the changing environment of cancer treatment and enrich available evidence.

INTRODUCTION

Breast cancer is the most commonly diagnosed cancer in women, with 2.26 million incident cases occurring worldwide in 2020 [1]. It is also the leading cause of cancer-related deaths in women, followed by lung and colorectal cancers [1]. Although breast cancer has been extensively investigated, the worldwide prevalence of metastatic breast cancer (MBC), which is the most advanced form of breast cancer, is unknown [2]. In Korea, MBC accounts for approximately 1% of all breast cancer [3]. Although patients with early-stage breast cancer usually have good prognosis and have a five-year survival rate of over 90%, MBC is recognized as an incurable disease with a survival rate of 27%–34% [3,4]. Therefore, efforts to develop new treatments for MBC, particularly those targeting hormone receptors or gene mutations, have been ongoing over the past decade [5].

Investigators regard quality of life as an important endpoint to measure in clinical trials and survivorship studies, along with survival outcomes [6,7]. Survival times have increased because of the availability of new treatments, and the paradigm has shifted to patient-centered care in recent years. Health-related quality of life (HRQOL) is a quality-of-life concept that focuses on the effects of illness and treatment on various aspects of health, including physical, mental, and emotional status [8,9]. HRQOL can be measured using generic, disease-specific, or condition-specific instruments [10]. Although disease-specific instruments allow for greater sensitivity to the domains most relevant to the disease, generic instruments have the unique advantage of permitting comparisons with the general population or between groups with different diseases [11]. Therefore, generic instruments are well suited to assist decision makers in allocating limited healthcare resources effectively. However, few studies have evaluated the HRQOL of women with MBC or other clinical conditions by using generic instruments [12,13,14]. Little is known about this topic in the Korean population. Patients with the same disease may have different HRQOLs across countries owing to differences in culture and the availability of new treatments. Therefore, it is necessary to assess the HRQOL of Korean patients with MBC.

We measured the HRQOL of patients with MBC who visited two tertiary hospitals in Korea in terms of progression-free survival (PFS) and progressed state, which are commonly considered in the cost-effectiveness analyses of MBC [15,16,17], by using the EuroQol-5D-5L (EQ-5D). EQ-5D is one of the most popular generic instruments used to estimate HRQOL [18,19], and EQ-5D index values are commonly used to estimate quality-adjusted life year gains in the economic evaluations of healthcare interventions. In addition, we stratified patients according to various clinical conditions such as progression status, therapy type, and number of adverse events, as well as factors affecting their EQ-5D index values.

METHODS

HRQOL measurements

We assessed the HRQOL by using the official version of the EQ-5D provided by the EuroQol Group. The EQ-5D-5L consists of five dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) that are measured in five levels. By combining 1 level from each of the 5 dimensions, the EQ-5D-5L yielded 3,125 possible health states. The EQ-5D index was calculated using the EQ-5D-5L valuation set created for the Korean population in 2014 [20,21].

Study participants and procedures

A total of 298 female patients with MBC were recruited from two tertiary hospitals in Seoul, Korea, between September and October 2014. Only patients who agreed to participate in the study and signed an informed consent form were surveyed to measure their HRQOL and its influence on sociodemographic, economic, and educational characteristics. Most of the patients included in the survey were outpatients, and only six were inpatients. They were interviewed in person by three pre-trained nurses during medical visits or hospitalizations. Survey guidelines were created to instruct the interviewers about the interview process and eliminate deviations.

Patient medical records were reviewed to collect clinical information on metastatic lesions, adverse events associated with treatment, type of treatment in the preceding four weeks, and disease progression status. Patients with MBC were classified in terms of PFS and progression state, as assessed by a doctor. PFS was further classified into responsive and stable phases on the basis of response evaluation. These two health states are frequently used in pharmacoeconomic studies of cancer drugs [15,16,17]. The physician in charge determined the patients’ Eastern Cooperative Oncology Group Performance State (ECOG-PS) score, which is widely used to assess the functional performance status of patients [22].

Statistical analyses

We analyzed the distribution of the patients’ sociodemographic and clinical characteristics. We then calculated the EQ-5D index according to the clinical condition of the patients with MBC and compared the means by using the Kruskal-Wallis test. Multiple regression analyses were also conducted to assess the effect of sociodemographic factors and clinical conditions on the EQ-5D index. Three regression models were used to identify the factors affecting HRQOL by classifying treatment types, adverse events, and sociodemographic factors. Model 1 included clinical status and treatment as independent variables, Model 2 included adverse events and the variables in Model 1, and Model 3 explored the effects of sociodemographic factors.

The EQ-5D index scores ranged from zero to one and did not follow a normal distribution. Thus, we applied a beta distribution to the multiple regressions. The beta distribution is a very flexible starting point because it allows for the modeling of data that are left and right skewed and have heteroscedastically distributed outcomes [23]. The beta regression model was shown to be superior to alternative regressions in previous HRQOL studies, and the logit function has been commonly used as a link of choice [24,25]. Three regression models were analyzed to demonstrate the differential effects of the independent variables. Model 1 included only clinical characteristics as independent variables, Model 2 included adverse events and the variables in Model 1, and Model 3 included socioeconomic characteristics. Statistical analysis was performed using Stata Statistical Software Release 17 (StataCorp LLC, College Station, USA).

Ethical approval

This study was approved by the Institutional Review Boards (IRBs) of two tertiary hospitals in Seoul (IRB No. 2014-08076 and 2014-0875). Informed consent was obtained from all participants in the study.

RESULTS

Among the 298 patients surveyed, 71.47% were in their 40s and 50s, and 78.86% had attained a high school or higher education level. Among the respondents, 75.17% were married or living with someone, 82.88% were either unemployed or homemakers, and 38.58% earned more than USD 4,000 in family income per month (Table 1). The ECOG-PS ranged from zero to one for most patients: 37.60% were fully active, with a score of zero, and 60.00% had experienced restrictions in physically strenuous activity but were ambulatory, with a score of one. Only 2.40% of the patients had an ECOG-PS score of two, thus indicating that they were capable of self-care but were unable to perform any work activities. Most patients (83.22%) had PFS, and the remaining 49 patients were in a progressed state. Among the patients with PFS, 31.21% and 52.01% were in responsive and stable states, respectively. Most patients (70.47%) received chemotherapy, and 27.52% and 25.50% were treated with hormonal and targeted therapies, respectively, either as monotherapy or in combination. Among the patients, 84.90% experienced one or more adverse events in the previous four weeks, with fatigue (57.05%) and hand–foot syndrome (56.38%) being the most common. Lymph nodes (51.86%) and bones (51.19%) were the most frequent sites of metastasis.

Table 1. General characteristics of study subjects.

Variable		Value
Female		298 (100.00)
EQ-5D index score		0.79 ± 0.13
Age (yr)		52.31 ± 9.50
Age (yr)
	20–39	24 (8.05)
	40–49	96 (32.21)
	50–59	117 (39.26)
	60 and older	60 (20.13)
	Unknown	1 (0.34)
Marital status
	Never married	34 (11.41)
	Married/cohabitating	224 (75.17)
	Widowed/divorced/separated	40 (13.42)
Education
	Secondary school graduate or less	63 (21.15)
	High school graduate	137 (45.97)
	College graduate or higher	98 (32.89)
Family income monthly ($)
	< 2,000	83 (27.85)
	2,000–3,999	100 (33.56)
	4,000–5,999	58 (19.46)
	6,000–7,999	31 (10.40)
	≥ 8000	26 (8.72)
Employed
	Yes	51 (17.11)
	No	247 (82.88)
Sleep difficulty
	Yes	119 (39.93)
	No	179 (60.07)
Mastectomy
	Yes	219 (73.49)
	No	79 (26.51)
Chronic diseases*
	Yes	112 (37.97)
	No	186 (62.03)
ECOG-PS
	0	94 (37.60)
	1	150 (60.00)
	2	6 (2.40)
	Unknown	48 (16.11)
Disease progression
	PFS-responsive	93 (31.21)
	PFS-stable	155 (52.01)
	Progressive state	50 (16.78)
Adverse event
	Febrile neutropenia	27 (9.06)
	Diarrhea or vomiting	50 (16.78)
	Hand-foot syndrome	168 (56.38)
	Stomatitis	80 (26.85)
	Fatigue	170 (57.05)
	Hair loss	100 (33.56)
Type of treatment
	Chemotherapy	210 (70.47)
	Hormonal therapy	82 (27.52)
	Targeted therapy	76 (25.50)
	None	9 (3.02)
	Unknown	4 (1.34)
Metastatic area
	Bone	151 (51.19)
	Liver	73 (24.75)
	Lung	102 (34.58)
	Lymph	153 (51.86)
	Pleura	25 (8.47)
	Brain	21 (7.12)
	Skin	12 (4.07)
	Other	36 (12.20)
	Unknown	3 (1.01)

Values are presented as number (%) or mean ± standard deviation.

EQ-5D = EuroQol-5D-5L; ECOG-PS = Eastern Cooperative Oncology Group Performance Status; PFS = progression-free survival.

^*Any illness lasting more than 3 months.

The EQ-5D index was summarized according to the clinical status of the patients with MBC (Table 2). The mean score for the total sample of patients was 0.79. The mean score was significantly higher in patients with PFS than in those in the progressed state (0.80 vs. 0.73, p = 0.0002). Within PFS, patients in the responsive phase had higher HRQOL than those in the stable phase; however, the difference was not significant (0.82 vs. 0.79). Patients who received chemotherapy had significantly lower HRQOL than those who received hormonal or targeted therapy (p = 0.0020). In addition, an increase in the number of adverse events and higher ECOG-PS scores were significantly associated with a lower HRQOL (p = 0.0001 for both).

Table 2. Quality of life according to the clinical condition of metastatic breast cancer.

Variable		No.	Mean ± SD	p-value
All patients		298	0.79 ± 0.13
Disease progression				0.0002
	Progression-free survival: responsive	93	0.82 ± 0.10
	Progression-free survival: stable	155	0.79 ± 0.12
Progressive state		50	0.73 ± 0.19
Type of therapy within one month				0.0020
	Chemotherapy only	134	0.76 ± 0.145
	Hormonal therapy only	54	0.82 ± 0.10
	Targeted therapy only	15	0.85 ± 0.07
	Chemotherapy & hormone	21	0.83 ± 0.05
	Chemotherapy & target	54	0.81 ± 0.10
	Target & hormone	6	0.82 ± 0.05
	Chemotherapy & hormone & target	1	0.68
Number of adverse events				0.0001
	≤ 2	178	0.81 ± 0.11
	≥ 3	120	0.75 ± 0.15
ECOG-PS				0.0001
	0	94	0.85 ± 0.06
	1	150	0.76 ± 0.13
	2	6	0.46 ± 0.32

SD = standard deviation; ECOG-PS = Eastern Cooperative Oncology Group Performance Status.

Regression Model 1 showed that HRQOL was negatively associated with older age (p = 0.025), chemotherapy (p = 0.034), and progressed state (p = 0.005; Table 3). In Model 2, chemotherapy no longer had a negative effect on HRQOL. However, adverse events, such as hair loss (p = 0.056) and stomatitis (p = 0.084), were negatively associated with HRQOL. Other adverse events, mastectomy, and duration of illness from diagnosis appeared to decrease the HRQOL but at a statistically insignificant extent. Model 3 showed that HRQOL increased significantly when household income was USD 8,000 or more compared with USD 2,000 (p = 0.042) and when the patient with MBC was employed (p = 0.003).

Table 3. Factors affecting quality of life of patients with metastatic breast cancer.

Variable	Reference (if categorical)	Model 1 Coefficient	Model 2 Coefficient	Model 3 Coefficient
Age (yr)		−0.009**	−0.008**	−0.005
Progressive state	Progression-free survival	−0.271***	−0.288***	−0.254***
Chemotherapy	Other treatment	−0.178**	−0.081
Duration of illness from diagnosis (mon)		−0.031	−0.026
	No surgery†	−0.067	−0.063
Hair loss	No hair loss		−0.156*
Stomatitis	No stomatitis		−0.147*
Diarrhea or vomiting	No diarrhea or vomiting		−0.154
Neutropenia	No neutropenia		0.105
Hand-foot syndrome	No hand-foot syndrome		−0.062
Family monthly income $2,000–$3,999	< $2,000			0.053
Family monthly income $4,000–$5,999	< $2,000			0.009
Family monthly income $6,000–$7,999	< $2,000			−0.009
Family monthly income ≥ $8,000	< $2,000			0.259*
College graduate or higher	High school graduate or less			0.079
Employed	Unemployed			0.312***
Married or cohabitating	Other marital status			0.079
Akaike information criteria		(−488)	(−491)	(−490)
Bayesian information criteria		(−462)	(−447)	(−450)

^*p < 0.1, ^**p < 0.05, and ^***p < 0.01.

^†Any surgery regardless of duration from surgery to survey.

DISCUSSION

This study found that the HRQOL of patients with MBC was the highest in the responsive phase of PFS and decreased as the disease progressed. However, the difference in the EQ-5D indices between health states was small, with a maximum value of 0.09. The current study showed a higher quality of life than a previous study (0.79 vs. 0.71 for the stable state, 0.82 vs. 0.78 for the responsive phase, and 0.73 vs. 0.44 for the progressed state) conducted in the United Kingdom [26]. Lloyd et al. [26] interviewed the general population by using the standard gamble to determine the health state utility with hypothetical health scenarios developed to describe the health states of MBC and adverse events occurring in patients. The HRQOL reported by the patients with advanced MBC differed from that measured by using a hypothetical scenario. Usually, patients report a higher quality of life than the general population for the same health condition [27] because as patients adapt to their illness, they take it less seriously than the general population who have not experienced the illness [28]. In the current study, patients in the progressed state of MBC rated their HRQOL slightly lower (0.09) than those in the treatment response stage. Patients evaluate and respond to their overall health condition; however, the general population is likely to regard the disease as described in the scenario without an accurate understanding of the disease. Consequently, non-patients tend to perceive the disease more seriously than patients.

A few studies have measured the quality of life of patients with MBC in Korea, but most have used disease-specific instruments [29,30]. A study that developed eight hypothetical health states and measured them in the general population by using a preference-based measurement, namely, the standard gamble, reported a quality of life of 0.352 for MBC [31]. South Korea uses economic evaluations to assess the value of new drugs when deciding whether to reimburse patients. In this case, a generic instrument that reflects preferences is used to measure quality of life, and the EQ-5D is the most commonly used instrument recommended by the National Institute for Health and Care Excellence in the UK. However, no study has used the EQ-5D to measure quality of life in Korean patients with MBC.

The HRQOL of a patient may depend on the type of treatment administered. Adamowicz and Baczkowska-Waliszewska [32] reported that the quality of life measured using the EORTC-QLQ30 was lower in the chemotherapy group than in patients receiving targeted therapy or hormonal therapy. Women receiving hormone therapy reported fewer severe systemic side effects than those treated with chemotherapy [32]. Schleinitz et al. [33] investigated the HRQOL of women ≥ 25 years old by using the time trade-off method and reported that the mean HRQOL score was higher in patients treated with hormone therapy than in those treated with chemotherapy (0.54 vs. 0.48). In the current study, HRQOL was the highest among patients receiving only targeted therapy (0.85) and the lowest among patients receiving only chemotherapy (0.76); hormonal therapy was lower than targeted therapy (0.82).

Model 1 showed that chemotherapy was significantly associated with decreased HRQOL in patients with MBC. However, when chemotherapy-related adverse events were added as independent variables in Model 2, the significance of chemotherapy disappeared, and chemotherapy-induced adverse events, such as hair loss and stomatitis, were significantly associated with lower HRQOL. These findings suggest that adverse events are an important cause of compromised HRQOL in patients undergoing chemotherapy. The relationship between HRQOL and diarrhea/vomiting was not statistically significant; however, the EQ-5D scores for these events were as low as those for hair loss.

Several studies have reported the effect of sociodemographic factors on the quality of life of patients with MBC. Eljedi and Nofal [34] reported that patients who were employed and had higher education and income levels were more likely to have higher HRQOL scores. Other studies reported that financial difficulties significantly aggravate the quality of life of patients with breast cancer [35,36,37]. The current study found that the effect of income was significantly positive only in the highest income group, and this finding may be primarily due to the benefits provided to patients with cancer by the national healthcare system. In Korea, cancer patients pay a co-payment rate of 5%, with a cap on the annual burden associated with cancer treatment, according to income. Although more than 60% of the patients surveyed in our study had a monthly household income of less than USD 40,000 and considering that the respondents’ income levels were evenly distributed, economic levels might not have significantly affected the results, except for those in the highest group.

Previous studies have shown that higher education [37] and employment [35] are predictors of quality of life. However, employment alone was a significant predictor. Marital status was not significantly associated with quality of life in our study, and this finding is consistent with the results of a previous study [34].

Each regression model revealed that several factors affected the HRQOL of patients with MBC. Among the clinically important factors, disease progression had the greatest effect on the quality of life, and chemotherapy also resulted in a significant decrease.

This study had several limitations. First, the study included only six patients with an ECOG-PS of two who were hospitalized because of a severe condition, and it was difficult to interview them because of their critical condition. Generally, patients with progressive MBC have an ECOG-PS score of zero or one when receiving outpatient treatment. Nonetheless, the quality of life in advanced stages may have been slightly overestimated. Second, because adverse events were not collected separately for each drug administered, it was not possible to directly confirm the effect of adverse reactions according to treatment or to report the effect of individual adverse reactions on the quality of life. Third, the treatment paradigm for patients with MBC has changed significantly since our study with the adoption of new targeted therapies and immunotherapies (e.g., cyclin-dependent kinase 4/6 inhibitors, human epidermal growth factor receptor 2 inhibitors, and programmed death ligand 1 inhibitors). This change in treatment type may affect the patients’ quality of life. Our study showed that HRQOL differed according to the therapy type (Table 2). Further studies are needed to investigate the effects of these new drugs on the HRQOL.

This study found that disease progression, chemotherapy, and adverse events led to poor HRQOL in patients with MBC. HRQOL differs depending on the clinical condition, and several sociodemographic factors independently affect HRQOL. This study provides insights that are useful for medical professionals who are treating patients and economic analysts who are performing cost–utility studies.