Demographic and treatment-related characteristics
A total of 239 patients participated in the study, 188 of whom completed all the investigations. All of them were female, and their mean age was 53.79 (SD 10.44). Demographic and treatment-related characteristics of the patients are shown in Table 1.
Table 1
Demographic and treatment-related characteristics of participants(n=239)
Characteristics of participants
|
N (%)
|
Age
|
53.79±10.44
|
Sex
|
|
Male
|
0(0.0%)
|
Female
|
239(100.0%)
|
Marital Status
|
|
Married
|
205(85.8%)
|
Unmarried
|
13(5.4%)
|
Divorced
|
14(5.9%)
|
Widowed
|
7(2.9%)
|
Residence
|
|
Rural
|
76(31.8%)
|
Urban
|
163(68.2%)
|
Mode of residence
|
|
Living alone
|
31(12.9%)
|
Living with spouse or children
|
208(87.1%)
|
Menstrual Cycle
|
|
Not menopausal, with regular monthly periods
|
76(34.4%)
|
Not menopausal, with irregular monthly periods
|
35(15.8%)
|
Menopausal
|
102(46.2%)
|
Uterine removed
|
8(3.6%)
|
Education
|
|
Primary school
|
29(12.1%)
|
Junior high school
|
96(40.2%)
|
Senior high school/Technical secondary school
|
69(28.9%)
|
Bachelor’s degree and above
|
26(10.9%)
|
Employment status
|
|
On the job
|
21(8.8%)
|
On sick leave
|
82(34.3%)
|
Unemployed
|
31(13.0%)
|
Retired
|
105(43.9%)
|
Occupation
|
|
Farmer
|
38(15.9%)
|
Worker
|
29(12.1%)
|
Cadre
|
11(4.6%)
|
Teacher
|
31(12.9%)
|
Student
|
0(0.0%)
|
Self-employed
|
46(19.2%)
|
Others
|
84(35.3%)
|
Religion
|
|
Yes
|
43(17.9%)
|
No
|
196(82.1%)
|
Per capita monthly household income
|
|
≤3000 RMB
|
14(5.9%)
|
3001-6000 RMB
|
138(57.7%)
|
6001-9000 RMB
|
77(32.2%)
|
>9000 RMB
|
10(4.2%)
|
Payment method for medical expenses
|
|
Self-funded
|
26(10.9%)
|
Publicly-funded
|
0(0.0%)
|
Medical insurance
|
213(89.0%)
|
Family history of the disease
|
|
Yes
|
52(21.8%)
|
No
|
187(78.2%)
|
Knowledge of the disease
|
|
Fully aware
|
10(4.2%)
|
Partially aware
|
189(79.1%)
|
Unaware
|
40(16.7%)
|
Other diseases
|
|
Diabetes
|
16(6.7%)
|
Hypertension
|
49(20.5%)
|
Heart Disease
|
11(4.6%)
|
Others
|
16(6.7%)
|
None
|
147(61.5%)
|
Disease stage
|
|
Ⅰ
|
0(0.0%)
|
Ⅱ
|
101(42.3%)
|
Ⅲ
|
138(57.7%)
|
Ⅳ
|
0(0.0%)
|
Lymph node metastasis
|
|
Yes
|
130(54.4%)
|
No
|
109(45.6%)
|
Unknown
|
0(0.0%)
|
Surgical options
|
|
Breast-conserving surgery
|
49(20.5%)
|
Simple mastectomy
|
67(28.0%)
|
Modified radical mastectomy
|
110(46.0%)
|
Extended radical mastectomy
|
13(5.5%)
|
Chemotherapy medications
|
|
Anthracycline
|
65(27.2%)
|
Taxanes
|
78(32.6%)
|
Anthracycline and Taxanes
|
96(40.2%)
|
Overall Trend Analysis
The overall trend of fatigue and depression levels throughout the chemotherapy cycle is gradually increasing. In contrast, the level of pain and anxiety symptoms gradually decreased with the progress of chemotherapy(Figure 1).
GMM Analysis
Fatigue
Using GMM to analyze fatigue data, the BIC value of the 3-class model was the smallest, and BLRT and VLMR were statistically significant, so the 3-class model was selected (Table 2). As shown in Figure 2, most patients were classified into the moderate-fatigue group (n=174, 73.0%). The proportion of the mild-fatigue group was 14.2% (n=34), and patients of this group had the lowest fatigue level and maintained a similar level throughout the chemotherapy cycle. The severe fatigue group was the least (n=31,12.8%), but these patients experienced more severe fatigue in the whole chemotherapy cycle, and the fatigue level increased with the progress of chemotherapy. The changing trend was gradually increased in the first three chemotherapy cycles and decreased in the fourth chemotherapy cycle.
Table 2
Summary of model fitting information
(a) Fitting results of six alternative potential class models for fatigue trajectory in breast cancer patients undergoing chemotherapy
GMM
|
LL
|
AIC
|
BIC
|
ABIC
|
entropy
|
BLRT
p
|
VLMR
p
|
1C
|
-667.22
|
1352.44
|
1371.87
|
1343.55
|
|
|
|
2C
|
-657.47
|
1338.94
|
1364.84
|
1327.08
|
0.79
|
0.10
|
0.11
|
3C
|
-647.05
|
1324.10
|
1356.48
|
1303.27
|
0.84
|
<0.00
|
0.10
|
4C
|
-642.44
|
1320.87
|
1359.73
|
1309.08
|
0.81
|
0.08
|
0.25
|
5C
|
-654.52
|
1307.03
|
1369.37
|
1346.28
|
0.74
|
0.34
|
0.50
|
6C
|
-659.46
|
1314.92
|
1366.73
|
1387.20
|
0.65
|
0.56
|
0.46
|
(b) Fitting results of six alternative potential class models for pain trajectory in breast cancer patients undergoing chemotherapy
GMM
|
LL
|
AIC
|
BIC
|
ABIC
|
entropy
|
BLRT
p
|
VLMR
p
|
1C
|
-520.87
|
1059.75
|
1079.72
|
1051.38
|
|
|
|
2C
|
-510.67
|
999.56
|
1056.25
|
1001.02
|
0.80
|
0.12
|
0.56
|
3C
|
-481.48
|
987.96
|
1026.25
|
979.02
|
0.91
|
0.00
|
0.01
|
4C
|
-475.61
|
992.21
|
1027.16
|
979.48
|
0.86
|
0.05
|
0.34
|
5C
|
-466.98
|
997.96
|
1044.57
|
999.44
|
0.77
|
0.07
|
0.45
|
6C
|
-463.46
|
1004.93
|
1428.19
|
1001.67
|
0.72
|
0.13
|
0.55
|
(c) Fitting results of six alternative potential class models for anxiety trajectory in breast cancer patients undergoing chemotherapy
GMM
|
LL
|
AIC
|
BIC
|
ABIC
|
entropy
|
BLRT
p
|
VLMR
p
|
1C
|
-502.42
|
1026.83
|
1051.25
|
1016.61
|
|
|
|
2C
|
-451.46
|
932.92
|
966.21
|
918.92
|
0.90
|
0.00
|
0.00
|
3C
|
-426.47
|
990.94
|
987.11
|
973.27
|
0.84
|
0.04
|
0.43
|
4C
|
-420.11
|
980.22
|
997.27
|
984.84
|
0.81
|
0.07
|
0.48
|
5C
|
-513.61
|
1081.21
|
1041.14
|
1056.11
|
0.77
|
0.60
|
0.56
|
6C
|
-599.40
|
1160.81
|
1129.61
|
1131.99
|
0.73
|
0.71
|
0.59
|
(d) Fitting results of six alternative potential class models for depression trajectory in breast cancer patients undergoing chemotherapy
GMM
|
LL
|
AIC
|
BIC
|
ABIC
|
entropy
|
BLRT
p
|
VLMR
p
|
1C
|
-744.16
|
1508.32
|
1529.91
|
1498.44
|
|
|
|
2C
|
-716.71
|
1459.42
|
1487.49
|
1446.57
|
0.71
|
0.31
|
0.238
|
3C
|
-693.21
|
1418.41
|
1452.95
|
1402.59
|
0.79
|
0.10
|
0.263
|
4C
|
-681.33
|
1440.67
|
1441.69
|
1380.89
|
0.83
|
0.01
|
0.281
|
5C
|
-669.04
|
1465.27
|
1496.21
|
1502.25
|
0.79
|
0.10
|
0.56
|
6C
|
-661.07
|
1472.13
|
1526.10
|
1547.42
|
0.73
|
0.20
|
0.60
|
Notes: GMM=Growth mixture model; LL=log likelihood test; AIC=Akaike Information Criteria; BIC=Bayesian Information Criterion; ABIC=Adjusted Bayesian Information Criterion; BLRT=bootstrapped likelihood ratio test; VLMR=Vuong-Lo-MendelleRubin test.
Pain
Using GMM to analyze fatigue data, A 3-class model was selected because its BIC was the smallest and the entropy value was the largest. In addition, LL and AIC were smaller among the six analyzed groups, and BLRT and VLMR were statistically significant (Table 2). As shown in Figure 2, most patients were classified into the mild-pain group (n = 119, 49.9%). The trend of mild pain and pain-declining groups both decreased. The pain change trend of patients in the fluctuating-pain group (n = 43,17.9%) was first to decline and then to rise, and they had the highest pain level at the end of chemotherapy.
Anxiety
Using GMM to analyze anxiety data, A 2-class model was selected because its BIC was the smallest and the most considerable entropy value. In addition, AIC and ABIC were smaller among the six analyzed groups, and BLRT and VLMR were statistically significant (Table 2). The anxiety change trajectory is shown in Figure 2. The largest group of the patients was classified into the anxiety-declining group (n = 183, 76.5%). Furthermore, the trend of this group was declining. By contrast, the other group's trajectory increased during the whole cycle (n = 56, 23.5%).
Depression
Using GMM to analyze depression data, A 4-class model was selected because BIC and ABIC were the smallest, and the entropy value was the largest. In addition, LL and AIC were smaller among the six analyzed groups, and BLRT was statistically significant (Table 2). The depression change trajectory, as shown in Figure 2, the largest group of the patients was classified into the mild-depression group (n=160, 67.2%). The depression level of this group of patients was low and remained stable throughout the cycle. The next largest class was the depression-declining group (n=37, 15.6%), and their trajectory was slowly decreasing. Both the depression-rising group (n=11, 4.7%) and the severe-depression group (n=30, 12.5%) showed a gradual increase, and the depression level of patients in the severe depression group was the highest.
Differences in Demographic Characteristics
Fatigue
As shown in Table 3, compared with the mild-fatigue group, patients in the moderate- fatigue group had a lower income (<6000 RMB/month), a lower educational level (primary and secondary education) and received anthracycline chemotherapy. Compared between the mild-fatigue and severe-fatigue groups, education levels and family per capita income are no longer significant. However, the chemotherapy scheme included anthracycline is the only predictor of severe degree of fatigue group. The risk of severe fatigue trajectory in patients using anthracycline was 10.63 times higher than those using taxanes.
Pain
Compared with the mild pain group, the older the patients were, the less likely they developed the pain trajectory into the pain-declining group. The solitary patients were more likely to develop into the pain-declining group. Patients treated with anthracyclines were less likely to develop a fluctuating-pain trajectory (Table 3).
Anxiety
Compared with the anxiety-declining group, the older the patients were, the less likely they developed into the anxiety-rising group. On the contrary, the anxiety trajectory of pre-menopausal patients with regular monthly menstruation is more likely to develop into increased anxiety; regression analysis also confirmed that the disease stage has a predictive effect, and patients diagnosed with stage II breast cancer have lower anxiety levels (Table 3).
Depression
Compared with the mild-depression group, the change trajectory of depression in the solitary patients was more likely to develop into the depression-rising group. In addition, the older the patients were, the less likely they were to develop into severe depression (Table 3).
Table 3
Multinational logistic regression of predictors of four symptoms
Trajectories
|
Predictors
|
OR
|
95%CI for OR
|
P
|
Moderate-fatigue group
|
Education
|
|
|
|
Primary school
|
1.08
|
0.39-2.99
|
0.01
|
Junior high school
|
1.76
|
0.13-0.81
|
0.04
|
Per capita monthly household income
|
|
|
|
≤3000 RMB
|
11.34
|
1.32-13.95
|
0.00
|
3001-6000 RMB
|
10.45
|
1.24-14.67
|
0.01
|
Chemotherapy medications
|
|
|
|
Anthracycline
|
1.78
|
0.52-1.94
|
0.02
|
Severe-fatigue group
|
Chemotherapy medications
|
|
|
|
Anthracycline
|
10.63
|
1.34-20.16
|
0.01
|
Pain-declining group
|
Age
|
0.79
|
0.69-1.69
|
0.04
|
Mode of residence
|
|
|
|
Living alone
|
8.48
|
0.61-118.72
|
0.02
|
Fluctuating-pain group
|
Chemotherapy medications
|
|
|
|
Anthracycline
|
0.01
|
0.00-0.20
|
0.02
|
Anxiety-rising group
|
Age
|
0.91
|
0.63-156.69
|
0.03
|
Menstrual Cycle
|
|
|
|
Not menopausal, with regular monthly periods
|
1.44
|
1.08-311.30
|
0.04
|
Disease stage
|
|
|
|
Ⅱ
|
0.32
|
0.15-16.49
|
0.04
|
Depression-rising group
|
Mode of residence
|
|
|
|
Living alone
|
10.04
|
0.56-1734.73
|
0.04
|
Severe-depression group
|
Age
|
0.86
|
0.76-0.96
|
0.01
|