PURPOSE We aimed to characterize long-term quality of life (QOL) trajectories among patients with breast cancer treated with adjuvant chemotherapy and to identify related patterns of health behaviors. METHODS Female stage I-III breast cancer patients receiving chemotherapy in CANTO (CANcer TOxicity; ClinicalTrials.gov identifier: NCT01993498 ) were included. Trajectories of QOL (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–C30 Summary Score) and associations with trajectory group membership were identified by iterative estimations of group-based trajectory models and multivariable multinomial logistic regression, respectively. RESULTS Four trajectory groups were identified (N = 4,131): excellent (51.7%), very good (31.7%), deteriorating (10.0%), and poor (6.6%) QOL. The deteriorating trajectory group reported fairly good baseline QOL (mean [95% CI], 78.3/100 [76.2 to 80.5]), which significantly worsened at year-1 (58.1/100 [56.4 to 59.9]) and never recovered to pretreatment values through year-4 (61.1/100 [59.0 to 63.3]) postdiagnosis. Healthy behaviors were associated with better performing trajectory groups. Obesity (adjusted odds ratio [aOR] v lean, 1.51 [95% CI, 1.28 to 1.79]; P < .0001) and current smoking (aOR v never, 1.52 [95% CI, 1.27 to 1.82]; P < .0001) at diagnosis were associated with membership to the deteriorating group, which was also characterized by a higher prevalence of patients with excess body weight and insufficient physical activity through year-4 and by frequent exposure to tobacco smoking during chemotherapy. Additional factors associated with membership to the deteriorating group included younger age (aOR, 1-year decrement 1.01 [95% CI, 1.01 to 1.02]; P = .043), comorbidities (aOR v no, 1.22 [95% CI, 1.06 to 1.40]; P = .005), lower income (aOR v wealthier households, 1.21 [95% CI, 1.07 to 1.37]; P = .002), and endocrine therapy (aOR v no, 1.14 [95% CI, 1.01 to 1.30]; P = .047). CONCLUSION This latent-class analysis identified some patients with upfront poor QOL and a high-risk cluster with severe, persistent postchemotherapy QOL deterioration. Screening relevant patient-level characteristics may inform tailored interventions to mitigate the detrimental impact of chemotherapy and preserve QOL, including early addressal of behavioral concerns and provision of healthy lifestyle support programs.

PURPOSE Fatigue is common and troublesome among breast cancer survivors; however, limited tools exist to predict its risk. PATIENTS AND METHODS Participants with stage I-III breast cancer were prospectively included from CANTO (ClinicalTrials.gov identifier: NCT01993498 ), collecting longitudinal data at diagnosis (before the initiation of any cancer treatment) and 1 (T1), 2 (T2), and 4 (T3) years after diagnosis. The main outcome was severe global fatigue at T2 (score ≥ 40/100, European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-C30). Analyses at T3 were exploratory. Secondary outcomes included physical, emotional, and cognitive fatigue (EORTC Quality of Life Questionnaire-FA12). Multivariable logistic regression models retained associations with severe fatigue by bootstrapped Augmented Backward Elimination. Validation methods included 10-fold internal cross-validation, overoptimism-corrected area under the receiver operating characteristic curves, and external validation. RESULTS Among 5,640, 5,000, and 3,400 patients at T1, T2, and T3, respectively, the prevalence of post-treatment severe global fatigue was 35.6%, 34.0%, and 31.5% in the development cohort. Retained risk factors for severe global fatigue at T2 were severe pretreatment fatigue (adjusted odds ratio v no 3.191 [95% CI, 2.704 to 3.767]); younger age (for 1-year decrement 1.015 [1.009 to 1.022]), higher body mass index (for unit increment 1.025 [1.012 to 1.038]), current smoking behavior ( v never 1.552 [1.291 to 1.866]), worse anxiety ( v noncase 1.265 [1.073 to 1.492]), insomnia (for unit increment 1.005 [1.003 to 1.007]), and pain at diagnosis (for unit increment 1.014 [1.010 to 1.017]), with an area under the receiver operating characteristic curve of 0.73 (95% CI, 0.72 to 0.75). Receipt of hormonal therapy was a risk factor for severe fatigue at T3 ( v no 1.448 [1.165 to 1.799]). Dimension-specific risk factors included body mass index for physical fatigue and emotional distress for emotional and cognitive fatigue. CONCLUSION We propose a predictive model to assess fatigue among breast cancer survivors, within a personalized survivorship care framework. This may help clinicians to provide early management interventions or to correct modifiable risk factors and offer more tailored monitoring and education to patients at risk of severe post-treatment fatigue.

PURPOSE Fatigue is recognized as one of the most burdensome and long-lasting adverse effects of cancer and cancer treatment. We aimed to characterize long-term fatigue trajectories among breast cancer survivors. METHODS We performed a detailed longitudinal analysis of fatigue using a large ongoing national prospective clinical study (CANcer TOxicity, ClinicalTrials.gov identifier: NCT01993498 ) of patients with stage I-III breast cancer treated from 2012 to 2015. Fatigue was assessed at diagnosis and year 1, 2, and 4 postdiagnosis. Baseline clinical, sociodemographic, behavioral, tumor-related, and treatment-related characteristics were available. Trajectories of fatigue and risk factors of trajectory-group membership were identified by iterative estimates of group-based trajectory models. RESULTS Three trajectory groups were identified for severe global fatigue (n = 4,173). Twenty-one percent of patients were in the high-risk group, having risk estimates of severe global fatigue of 94.8% (95% CI, 86.6 to 100.0) at diagnosis and 64.6% (95% CI, 59.2 to 70.1) at year 4; 19% of patients clustered in the deteriorating group with risk estimates of severe global fatigue of 13.8% (95% CI, 6.7 to 20.9) at diagnosis and 64.5% (95% CI, 57.3 to 71.8) at year 4; 60% were in the low-risk group with risk estimates of 3.6% (95% CI, 2.5 to 4.7) at diagnosis and 9.6% (95% CI, 7.5 to 11.7) at year 4. The distinct dimensions of fatigue clustered in different trajectory groups than those identified by severe global fatigue, being differentially affected by sociodemographic, clinical, and treatment-related factors. CONCLUSION Our findings highlight the multidimensional nature of cancer-related fatigue and the complexity of its risk factors. This study helps to identify patients with increased risk of severe fatigue and to inform personalized interventions to ameliorate this problem.