Development and Validation of a Predictive Model of Severe Fatigue After Breast Cancer Diagnosis: Toward a Personalized Framework in Survivorship Care

Abstract

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.

Dynamics of Long-Term Patient-Reported Quality of Life and Health Behaviors After Adjuvant Breast Cancer Chemotherapy

“…81,82 In the context of recently consolidated strategies of endocrine therapy escalation, [83][84][85] particular attention should be given to specific subgroups, such as younger women, who seem to be at higher risk of QOL deterioration and persistent symptoms. 86 The strengths of this study include its prospective, longitudinal design, and a large and heterogeneous sample. We analyzed a single, higher-order QOL outcome measure that summarizes multiple scales into a multidimensional response profile, thus avoiding multiple comparisons.…”

“… 81 , 82 In the context of recently consolidated strategies of endocrine therapy escalation, 83 - 85 particular attention should be given to specific subgroups, such as younger women, who seem to be at higher risk of QOL deterioration and persistent symptoms. 86 …”

Long-Term Longitudinal Patterns of Patient-Reported Fatigue After Breast Cancer: A Group-Based Trajectory Analysis

“…Previous literature has suggested that the patient population that is at higher risk of cancer-related fatigue includes (1) those who are more fragile at the time of diagnosis, including being young, belonging to lower socioeconomic class, not partnered, having higher BMI, comorbidities, being a smoker, presenting heavier psychologic distress (depressive and anxiety symptoms), and concomitant symptom burden and (2) those who receive specific classes of treatment such as chemotherapy and endocrine therapy. [4][5][6][7][8][9]26 Some previous studies have suggested that there are clusters of patients who experience distinct trajectories of fatigue. 27 For example, Bower et al examined post-treatment fatigue trajectories in 191 women with early-stage BC.…”

Oncological Treatment-Related Fatigue in Oncogeriatrics: A Scoping Review

“…In addition, the currently ongoing CANTO longitudinal cohort has led to the validation of a predictive model of severe fatigue; severe fatigue was defined as a fatigue score ≥40% according to the European Organisation for Research and Treatment of Cancer quality-of-life core questionnaire with 30 items (EORTC QLQ-C30). In this model, retained risk factors for severe fatigue at 2 years after diagnosis were pretreatment fatigue, younger age, higher body mass index, current smoking behavior, worse anxiety, insomnia, and pain at diagnosis; hormonal treatment was found to be a risk factor for severe fatigue 4 years after diagnosis [25].…”