Predictive Model for Severe Fatigue After Breast Cancer Diagnosis

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In a study reported in the Journal of Clinical Oncology, Di Meglio et al developed a predictive model for severe fatigue after a diagnosis of breast cancer.

Study Details

The study involved data on patients with stage I to III breast cancer included in the prospective multicenter CANcer TOxicity (CANTO) cohort. Longitudinal data were collected at diagnosis (before initiation of any cancer treatment) and at 1 (T1), 2 (T2), and 4 (T3) years after diagnosis. The main outcome measure was severe global fatigue at T2, defined as a score of ≥ 40/100 on the EORTC Quality of Life Questionnaire-C30 (QLQ-C30).  

Key Findings

Prevalence of severe global fatigue at baseline was 24.3%. Among 5,640, 5,000, and 3,400 patients at T1, T2, and T3, respectively, the prevalence of posttreatment severe global fatigue was 35.6%, 34.0%, and 31.5%, respectively.

The main predictive model for severe global fatigue at T2 included seven factors:

  • Severe pretreatment fatigue (adjusted odds ratio [OR] vs no severe fatigue = 3.19, 95% confidence interval [CI] = 2.70–3.77)
  • Younger age (adjusted OR for 1-year decrement = 1.02, 95% CI = 1.01–1.02)
  • Higher body mass index (adjust OR per 1-unit increment = 1.03, 95% CI = 1.01–1.04)
  • Current smoking (adjusted OR vs never-smoker = 1.55, 95% CI = 1.29–1.87)
  • Worse anxiety at diagnosis (adjusted OR for case vs noncase = 1.27, 95% CI = 1.07–1.49; case = score of 11–21, noncase = score of 0–7 on Hospital Anxiety and Depression Scale)
  • Insomnia at diagnosis (adjusted OR per unit increment on QLQ-C30 = 1.01, 95% CI = 1.00–1.01)
  • Pain at diagnosis (adjusted OR per unit increment on QLQ-C30 = 1.01, 95% CI = 1.01–1.02). 

The receiver operating characteristic area under the curve for the model was 0.73 (95% CI = 0.72–0.75).

In exploratory analysis of severe fatigue at T3, receipt vs no receipt of hormonal therapy was an additional risk factor (adjusted OR = 1.45, 95% CI = 1.17–1.80).

On the EORTC QLQ-FA12, dimension-specific risk factors included body mass index for physical fatigue and emotional distress for emotional and cognitive fatigue.

The investigators concluded, “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.”

Ines Vaz-Luis, MD, PhD, of Institut Gustave Roussy, Villejuif, is the corresponding author for the Journal of Clinical Oncology article.

Disclosure: The study was supported by Conquer Cancer, ASCO Foundation, Rising Tide Foundation for Clinical Cancer Research, Susan G. Komen, Odyssea, Foundation Gustave Roussy, and others. For full disclosures of the study authors, visit

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.