Purpose: Prolonged and disabling fatigue is prevalent after cancer treatment, but the early natural history of cancer-related fatigue (CRF) has not been systematically examined to document consistent presence of symptoms. Hence, relationships to cancer, surgery, and adjuvant therapy are unclear.
Patients And Methods: A prospective cohort study of women receiving adjuvant treatment for early-stage breast cancer was conducted. Women (n = 218) were enrolled after surgery and observed at end treatment and at 1, 3, 6, 9, and 12 months as well as 5 years. Structured interviews and self-report questionnaires were used to record physical and psychologic health as well as disability and health care utilization. Patients with CRF persisting for 6 months were assessed to exclude alternative medical and psychiatric causes of fatigue. Predictors of persistent fatigue, mood disturbance, and health care utilization were sought by logistic regression.
Results: The case rate for CRF was 24% (n = 51) postsurgery and 31% (n = 69) at end of treatment; it became persistent in 11% (n = 24) at 6 months and 6% (n = 12) at 12 months. At each time point, approximately one third of the patients had comorbid mood disturbance. Persistent CRF was predicted by tumor size but not demographic, psychologic, surgical, or hematologic parameters. CRF was associated with significant disability and health care utilization.
Conclusion: CRF is common but generally runs a self-limiting course. Much of the previously reported high rates of persistent CRF may be attributable to factors unrelated to the cancer or its treatment.
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http://dx.doi.org/10.1200/JCO.2011.34.6148 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFAnnu Rev Clin Psychol
January 2025
3Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
Most people with mental health needs cannot access treatment; among those who do, many access services only once. Accordingly, single-session interventions (SSIs) may help bridge the treatment gap. We conducted the first umbrella review synthesizing research on SSIs for mental health problems and service engagement in youth and adults.
View Article and Find Full Text PDFACS Sens
January 2025
Department of Physics and Astronomy, Franklin College of Arts and Sciences, The University of Georgia, Athens, Georgia 30602, United States.
Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
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