Purpose Understanding the etiology of cancer-related fatigue (CRF) is critical to identify targets to develop therapies to reduce CRF burden. The goal of this systematic review was to expand on the initial work by the National Cancer Institute CRF Working Group to understand the state of the science of the biology of CRF. Specifically, to evaluate studies that examined the relationships between biomarkers and CRF, and to develop an etiologic model of CRF to guide researchers on pathways to explore or therapeutic targets to investigate. Methods This review was completed by the Multinational Association of Supportive Care in Cancer Fatigue Study Group – Biomarker Working Group. The initial search used three terms (biomarkers, fatigue, cancer), which yielded 11,129 articles. After removing duplicates, 7,175 articles remained. Titles were assessed for the keywords, “cancer” and “fatigue” resulting in 3,811 articles. Articles published before 2010 and those with samples <50 were excluded, leaving 75 articles for full-text review. Of the 75 articles, 25 were further excluded for not investigating the associations of biomarkers and CRF. Results Of the 47 articles reviewed, 25 were cross-sectional and 22 were longitudinal studies. Less than half (44%) were published recently (2010-2013). Almost half (46%) enrolled breast cancer participants. A majority of studies assessed fatigue using self-report questionnaires, and only two studies used clinical parameters to measure fatigue. Conclusions The findings from this review suggest that CRF is linked to immune/inflammatory, metabolic, neuroendocrine, and genetic biomarkers. We also identified gaps in knowledge and made recommendations for future research.

Background Attrition is common among supportive/palliative oncology clinical trials. Few studies have documented the reasons and predictors for dropout. We aimed to determine the rate, reasons and factors associated with attrition both before reaching the primary endpoint and the end of study. Methods We conducted a review of all prospective interventional supportive/palliative oncology trials in the Department of Palliative Care and Rehabilitation Medicine at MD Anderson Cancer Center between 1999–2011. Patient and study characteristics and attrition data were extracted. Results 1214 patients were included in 18 clinical trials. The median age was 60, 41% had performance status ≥3, median fatigue 7/10 and median dyspnea 2/10. The attrition rate was 26% (95% confidence interval [CI] 23%-28%) for the primary endpoint and 44% (95% CI 41%-47%) for the end of study. Common reasons for primary endpoint dropout were symptom burden (21%), patient preference (15%), hospitalization (10%) and death (6%). Primary endpoint attrition was associated with higher baseline intensity of fatigue (odds ratio [OR]=1.10 per point, P=0.01) and longer study duration (P=0.04). End of study attrition was associated with higher baseline levels of dyspnea (OR=1.06, P=0.01), fatigue (OR=1.08, P=0.01), Hispanic race (OR=1.87, P=0.002), higher education (P=0.02), longer study duration (P=0.01) and outpatient studies (P=0.05). Conclusions The attrition rate was high in supportive/palliative oncology clinical trials, and was associated with various patient characteristics and high baseline symptom burden. These findings have implications for future clinical trial design including eligibility criteria and sample size calculation.

Background. The physical signs of impending death have not been well characterized in cancer patients. A better understanding of these signs may improve the ability of clinicians to diagnose impending death. We examined the frequency and onset of 10 bedside physical signs and their diagnostic performance for impending death. Methods. We systematically documented 10 physical signs every 12 hours from admission to death or discharge in 357 consecutive patients with advanced cancer admitted to two acute palliative care units. We examined the frequency and median onset of each sign from death backward and calculated their likelihood ratios (LRs) associated with death within 3 days. Results. In total, 203 of 357 patients (52 of 151 in the U.S., 151 of 206 in Brazil) died. Decreased level of consciousness, Palliative Performance Scale #20%, and dysphagia of liquids appeared at high frequency and .3 days before death and had

Dexamethasone is more effective than placebo in improving CRF and quality of life in patients with advanced cancer.

Background The Edmonton Symptom Assessment Scale (ESAS) is widely used for symptom assessment in the clinical and research settings. We used the sensitivity-specificity approach to identify the minimal clinically important difference (MCID) for improvement and deterioration for each of the 10 ESAS symptoms. Methods This multicenter, prospective, longitudinal study enrolled advanced cancer patients. ESAS was measured at first clinic visit and a second visit 3 weeks later. For each symptom, we assessed Patient's Global Impression (“better”, “about the same”, or “worse”) at the second visit as the external criterion, and determined the MCID based on the optimal cutoff in receiver-operating characteristic (ROC) curve. We conducted sensitivity analysis by estimating MCIDs using other approaches. Results Among the 796 participants, the median duration between the 2 study visits was 21 days (interquartile range 18-28 days). The area under the ROC curve varied between 0.70-0.87, suggesting good responsiveness. For all 10 symptoms, the optimal cutoff was ≥1 point for improvement and ≤−1 point for deterioration, with sensitivities of 59%-85% and specificities of 69%-85%. Using other approaches, the MCIDs varied between 0.8 and 2.2 for improvement and between −0.8 and −2.3 for deterioration in within-patient analysis, between 1.2 and 1.6 with the ½ standard deviation approach, and between 1.3 and 1.7 with the standard error of measurement approach. Conclusions ESAS was responsive to change. The optimal cutoffs were ≥1 point for improvement and ≤−1 point for deterioration for each of the 10 symptoms. Our findings have implications for sample size calculations and response determination.