No abstract

The optimal threshold values that were determined represent a maximized test sensitivity and specificity and are not subject to any user bias. When applied to the datasets that we studied, our results suggest the use of patient specific QA as a safety tool that can effectively prevent large errors (e.g., σ > 3 mm) as opposed to a tool to improve the quality of IMRT delivery.

PURPOSE Pain, fatigue, and distress are common among patients with cancer but are often underassessed and undertreated. We examine the prevalence of pain, fatigue, and emotional distress among patients with cancer, as well as patient perceptions of the symptom care they received. PATIENTS AND METHODS Seventeen Commission on Cancer–accredited cancer centers across the United States sampled patients with local/regional breast (82%) or colon (18%) cancer. We received 2,487 completed surveys (61% response rate). RESULTS Of patients, 76%, 78%, and 59% reported talking to a clinician about pain, fatigue, and distress, respectively, and 70%, 61%, and 54% reported receiving advice. Sixty-one percent of patients experienced pain, 74% fatigue, and 46% distress. Among those patients experiencing each symptom, 58% reported getting the help they wanted for pain, 40% for fatigue, and 45% for distress. Multilevel logistic regression models revealed that patients experiencing symptoms were significantly more likely to have talked about and received advice on coping with these symptoms. In addition, patients who were receiving or recently completed curative treatment reported more symptoms and better symptom care than did those who were further in time from curative treatment. CONCLUSION In our sample, 30% to 50% of patients with cancer in community cancer centers did not report discussing, getting advice, or receiving desired help for pain, fatigue, or emotional distress. This finding suggests that there is room for improvement in the management of these three common cancer-related symptoms. Higher proportions of talk and advice among those experiencing symptoms imply that many discussions may be patient initiated. Lower rates of talk and advice among those who are further in time from treatment suggest the need for more assessment among longer-term survivors, many of whom continue to experience these symptoms. These findings seem to be especially important given the high prevalence of these symptoms in our sample.

No abstract

ImportanceEach year, the National Cancer Database (NCDB) collects and analyzes data used in reports to support research, quality measures, and Commission on Cancer program accreditation. Because data models used to generate these reports have been historically stable, year-to-year variances have been attributed to changes within the cancer program rather than data modeling. Cancer submissions in 2020 were anticipated to be significantly different from prior years because of the COVID-19 pandemic. This study involved a validation analysis of the variances in observed to expected 2020 NCDB cancer data in comparison with 2019 and 2018.ObservationsThe NCDB captured a total of 1 223 221 overall cancer cases in 2020, a decrease of 14.4% (Δ = −206 099) compared with 2019. The early months of the COVID-19 pandemic (March-May 2020) coincided with a nadir of cancer cases in April 2020 that did not recover to overall prepandemic levels through the remainder of 2020. In the early months of the COVID-19 pandemic, the proportion of early-stage disease decreased sharply overall, while the proportion of late-stage disease increased. However, differences in observed to expected stage distribution in 2020 varied by primary disease site. Statistically significant differences in the overall observed to expected proportions of race and ethnicity, sex, insurance type, geographic location, education, and income were identified, but consistent patterns were not evident.Conclusions and RelevanceHistorically stable NCDB data models used for research, administrative, and quality improvement purposes were disrupted during the first year of the COVID-19 pandemic. NCDB data users will need to carefully interpret disease- and program-specific findings for years to come to account for pandemic year aberrations when running models that include 2020.