Racial Differences in Over-the-Counter Non-steroidal Anti-inflammatory Drug Use Among Individuals at Risk of Adverse Cardiovascular Events.

PURPOSE: Black Americans are disproportionately affected by adverse cardiovascular events (ACEs). Over-the-counter (OTC) non-steroidal anti-inflammatory drugs (NSAIDs) confer increased risk for ACEs, yet racial differences in the use of these products remain understudied. This study sought to determine racial differences in OTC NSAID and high-potency powdered NSAID (HPP-NSAID) use. METHODS AND MATERIALS: This retrospective analysis examined participants at risk of ACEs (defined as those with self-reported hypertension, diabetes, heart disease, or smoking history ≥ 20 years) from the North Carolina Colon Cancer Study, a population-based case-control study. We used multivariable logistic regression models to assess the independent associations of race with any OTC NSAID use, HPP-NSAID use, and regular use of these products. RESULTS: Of the 1286 participants, 585 (45%) reported Black race and 701 (55%) reported non-Black race. Overall, 665 (52%) reported any OTC NSAID use and 204 (16%) reported HPP-NSAID use. Compared to non-Black individuals, Black individuals were more likely to report both any OTC NSAID use (57% versus 48%) and HPP-NSAID use (22% versus 11%). In multivariable analyses, Black (versus non-Black) race was independently associated with higher odds of both NSAID use (OR 1.4, 95% CI (1.1, 1.8)) and HPP-NSAID use (OR 1.8 (1.3, 2.5)). CONCLUSIONS: Black individuals at risk of ACEs had higher odds of any OTC NSAID and HPP-NSAID use than non-Black individuals, after controlling for pain and socio-economic status. Further research is necessary to identify potential mechanisms driving this increased use.

Local Recurrence Outcomes of Colorectal Cancer Oligometastases Treated With Stereotactic Ablative Radiotherapy.

PURPOSE: The aim of this study was to report local failure (LF) outcomes and associated predictors in patients with oligometastatic colorectal cancer (CRC) treated with stereotactic ablative radiotherapy (SABR). MATERIALS AND METHODS: We retrospectively reviewed patients with CRC metastases to the brain, liver, spine, or lung treated with SABR between 2001 and 2016. Time to LF was summarized using cumulative incidence of LF curves with death as a competing risk. RESULTS: The analysis included a total of 130 patients and 256 lesions. Of the metastases treated, 129 (50%) were brain, 50 (20%) liver, 49 (19%) spine, and 28 (11%) lung. Median gross tumor volume was 24 mL for liver metastases, 2 mL for brain metastases, 4 mL for spine metastases, and 1 mL for lung metastases. The overall 1, 2, and 3-year cumulative incidence of LF rates were 21.6% (16.5, 27.1), 28.2% (22.3, 34.4), and 31.5% (25.2, 38.0), respectively. LF was highest among the liver metastases (1 y: 26.0%, 2 y: 38.5%), followed by spine (1 y: 25.1%, 2 y: 31.1%), brain (1 y: 20%, 2 y: 25.2%), and lung (1 y: 13.7%, 2 y: insufficient data). Metastases from right-sided primary CRC were significantly more likely to have LF (P=0.0146, HR=2.23). Biologically effective dose>70 Gy, defined using a standard linear quadratic model using α/ß ratio of 10 on the individual lesion level, and pre-SABR chemotherapy were also significant predictors of LF (P= 0.0009 and 0.018, respectively). CONCLUSIONS: CRC metastases treated with SABR had significantly higher rates of LF if they originated from right-sided primary CRC, compared with left-sided. Liver metastases had the highest rates of LF compared with other metastatic sites. Thus, CRC liver metastases and metastases from right-sided CRC may benefit from more aggressive radiotherapy.

Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant.

PURPOSE: Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered. METHODS AND MATERIALS: The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion. RESULTS: Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable. CONCLUSIONS: Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review.

Automated model versus treating physician for predicting survival time of patients with metastatic cancer.

OBJECTIVE: Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model. MATERIALS AND METHODS: A machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015-2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots. RESULTS: The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63-0.81), 0.77 (0.73-0.81), and 0.68 (0.65-0.71), respectively. CONCLUSIONS: The machine learning model's predictions were more accurate than those of the treating physician or a traditional model.

"Pain is Subjective": A Mixed-Methods Study of Provider Attitudes and Practices Regarding Pain Management in Sickle Cell Disease Across Three Countries.

CONTEXT: Sickle cell disease (SCD), an autosomal recessive blood disorder, affects millions of people worldwide. Approximately 80% of all cases are located in Africa. OBJECTIVES: This cross-national, interdisciplinary, collaborative study investigated provider attitudes about, and practices for, managing (assessing and treating) SCD pain. METHODS: We conducted 111 quantitative surveys and 52 semistructured interviews with health-care providers caring for adults and/or children with SCD in Cameroon, Jamaica, and the U.S. RESULTS: Applying Haywood's scale for assessing SCD provider attitudes, the Jamaica site scored lower on "Negative Attitudes" than the Cameroonian and U.S. sites (P = 0.03 and <0.001, respectively). Providers at the U.S. site scored lower on "Positive Attitudes" than other sites (P < 0.001). "Red Flag" scores at the Cameroon sites were lower than at other sites (P < 0.001). Qualitative results across all three sites describe the current practices for SCD pain management, as well as the challenges surrounding management for health providers, including pain subjectivity, patient-provider and parent-provider relationships, resource availability, perceptions of drug-seeking behavior, and adherence. Providers also spontaneously offered solutions to reported challenges. CONCLUSION: Overall, findings reveal that SCD provider attitudes toward their patients differed across sites, yet at all three sites, treating SCD pain is multidimensional.

Local control and toxicity outcomes of stereotactic radiosurgery for spinal metastases of gastrointestinal origin.

OBJECTIVE: Colorectal cancer (CRC) and other gastrointestinal (GI) cancers are believed to have greater radioresistance than other histologies. The authors report local control and toxicity outcomes of stereotactic radiosurgery (SRS) to spinal metastases from GI primary cancers. METHODS: A retrospective single-center review was conducted of patients with spinal metastases from GI primary cancers treated with SRS from 2004 to 2017. Patient demographics and lesion characteristics were summarized using medians, interquartile ranges (IQRs), and proportions. Local failure (LF) was estimated using the cumulative incidence function adjusted for the competing risk of death and compared using Gray's test for equality. Multivariable analyses were conducted using Cox proportional hazard models, adjusting for death as a competing risk, on a per-lesion basis. Patients were stratified in the Cox model to account for repeated measures for clustered outcomes. Median survival was calculated using the Kaplan-Meier method. RESULTS: A total of 74 patients with 114 spine lesions were included in our analysis. The median age of the cohort was 62 years (IQR 53-70 years). Histologies included CRC (46%), hepatocellular carcinoma (19%), neuroendocrine carcinoma (13%), pancreatic carcinoma (12%), and other (10%). The 1- and 2-year cumulative incidence rates of LF were 24% (95% confidence interval [CI] 16%-33%) and 32% (95% CI 23%-42%), respectively. Univariable analysis revealed that older age (p = 0.015), right-sided primary CRCs (p = 0.038), and single fraction equivalent dose (SFED; α/ß = 10) < 20 Gy (p = 0.004) were associated with higher rates of LF. The 1-year cumulative incidence rates of LF for SFED < 20 Gy10 versus SFED ≥ 20 Gy10 were 35% and 7%, respectively. After controlling for gross tumor volume and prior radiation therapy to the lesion, SFED < 20 Gy10 remained independently associated with worse LF (hazard ratio 2.92, 95% CI 1.24-6.89, p = 0.014). Toxicities were minimal, with pain flare observed in 6 patients (8%) and 15 vertebral compression fractures (13%). CONCLUSIONS: Spinal metastases from GI primary cancers have high rates of LF with SRS at a lower dose. This study found that SRS dose is a significant predictor of failure and that prescribed SFED ≥ 20 Gy10 (biological equivalent dose ≥ 60 Gy10) is associated with superior local control.

Predicting Survival for Patients With Metastatic Disease.

PURPOSE: This prospective study aimed to determine the accuracy of radiation oncologists in predicting the survival of patients with metastatic disease receiving radiation therapy and to understand factors associated with their accuracy. METHODS AND MATERIALS: This single-institution study surveyed 22 attending radiation oncologists to estimate patient survival. Survival predictions were defined as accurate if the observed survival (OS) was within the correct survival prediction category (0-6 months, >6-12 months, >12-24 months, and >24 months). The physicians made survival estimates for each course of radiation, yielding 877 analyzable predictions for 689 unique patients. Data analysis included Stuart's Tau C, logistic regression models, ordinal logistic regression models, and stepwise selection to examine variable interactions. RESULTS: Of the 877 radiation oncologists' predictions, 39.7% were accurate, 26.5% were underestimations, and 33.9% were overestimations. Stuart's Tau C showed low correlation between OS and survival estimates (0.3499), consistent with the inaccuracy reported in the literature. However, results showed less systematic overprediction than reported in the literature. Karnofsky performance status was the most significant predictor of accuracy, with greater accuracy for patients with shorter OS. Estimates were also more accurate for patients with lower Karnofsky performance status. Accuracy by patient age varied by primary site and race. Physician years of experience did not correlate with accuracy. CONCLUSIONS: The sampled radiation oncologists have a 40% accuracy in predicting patient survival. Future investigation should explore how survival estimates influence treatment decisions and how to improve survival prediction accuracy.