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BackgroundThere is limited research on how the opioid epidemic and consequent risk reduction policies have affected pain management among cancer patients. The purpose of this study was to analyze how the Opioid Safety Initiative (OSI) implemented at the Veterans Health Administration affected opioid prescribing patterns and opioid-related toxicity.MethodsWe performed an interrupted time series analysis of 42 064 opioid-naïve patients treated at the Veterans Health Administration for prostate, lung, breast, and colorectal cancer from 2011 to 2016. Segmented regression was used to evaluate the impact of the OSI on the incidence of any new opioid prescriptions, high-risk prescriptions, persistent use, and pain-related emergency department (ED) visits. We compared the cumulative incidence of adverse opioid events including an opioid-related admission or diagnosis of misuse before and after the OSI. All statistical tests were 2-sided.ResultsThe incidence of new opioid prescriptions was 26.7% (95% confidence interval [CI] = 25.0% to 28.4%) in 2011 and increased to 50.6% (95% CI = 48.3% to 53.0%) by 2013 before OSI implementation (monthly rate of change: +3.3%, 95% CI = 1.3% to 4.2%, P

PurposeMinority race and lower socioeconomic status are associated with lower rates of opioid prescription and undertreatment of pain in multiple noncancer healthcare settings. It is not known whether these differences in opioid prescribing exist among patients undergoing cancer treatment.Methods and materialsThis observational cohort study involved 33,872 opioid-naive patients of age > 65 years undergoing definitive cancer treatment. We compared rates of new opioid prescriptions by race or ethnicity and socioeconomic status controlling for differences in baseline patient, cancer, and treatment factors. To evaluate downstream impacts of opioid prescribing and pain management, we also compared rates of persistent opioid use and pain-related emergency department (ED) visits.ResultsCompared with non-Hispanic White patients, the covariate-adjusted odds of receiving an opioid prescription were 24.9% (95% CI, 16.0 to 33.9, P < .001) lower for non-Hispanic Blacks, 115.0% (84.7 to 150.3, P < .001) higher for Asian-Pacific Islanders, and not statistically different for Hispanics (-1.0 to 14.0, P = .06). There was no significant association between race or ethnicity and persistent opioid use or pain-related ED visits. Patients living in a high-poverty area had higher odds (53.9% [25.4 to 88.8, P < .001]) of developing persistent use and having a pain-related ED visit (39.4% [16.4 to 66.9, P < .001]).ConclusionFor older patients with cancer, rates of opioid prescriptions and pain-related outcomes significantly differed by race and area-level poverty. Non-Hispanic Black patients were associated with a significantly decreased likelihood of receiving an opioid prescription. Patients from high-poverty areas were more likely to develop persistent opioid use and have a pain-related ED visit.

BackgroundAlthough opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse.MethodsWithin a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique.ResultsThe rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78).ConclusionThis study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients.

PurposeOlder patients undergoing radiation therapy (RT) for pelvic malignancies are at increased risk for pelvic fracture, which is associated with significant morbidity and mortality. RT techniques such as brachytherapy or intensity modulated RT (IMRT) allow for more conformal dose distributions, but it is not known whether the risk for pelvic fracture varies by RT modality.Methods and materialsThis observational cohort study involved 28,354 patients ≥65 years old, treated with RT for pelvic malignancies. We evaluated the relative risk of pelvic fracture by type of RT when accounting for baseline factors. To test for nonspecific effects, we also evaluated risk of nonpelvic fractures in the same population.ResultsThe 5-year incidence of pelvic fractures was 12.7% (95% confidence interval [CI], 11.6%-13.8%), 11.8% (10.8%-12.8%), and 3.7% (3.4%-4.0%) for patients with gastrointestinal, gynecologic, and prostate cancer, respectively. On multivariable analysis, being treated with IMRT (hazard ratio, 0.85; 95% CI, 0.73-0.99) or brachytherapy therapy alone (hazard ratio, 0.43; 95% CI, 0.34-0.54) was associated with a reduced hazard for pelvic fractures compared with 3D conformal radiation therapy in female patients. In contrast, there was no association with RT modality and the hazard for nonpelvic fractures among females. There was no significant association between pelvic fractures and IMRT or brachytherapy for male patients. White race, advanced age, and higher comorbidity were associated with an increased hazard for pelvic fracture.ConclusionsIMRT and brachytherapy were associated with a reduced risk of pelvic fractures in older women undergoing RT for pelvic malignancies. Pelvic insufficiency fracture risk should be considered when treating with pelvic RT.

PurposePrevious studies indicate that the benefit of therapy depends on patients' risk for cancer recurrence relative to noncancer mortality (ω ratio). We sought to test the hypothesis that patients with head and neck cancer (HNC) with a higher ω ratio selectively benefit from intensive therapy.Experimental designWe analyzed 2,688 patients with stage III-IVB HNC undergoing primary radiotherapy (RT) with or without systemic therapy on three phase III trials (RTOG 9003, RTOG 0129, and RTOG 0522). We used generalized competing event regression to stratify patients according to ω ratio and compared the effectiveness of intensive therapy as a function of predicted ω ratio (i.e., ω score). Intensive therapy was defined as treatment on an experimental arm with altered fractionation and/or multiagent concurrent systemic therapy. A nomogram was developed to predict patients' ω score on the basis of tumor, demographic, and health factors. Analysis was by intention to treat.ResultsDecreasing age, improved performance status, higher body mass index, node-positive status, P16-negative status, and oral cavity primary predicted a higher ω ratio. Patients with ω score ≥0.80 were more likely to benefit from intensive treatment [5-year overall survival (OS), 70.0% vs. 56.6%; HR of 0.73, 95% confidence interval (CI): 0.57-0.94; P = 0.016] than those with ω score

PurposeGeneralized competing event (GCE) models improve stratification of patients according to their risk of cancer events relative to competing causes of mortality. The potential impact of such methods on clinical trial power and cost, however, is uncertain. We sought to test the hypothesis that GCE models can reduce estimated clinical trial cost in elderly patients with cancer.MethodsPatients with nonmetastatic head and neck (n = 9,677), breast (n = 22,929), or prostate cancer (n = 51,713) were sampled from the SEER-Medicare database. Using multivariable Cox proportional hazards models, we compared risk scores for all-cause mortality (ACM) and cancer-specific mortality (CSM) with GCE-based risk scores for each disease. We applied a cost function to estimate the cost and duration of clinical trials with a primary end point of overall survival in each population and in high-risk subpopulations. We conducted sensitivity analyses to examine model uncertainty.ResultsFor the purpose of enriching subpopulations, GCE models reduced estimated clinical trial cost compared with Cox models of ACM and CSM in all disease sites. The relative cost reductions with GCE models compared with ACM and CSM models, respectively, were -68.4% and -14.4% in prostate cancer, -38.8% and -18.3% in breast cancer, and -17.1% and -4.1% in head and neck cancer. Cost savings in breast and prostate cancers were on the order of millions of dollars. The GCE model also reduced relative clinical trial duration compared with CSM and ACM models for all disease sites. The optimal risk score cutoff for clinical trial enrollment occurred near the top tertile for all disease sites.ConclusionGCE models have significant potential to improve clinical trial efficiency and reduce cost, with a potentially large impact in prostate and breast cancers.

PurposeComorbidity is an independent predictor of mortality and treatment tolerance in head and neck cancer and should be considered with regard to treatment intensification. Multiple previously validated models can be used to evaluate comorbidity and propensity to benefit from intensive treatment, but they have not been directly compared.Materials and methodsAn online tool was developed and used to calculate the Charlson Comorbidity Index (CCI), Adult Comorbidity Evaluation-27 (ACE-27), Cumulative Illness Rating Scale for Geriatrics (CIRS-G), Geriatric 8 (G8), Cancer and Aging Research Group (CARG), and Generalized Competing Event (GCE) scores. To assess interrater variability, five evaluators independently calculated scores on a retrospective cohort of 20 patients. Correlation between models as well as age and performance status were calculated from a cohort of 40 patients.ResultsThe GCE and G8 models had an excellent (intraclass correlation coefficient and Fleiss' kappa ≥ 0.75) degree of interrater agreement. The CCI, ACE-27, CIRS-G, and CARG had a good (intraclass correlation coefficient and Fleiss' kappa 0.6-0.74) degree of interrater agreement. There was statistically significant correlation between models, especially with the CCI, ACE-27, and CIRS-G indices. Increased age was correlated with an increased CCI score and having moderate to severe comorbidity was correlated with the ACE-27 model. Except for the G8 model, the comorbidity indices were not associated with Eastern Cooperative Oncology Group performance status.ConclusionWe developed an online tool to calculate indices of comorbidity in patients with head and neck cancer with a high degree of reproducibility. Comorbidity is not strongly correlated with performance status and should be independently evaluated in patients.

BackgroundPrevious studies have reported conflicting information regarding the prognostic role of p16 in nonoropharyngeal head and neck squamous cell carcinoma (HNSCC).MethodsUsing the US Veterans Affairs database, we analyzed 1448 patients with locoregionally advanced HNSCC and known p16 status diagnosed between 2005 and 2015 and treated with surgery, radiotherapy, or chemoradiotherapy. Tumor p16 status was determined through manual review of pathology reports of primary tumor specimens. Oropharyngeal (n = 1061) or nonoropharyngeal (n = 387; hypopharyngeal, laryngeal, or oral cavity) tumor site was determined from tumor registry data and manually reviewed for accuracy. We used multivariable Cox regression to analyze the effect of p16 status on overall survival (OS), cancer-specific survival (CSS), and competing mortality (CM) for oropharyngeal or nonoropharyngeal tumor sites. All statistical tests were two-sided.ResultsIn multivariable models adjusting for treatment, stage, age, comorbidity, and body mass index, patients with p16-positive tumors had improved OS, CSS, and CM compared with patients with p16-negative tumors in both oropharyngeal (OS: hazard ratio [HR] = 0.53, 95% confidence interval [CI] = 0.40 to 0.71, P < .001; CSS: HR = 0.50, 95% CI = 0.35 to 0.73, P < .001; CM: HR = 0.59, 95% CI = 0.38 to 0.93, P = .02) and nonoropharyngeal primary sites (OS: HR = 0.41, 95% CI = 0.25 to 0.69, P < .001; CSS: HR = 0.37, 95% CI = 0.18 to 0.77, P = .008; CM: HR = 0.46, 95% CI = 0.23 to 0.95, P = .04). The prognostic impact of p16 status did not statistically significantly differ by primary tumor site for OS, CSS, or CM (Pinteraction > .05).ConclusionsOur findings support the hypothesis that p16 has a similar prognostic role in both nonoropharyngeal and oropharyngeal cancer. Consideration should be given to increased testing for p16 in laryngeal, hypopharyngeal, and oral cavity primaries.