Feasibility of risk assessment for breast cancer molecular subtypes.

PURPOSE: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination. METHODS: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype. RESULTS: There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI. CONCLUSION: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.

Behavioural economic interventions to embed palliative care in community oncology (BE-EPIC): study protocol for the BE-EPIC randomised controlled trial.

INTRODUCTION: Palliative care (PC) is a medical specialty focusing on providing relief from the symptoms and stress of serious illnesses such as cancer. Early outpatient specialty PC concurrent with cancer-directed treatment improves quality of life and symptom burden, decreases aggressive end-of-life care and is an evidence-based practice endorsed by national guidelines. However, nearly half of patients with advanced cancer do not receive specialty PC prior to dying. The objective of this study is to test the impact of an oncologist-directed default PC referral orders on rates of PC utilisation and patient quality of life. METHODS AND ANALYSIS: This single-centre two-arm pragmatic randomised trial randomises four clinician-led pods, caring for approximately 250 patients who meet guideline-based criteria for PC referral, in a 1:1 fashion into a control or intervention arm. Intervention oncologists receive a nudge consisting of an electronic health record message indicating a patient has a default pended order for PC. Intervention oncologists are given an opportunity to opt out of referral to PC. Oncologists in pods randomised to the control arm will receive no intervention beyond usual practice. The primary outcome is completed PC visits within 12 weeks. Secondary outcomes are change in quality of life and absolute quality of life scores between the two arms. ETHICS AND DISSEMINATION: This study has been approved by the Institutional Review Board at the University of Pennsylvania. Study results will be disseminated in peer-reviewed journals and scientific conferences using methods that describe the results in ways that key stakeholders can best understand and implement. TRIAL REGISTRATION NUMBER: NCT05365997.

Precision prophylaxis: Identifying the optimal timing for risk-reducing salpingo-oophorectomy based on type of BRCA1 and BRCA2 cluster region mutations.

OBJECTIVES: Current risk-reducing salpingo-oophorectomy (RRSO) guidelines for individuals with BRCA1/2 mutations do not account for risk variability due to BRCA1/2 cluster region mutations that are associated with varying risks for the development of breast and ovarian cancer. We assessed whether current recommendations are appropriate for individual patients considering mutation-specific risks. METHODS: Using a hypothetical cohort of patients with BRCA1/2 mutations, we constructed Markov models allowing for the estimation of mean life expectancy based upon BRCA1/2 mutation, the presence of a cluster region mutation (Ovarian Cancer Cluster Region (OCCR), Breast Cancer Cluster Region (BCCR), or non-BCCR/OCCR), age at time of BRCA1/2 diagnosis (20-65), and age at time of RRSO (21-80). RESULTS: For all BRCA1/2 mutation types, the optimal strategy was to undergo RRSO as early as possible. For BRCA1/2 carriers who delayed RRSO or who were identified with a mutation later in life, the OCCR mutation tended to be associated with lower life expectancy estimates than the BCCR and non-BCCR/OCCR mutations. Minimal delays in RRSO (i.e., neighboring 5-year intervals) were associated with minor losses in life expectancy. Variables associated with greatest impact on life expectancy included ovarian cancer risk after RRSO, breast cancer mortality rate, non-cancer mortality associated with RRSO, and breast cancer stage distribution. CONCLUSIONS: BRCA1/2 cluster regions may provide more precise estimates of life expectancy in counselling and shared decision-making. The most appropriate timing for RRSO is a complex decision and must be individualized for each patient.

Extreme-value sampling design is cost-beneficial only with a valid statistical approach for exposure-secondary outcome association analyses.

In epidemiology cohort studies, exposure data are collected in sub-studies based on a primary outcome (PO) of interest, as with the extreme-value sampling design (EVSD), to investigate their correlation. Secondary outcomes (SOs) data are also readily available, enabling researchers to assess the correlations between the exposure and the SOs. However, when the EVSD is used, the data for SOs are not representative samples of a general population; thus, many commonly used statistical methods, such as the generalized linear model (GLM), are not valid. A prospective likelihood method has been developed to associate SOs with single-nucleotide polymorphisms under an extreme phenotype sequencing design. In this paper, we describe the application of the prospective likelihood method (STEVSD) to exposure-SO association analysis under an EVSD. We undertook extensive simulations to assess the performance of the STEVSD method in associating binary and continuous exposures with SOs, comparing it to the simple GLM method that ignores the EVSD. To demonstrate the cost-benefit of the STEVSD method, we also mimicked the design of two new retrospective studies, as would be done in actual practice, based on the PO of interest, which was the same as the SO in the EVSD study. We then analyzed these data by using the GLM method and compared its power to that of the STEVSD method. We demonstrated the usefulness of the STEVSD method by applying it to a benign ethnic neutropenia dataset. Our results indicate that the STEVSD method can control type I error well, whereas the GLM method cannot do so owing to its ignorance of EVSD, and that the STEVSD method is cost-effective because it has statistical power similar to that of two new retrospective studies that require collecting new exposure data for selected individuals.

A maximum likelihood approach to electronic health record phenotyping using positive and unlabeled patients.

OBJECTIVE: Phenotyping patients using electronic health record (EHR) data conventionally requires labeled cases and controls. Assigning labels requires manual medical chart review and therefore is labor intensive. For some phenotypes, identifying gold-standard controls is prohibitive. We developed an accurate EHR phenotyping approach that does not require labeled controls. MATERIALS AND METHODS: Our framework relies on a random subset of cases, which can be specified using an anchor variable that has excellent positive predictive value and sensitivity independent of predictors. We proposed a maximum likelihood approach that efficiently leverages data from the specified cases and unlabeled patients to develop logistic regression phenotyping models, and compare model performance with existing algorithms. RESULTS: Our method outperformed the existing algorithms on predictive accuracy in Monte Carlo simulation studies, application to identify hypertension patients with hypokalemia requiring oral supplementation using a simulated anchor, and application to identify primary aldosteronism patients using real-world cases and anchor variables. Our method additionally generated consistent estimates of 2 important parameters, phenotype prevalence and the proportion of true cases that are labeled. DISCUSSION: Upon identification of an anchor variable that is scalable and transferable to different practices, our approach should facilitate development of scalable, transferable, and practice-specific phenotyping models. CONCLUSIONS: Our proposed approach enables accurate semiautomated EHR phenotyping with minimal manual labeling and therefore should greatly facilitate EHR clinical decision support and research.

Impact of payer status on survival in parotid malignancy.

OBJECTIVE: In the setting of current national healthcare reform, it becomes especially relevant to understand the current state of healthcare disparities with regards to insurance status. To determine the impact of payer status on survival in parotid malignancy, we utilized the National Cancer Database (NCDB). STUDY DESIGN: Retrospective database review. SETTING: National Cancer Database (2004-2012). SUBJECTS AND METHODS: The NCDB was queried for cases of primary malignancy of the parotid gland between 2004 and 2012. The impact of payer status on overall survival was evaluated, as well as the relationship of insurance status with patient and tumor variables. RESULTS: 15,815 cases met inclusion criteria. A majority had private insurance (47.8%), followed by Medicare (40.9%), Medicaid (5.0%), uninsured (3.2%) and other government sources (1.3%). Medicare patients had the lowest 5 and 10-year survival rates (50.7% (95% CI [49.3-52.1]) and 27.8% (95% CI [25.0-30.9]), respectively). On multivariable analysis, uninsured, Medicare, and Medicaid patients had worse overall survival than the privately insured (HR 1.42, 95% CI [1.17-1.74]; HR 1.29, 95% CI [1.17-1.42]; HR 1.36, 95% CI [1.13-1.62], respectively). Uninsured and Medicaid patients were more likely than the privately insured to present with advanced stage disease, nodal metastasis and longer times to treatment following diagnosis. CONCLUSION: In parotid malignancy, uninsured, Medicaid, and Medicare patients have worse survival outcomes compared to those with private insurance. Uninsured and Medicaid patients also present with more advanced stage disease and have increased wait times before definitive treatment is initiated.

Geographically Derived Socioeconomic Factors to Improve Risk Prediction in Patients Having Aortic Valve Replacement.

Socioeconomic status (SES) has been associated with adverse outcomes after cardiac surgery, but is not included in commonly applied risk adjustment models. This study evaluates whether inclusion of SES improves aortic valve replacement (AVR) risk prediction models, as this is the most common elective operation performed at our institution during the study period. All patients who underwent AVR at a single institution from 2005 to 2015 were evaluated. SES measures included unemployment, poverty, household income, home value, educational attainment, housing density, and a validated SES index score. The risk scores for mortality, complications, and increased length of stay were generated using models published by the Society for Thoracic Surgeons. Univariate models were fitted for each SES covariate and multivariable models for mortality, any complication, and prolonged length of stay (PLOS). A total of 1,386 patients underwent AVR with a 2.7% mortality, 15.1% complication rate, and 9.7% PLOS. In univariate models, higher education was associated with decreased mortality (odds ratio [OR] 0.96, p = 0.04) and complications (OR 0.97, p <0.01). Poverty was associated with increased length of stay (OR 1.02, p = 0.02). In the multivariable models, the inclusion of SES covariates increased the area under the curve for mortality (0.735 to 0.750, p = 0.14), for any complications (0.663 to 0.680, p <0.01), and for PLOS (0.749 to 0.751, p = 0.12). The inclusion of census-tract-level socioeconomic factors into the the Society of Thoracic Surgeons risk predication models is new and shows potential to improve risk prediction for outcomes after cardiac surgery. With the possibility of reimbursement and institutional ranking based on these outcomes, this study represents an improvement in risk prediction model.

Disparities in sinonasal squamous cell carcinoma short- and long-term outcomes: Analysis from the national cancer database.

OBJECTIVES/HYPOTHESIS: To examine sociodemographic, hospital-specific, and disease-related characteristics in relation to outcomes in sinonasal squamous cell carcinoma (SCC). STUDY DESIGN: The National Cancer Database was queried for location codes corresponding to the nasal cavity and paranasal sinuses and histology codes representing SCC malignancy. Multivariate analyses were performed against short- and long-term outcomes (overall survival, days to discharge, 30-day readmission, and 30- and 90-day mortality). RESULTS: The database included 5,584 sinonasal SCC patients with an overall median survival of 53.5 months (95% confidence interval [CI]: 49.7-57.8). On multivariate analysis, uninsured, Medicaid, and Medicare were independently associated with worse overall survival compared to private insurance (hazard ratio [HR]: 1.49, 95% CI: 1.22-1.82, P < .001; HR: 1.57, 95% CI: 1.34-1.85, P < .001; and HR: 1.14, 95% CI: 1.01-1.29, P = .03, respectively). Both Medicaid and Medicare were also associated with increased 30-day mortality (HR: 1.36, 95% CI: 1.76-143.29, P = .02; HR: 8.27, 95% CI: 1.66-70.88, P = .02, respectively), and Medicaid patients spent more time in the hospital following surgery (difference in days to discharge: HR: 2.09, 95% CI: 0.57-3.61, P < .01). Compared to white race, other race was associated with improved overall survival (HR: 0.79, 95% CI: 0.63-0.99, P = .04) but increased 30-day readmissions (HR: 3.85, 95% CI: 1.58-8.38, P < .01). Hispanic ethnicity was associated with increased 30-day readmissions (HR: 2.35, 95% CI: 1.08-4.75, P = .02]. The highest income bracket (≥$63,000) was associated with fewer hospital readmissions (HR: 0.33, 95% CI: 0.13-0.79, P = .01). CONCLUSIONS: Sociodemographic and economic differences in outcomes of patients with sinonasal SCC cancer exist. An understanding of these differences may help minimize disparities in oncologic treatment. LEVEL OF EVIDENCE: 2c. Laryngoscope, 128:560-567, 2018.

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

INTRODUCTION: Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). METHODS: Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided. RESULTS: All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80). CONCLUSIONS: Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.

Quantitative assessment of background parenchymal enhancement in breast MRI predicts response to risk-reducing salpingo-oophorectomy: preliminary evaluation in a cohort of BRCA1/2 mutation carriers.

INTRODUCTION: We present a fully automated method for deriving quantitative measures of background parenchymal enhancement (BPE) from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and perform a preliminary evaluation of these measures to assess the effect of risk-reducing salpingo-oophorectomy (RRSO) in a cohort of breast cancer susceptibility gene 1/2 (BRCA1/2) mutation carriers. METHODS: Breast DCE-MRI data from 50 BRCA1/2 carriers were retrospectively analyzed in compliance with the Health Insurance Portability and Accountability Act and with institutional review board approval. Both the absolute (| |) and relative (%) measures of BPE and fibroglandular tissue (FGT) were computed from the MRI scans acquired before and after RRSO. These pre-RRSO and post-RRSO measures were compared using paired Student's t test. The area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate the performance of relative changes in the BPE and FGT measures in predicting breast cancer that developed in these women after the RRSO surgery. RESULTS: For the 44 women who did not develop breast cancer after RRSO, the absolute volume of BPE and FGT had a significant decrease (P < 0.05) post-RRSO, whereas for the 6 women who developed breast cancer, there were no significant changes in these measures. Higher values in all BPE and FGT measures were also observed post-RRSO for the women who developed breast cancer, compared with women who did not. Relative changes in BPE percentage were most predictive of women who developed breast cancer after RRSO (P < 0.05), whereas combining BPE percentage and |FGT| yielded an AUC of 0.80, higher than BPE percentage (AUC = 0.78) or |FGT| (AUC = 0.66) alone (both P > 0.02). CONCLUSIONS: Quantitative measures of BPE and FGT are different before and after RRSO, and their relative changes are associated with prediction of developing breast cancer, potentially indicative of women who are more susceptible to develop breast cancer after RRSO in BRCA1/2 mutation carriers.

Genetic variation and willingness to participate in epidemiologic research: data from three studies.

The differences in common genetic polymorphism frequencies by willingness to participate in epidemiologic studies are unexplored, but the same threats to internal validity operate as for studies with nongenetic information. We analyzed single nucleotide polymorphism genotypes, haplotypes, and short tandem repeats among control groups from three studies with different recruitment designs that included early, late, and never questionnaire responders, one or more participation incentives, and blood or buccal DNA collection. Among 2,955 individuals, we compared 108 genotypes, 8 haplotypes, and 9 to 15 short tandem repeats by respondent type. Among our main comparisons, single nucleotide polymorphism genotype frequencies differed significantly (P < 0.05) between respondent groups in six instances, with 13 expected by chance alone. When comparing the odds of carrying a variant among the various response groups, 19 odds ratios were /=1.40, levels that might be notably different. Among the various respondent group comparisons, haplotype and short tandem repeat frequencies were not significantly different by willingness to participate. We observed little evidence to suggest that genotype differences underlie response characteristics in molecular epidemiologic studies, but a greater variety of genes should be examined, including those related to behavioral traits potentially associated with willingness to participate. To the extent possible, investigators should evaluate their own genetic data for bias in response categories.