Faculty Bibliography

PURPOSE/OBJECTIVE:To determine whether sociodemographic differences exist among female patients accessing fertility services post-cancer diagnosis in a representative sample of the United States population. METHODS:All women ages 15-45 with a history of cancer who responded to the National Survey for Family Growth (NSFG) from 2011 to 2017 were included. The population was then stratified into 2 groups, defined as those who did and did not seek infertility services. The demographic characteristics of age, legal marital status, education, race, religion, insurance status, access to healthcare, and self-perceived health were compared between the two groups. The primary outcome measure was the utilization of fertility services. The complex sample analysis using the provided sample weights required by the NSFG survey design was used. RESULTS:Five hundred forty-five women reported a history of cancer and were included in this study. Forty-three (7.89%) pursued fertility services after their cancer diagnosis. Using the NSFG sample weights, this equates to a population of 161,500.7 female cancer survivors in the USA who did utilize fertility services and 1,811,955.3 women who did not. Using multivariable analysis, household income, marital status, and race were significantly associated with women utilizing fertility services following a cancer diagnosis. CONCLUSIONS:In this nationally representative cohort of reproductive age women diagnosed with cancer, there are marital, socioeconomic, and racial differences between those who utilized fertility services and those who did not. This difference did not appear to be due to insurance coverage, access to healthcare, or perceived health status.

Modern predictive models require large amounts of data for training and evaluation, absence of which may result in models that are specific to certain locations, populations in them and clinical practices. Yet, best practices for clinical risk prediction models have not yet considered such challenges to generalizability. Here we ask whether population- and group-level performance of mortality prediction models vary significantly when applied to hospitals or geographies different from the ones in which they are developed. Further, what characteristics of the datasets explain the performance variation? In this multi-center cross-sectional study, we analyzed electronic health records from 179 hospitals across the US with 70,126 hospitalizations from 2014 to 2015. Generalization gap, defined as difference between model performance metrics across hospitals, is computed for area under the receiver operating characteristic curve (AUC) and calibration slope. To assess model performance by the race variable, we report differences in false negative rates across groups. Data were also analyzed using a causal discovery algorithm "Fast Causal Inference" that infers paths of causal influence while identifying potential influences associated with unmeasured variables. When transferring models across hospitals, AUC at the test hospital ranged from 0.777 to 0.832 (1st-3rd quartile or IQR; median 0.801); calibration slope from 0.725 to 0.983 (IQR; median 0.853); and disparity in false negative rates from 0.046 to 0.168 (IQR; median 0.092). Distribution of all variable types (demography, vitals, and labs) differed significantly across hospitals and regions. The race variable also mediated differences in the relationship between clinical variables and mortality, by hospital/region. In conclusion, group-level performance should be assessed during generalizability checks to identify potential harms to the groups. Moreover, for developing methods to improve model performance in new environments, a better understanding and documentation of provenance of data and health processes are needed to identify and mitigate sources of variation.

BACKGROUND:Prostate cancer (PCA) germline testing (GT) is now standard-of-care for men with advanced PCA. Thousands of men may consider GT due to clinical and family history (FH) features. Identifying and consenting men for GT can be complex. Here we identified barriers and facilitators of GT across a spectrum of providers which informed the development of Helix - an educational and clinical/FH collection tool to facilitate GT in practice. MATERIALS AND METHODS/METHODS:A 12-question survey assessing knowledge of genetics PCA risk and FH was administered December 2017 to March 2018 in the Philadelphia area and at the Mid-Atlantic AUA meeting (March 2018). Responses were analyzed using descriptive statistics. Semi-structured interviews were conducted with medical oncologists, radiation oncologists, and urologists across practice settings from March-October 2020 as part of a larger study based on the Tailored Implementation in Chronic Diseases framework. Helix was then developed followed by user testing. RESULTS:Fifty-six providers (50% urologists) responded to the survey. Multiple FH and genetic knowledge gaps were identified: only 66% collected maternal FH and 43% correctly identified BRCA2 and association to aggressive PCA. Genetic counseling gaps included low rates of discussing genetic discrimination laws (45%). Provider interviews (n = 14) identified barriers to FH intake including access to details and time needed. In user testing (n = 10), providers found Helix helpful for FH collection. All providers found Helix easy to use, suggesting expanded clinical use. CONCLUSION/CONCLUSIONS:Helix addressed multiple GT knowledge and practice gaps across a spectrum of providers. This tool will become publicly available soon to facilitate PCA GT in clinical practice.

Metabolomics genome wide association study (GWAS) help outline the genetic contribution to human metabolism. However, studies to date have focused on relatively healthy, population-based samples of White individuals. Here, we conducted a GWAS of 537 blood metabolites measured in the Chronic Renal Insufficiency Cohort (CRIC) Study, with separate analyses in 822 White and 687 Black study participants. Trans-ethnic meta-analysis was then applied to improve fine-mapping of potential causal variants. Mean estimated glomerular filtration rate was 44.4 and 41.5 mL/min/1.73m² in the White and Black participants, respectively. There were 45 significant metabolite associations at 19 loci, including novel associations at PYROXD2, PHYHD1, FADS1-3, ACOT2, MYRF, FAAH, and LIPC. The strength of associations was unchanged in models additionally adjusted for estimated glomerular filtration rate and proteinuria, consistent with a direct biochemical effect of gene products on associated metabolites. At several loci, trans-ethnic meta-analysis, which leverages differences in linkage disequilibrium across populations, reduced the number and/or genomic interval spanned by potentially causal single nucleotide polymorphisms compared to fine-mapping in the White participant cohort alone. Across all validated associations, we found strong concordance in effect sizes of the potentially causal single nucleotide polymorphisms between White and Black study participants. Thus, our study identifies novel genetic determinants of blood metabolites in chronic kidney disease, demonstrates the value of diverse cohorts to improve causal inference in metabolomics GWAS, and underscores the shared genetic basis of metabolism across race.

Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.

BACKGROUND:The quality of prostate cancer (PCa) content on Instagram is unknown. METHODS:We examined 62 still-images and 64 video Instagram posts using #prostatecancer on 5/18/20. Results were assessed with validated tools. RESULTS:Most content focused on raising awareness or sharing patient stories (46%); only 9% was created by physicians. 90% of content was low-to-moderate quality and most was understandable, but actionability was 0%. Of the 30% of content including objective information, 40% contained significant misinformation. Most posts had comments offering social support. CONCLUSIONS:Instagram is a source of understandable PCa content and social support; however, information was poorly actionable and had some misinformation.

OBJECTIVE:To determine if there is an association between patient race and physician time spent with the patient during outpatient urology consultations. METHODS:We identified all adult urology new outpatient visits in the National Ambulatory Medical Care Survey dataset for 2012-2016. Patient race was dichotomized as White or non-White. Our primary outcome was time spent during the visit between the patient and urologist. Using population-level weighting, we compared differences in mean time spent during visits with White and non-White patients. Mixed-effects linear regression was used to adjust for confounding factors and to account for clustering among individual physicians. Secondary outcomes included number of services provided and if ancillary providers were seen. RESULTS:Over the 5 year period, 1668 raw visits met criteria and were used to estimate 21million new outpatient urology visits nationwide. 80% of all visits were with White patients. Mean physician time spent among visits with white patients was 23.9 minutes and 24.4 minutes for non-White patients. There was no difference in number of services provided but visits with non-white patients were less likely to include an ancillary provider. After adjustment, there was no significant difference in mean time spent with the urologist among visits with White and non-White patients (difference 0.9 minutes, 95% CI: -0.6-2.4). There were also no differences in adjusted mean time spent among return visits or new visits for hematuria, urologic cancers, or BPH. CONCLUSION/CONCLUSIONS:We found no statistically significant difference in time spent with a urologist during outpatient office consultations between White and non-White patients.