Determinants of stage at diagnosis of HPV-related cancer including area deprivation and clinical factors.

BACKGROUND: Collecting social determinants of health in electronic health records is time-consuming. Meanwhile, an Area Deprivation Index (ADI) aggregates sociodemographic information from census data. The objective of this study was to ascertain whether ADI is associated with stage of human papillomavirus (HPV)-related cancer at diagnosis. METHODS: We tested for the association between the stage of HPV-related cancer presentation and ADI as well as the association between stage and the value of each census-based measure using ordered logistic regression, adjusting for age, race and sex. RESULTS: Among 3247 cases of HPV-related cancers presenting to an urban academic medical center, the average age at diagnosis was 57. The average stage at diagnosis was Surveillance, Epidemiology and End Results Stage 3. In the study population, 43% of patients were female and 87% were white. In this study population, there was no association between stage of HPV-related cancer presentation and either aggregate or individual census variables. CONCLUSIONS: These results may reflect insufficient sample size, a lack of socio-demographic diversity in our population, or suggest that simplifying social determinants of health into a single geocoded index is not a reliable surrogate for assessing a patient's risk for HPV-related cancer.

Predicting DNA methylation from genetic data lacking racial diversity using shared classified random effects.

Public genomic repositories are notoriously lacking in racially and ethnically diverse samples. This limits the reaches of exploration and has in fact been one of the driving factors for the initiation of the All of Us project. Our particular focus here is to provide a model-based framework for accurately predicting DNA methylation from genetic data using racially sparse public repository data. Epigenetic alterations are of great interest in cancer research but public repository data is limited in the information it provides. However, genetic data is more plentiful. Our phenotype of interest is cervical cancer in The Cancer Genome Atlas (TCGA) repository. Being able to generate such predictions would nicely complement other work that has generated gene-level predictions of gene expression for normal samples. We develop a new prediction approach which uses shared random effects from a nested error mixed effects regression model. The sharing of random effects allows borrowing of strength across racial groups greatly improving predictive accuracy. Additionally, we show how to further borrow strength by combining data from different cancers in TCGA even though the focus of our predictions is DNA methylation in cervical cancer. We compare our methodology against other popular approaches including the elastic net shrinkage estimator and random forest prediction. Results are very encouraging with the shared classified random effects approach uniformly producing more accurate predictions - overall and for each racial group.

HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.

OBJECTIVES: The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research. MATERIALS AND METHODS: We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities. RESULTS: Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself. DISCUSSION: Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities. CONCLUSION: The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

De-identifying Socioeconomic Data at the Census Tract Level for Medical Research Through Constraint-based Clustering.

Numerous studies have shown that a person's health status is closely related to their socioeconomic status. It is evident that incorporating socioeconomic data associated with a patient's geographic area of residence into clinical datasets will promote medical research. However, most socioeconomic variables are unique in combination and are affiliated with small geographical regions (e.g., census tracts) that are often associated with less than 20,000 people. Thus, sharing such tract-level data can violate the Safe Harbor implementation of de-identification under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). In this paper, we introduce a constraint-based k-means clustering approach to generate census tract-level socioeconomic data that is de-identification compliant. Our experimental analysis with data from the American Community Survey illustrates that the approach generates a protected dataset with high similarity to the unaltered values, and achieves a substantially better data utility than the HIPAA Safe Harbor recommendation of 3-digit ZIP code.

Should We Implement Geographic or Patient-Reported Social Determinants of Health Measures In Cardiovascular Patients.

Objectives: To compare patient-reported social determinants of health (SDOH) to the Brokamp Area Deprivation Index (ADI), and evaluate the association of patient-reported SDOH and ADI with mortality in patients with cardiovascular disease (CVD). Design: Prospective cohort. Setting: Academic medical center. Participants: Adults with acute coronary syndrome (ACS) and/or acute exacerbation of heart failure (HF) hospitalized between 2011 and 2015. Methods: Patient-reported SDOH included: income range, education, health insurance, and household size. ADI was calculated using census tract level variables of poverty, median income, high school completion, lack of health insurance, assisted income, and vacant housing. Primary Outcome: All-cause mortality, up to 5 years follow-up. Results: The sample was 60% male, 84% White, and 93% insured; mean patient-reported household income was $48,000 (SD $34,000). ADI components were significantly associated with corresponding patient-reported variables. In age, sex, and race adjusted Cox regression models, ADI was associated with mortality for ACS (HR 1.23, 95% CI 1.06, 1.42), but not HF (HR 1.09, 95% CI .99, 1.21). Mortality models for ACS improved with consideration of social determinants data (C-statistics: base demographic model=.612; ADI added=.644; patient-reported SDOH added=.675; both ADI and patient-reported SDOH added=.689). HF mortality models improved only slightly (C-statistics: .600, .602, .617, .620, respectively). Conclusions: The Brokamp ADI is associated with mortality in hospitalized patients with CVD. In the absence of available patient-reported data, hospitals could implement the Brokamp ADI as an approximation for patient-reported data to enhance risk stratification of patients with CVD.

Usefulness of Single Nucleotide Polymorphisms as Predictors of Sudden Cardiac Death.

The pathophysiology of sudden cardiac death (SCD) remains incompletely understood. Genetic mutations can create a favorable substrate for SCD. Our aim is to evaluate the evidence of single nucleotide polymorphisms (SNPs) as predictors of SCD. We searched the Medline database (2000 to 2017) and selected all case-control or cohort studies that reported associations between SNPs and SCD. Our search terms included "polymorphisms" and "sudden death." We collected the study design, population ethnic background, gene testing strategy, the association between the SNP and SCD, and the cardiovascular comorbidities of the population. Our search yielded 723 studies, of which we included 24 based upon our inclusion criteria. The studies had a total population of 78,165 participants, with a median age of 62.5 years (IQR 56 to 66) and 35% (IQR 13 to 32) were female. Almost all studies were conducted in white patients of European descent and the most commonly used genetic strategy was candidate gene panels. Fifteen of the studies had a case-control design that included SCD patients without known heart disease as the comparison group and the other 9 studies included patients with heart failure and coronary artery disease. The studies evaluated 53 SNPs and the most common genetic loci were SCN5A, RyR2, CASQ2, NOSA1P, and AGTR. SNPs with the 3 strongest statistically significant ORs >1 were: rs6684209 of CASQ2 (odds ratio [OR] 19), rs3814843 of CALM1 (OR 5.5), and rs35594137 of GJA5 (OR 3.6). In Conclusion, many SNPs are associated with SCD, with the strongest associations seen in SNPs of genes related to intracellular calcium handling. These findings were generated primarily using a candidate gene strategy in white patients with European descent.

We're not all cut from the same cloth: TAILORing treatments for children with chronic conditions.

BACKGROUND: Finding the optimal treatment for a chronic condition can be a complex and lengthy endeavor for both the patient and the clinician. To address this challenge, we developed an "N-of-1" quality improvement infrastructure to aid providers and patients in personalized treatment decision-making using systematic assessment of patient-reported outcomes during routine care. METHODS: Using the REDCap data management infrastructure, we implemented three pediatric pilots of the Treatment Assessments in the Individual Leading to Optimal Regimens (TAILOR) tool, including children receiving early intervention, children with attention deficit hyperactivity disorder, and children with challenging behaviors in the classroom setting. This retrospective review of data summarizes utilization and satisfaction data during our pilot experience with the tool. RESULTS: The three pilots included a combined total of 109 children and 39 healthcare providers, with 67 parents and 77 teachers invited to share data using brief surveys administered using TAILOR. Overall survey response rates ranged from 38% to 84% across the three pilots, with response rates notably higher among teachers as compared with parents. Satisfaction data indicated positive impressions of the tool's utility. DISCUSSION: These experiences show the utility of the TAILOR framework for supporting collection and incorporation of patient-reported outcomes into the care of individuals with chronic conditions.

COMPASS: A Pilot Trial of an Early Palliative Care Intervention for Patients With End-Stage Liver Disease.

CONTEXT: Palliative care interventions have shown promise in improving quality of life and reducing health-care utilization among patients with chronic organ failure. OBJECTIVES: To evaluate the effect of a palliative care intervention for adults with end-stage liver disease. METHODS: A randomized controlled trial of patients with end-stage liver disease admitted to the hepatology service at a tertiary referral center whose attending hepatologist indicated they would not be surprised if the patient died in the following year on a standardized questionnaire was performed. Control group patients received usual care. Intervention group patients received inpatient specialist palliative care consultations and outpatient phone follow-up by a palliative care nurse. The primary outcome was time until first readmission. Secondary outcomes included days alive outside the hospital, referral to hospice care, death, readmissions, patient quality of life, depression, anxiety, and quality of end-of-life care over 6 months. RESULTS: The trial stopped early because of difficulties in accruing patients. Of 293 eligible patients, only 63 patients were enrolled, 31 in the intervention group and 32 in the control group. This pace of enrollment was only 25% of what the study had planned, and so it was deemed infeasible to complete. Despite stopping early, intervention group patients had a lower hazard of readmission (hazard ratio: 0.36, 95% confidence interval: 0.16-0.83, P = 0.017) and greater odds of having more days alive outside the hospital than control group patients (odds ratio: 3.97, 95% confidence interval: 1.14-13.84, P = 0.030). No other statistically significant differences were observed. CONCLUSION: Logistical obstacles hindered completion of the trial as originally designed. Nevertheless, a preemptive palliative care intervention resulted in increased time to first readmission and more days alive outside the hospital in the first six months after study entry.

Patient and healthcare provider views on a patient-reported outcomes portal.

Background: Over the past decade, public interest in managing health-related information for personal understanding and self-improvement has rapidly expanded. This study explored aspects of how patient-provided health information could be obtained through an electronic portal and presented to inform and engage patients while also providing information for healthcare providers. Methods: We invited participants using ResearchMatch from 2 cohorts: (1) self-reported healthy volunteers (no medical conditions) and (2) individuals with a self-reported diagnosis of anxiety and/or depression. Participants used a secure web application (dashboard) to complete the PROMIS® domain survey(s) and then complete a feedback survey. A community engagement studio with 5 healthcare providers assessed perspectives on the feasibility and features of a portal to collect and display patient provided health information. We used bivariate analyses and regression analyses to determine differences between cohorts. Results: A total of 480 participants completed the study (239 healthy, 241 anxiety and/or depression). While participants from the tw2o cohorts had significantly different PROMIS scores (p < .05), both cohorts welcomed the concept of a patient-centric dashboard, saw value in sharing results with their healthcare provider, and wanted to view results over time. However, factors needing consideration before widespread use included personalization for the patient and their health issues, integration with existing information (eg electronic health records), and integration into clinician workflow. Conclusions: Our findings demonstrated a strong desire among healthy people, patients with chronic diseases, and healthcare providers for a self-assessment portal that can collect patient-reported outcome metrics and deliver personalized feedback.

Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.

Objective: Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository. Materials and Methods: We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE. Results: word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC = 0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC = 0.95) than ACE (AUPRC = 0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being "father" (21.8%) and "mother" (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%-47.6%). Conclusion: We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.

Variation in the α2A-adrenergic receptor gene and risk of gestational diabetes.

AIM: Sympathetic activation suppresses insulin secretion via pancreatic ADRA2A.  Because sympathetic activity and insulin demand increase during pregnancy, we tested the hypothesis  that ADRA2A variants are associated with gestational diabetes (GDM). PATIENTS & METHODS: Among Caucasian pregnant women without pre-existing diabetes, we genotyped 458 who had GDM and 1537 without GDM for seven ADRA2A variants. RESULTS: rs1800038 (OR: 2.34; p = 0.020) and rs3750625 (OR: 1.56; p = 0.010) increased the risk of GDM, and rs11195418 decreased it (OR: 0.62; p = 0.025). The associations remained significant after adjustment for maternal age, maternal BMI, parity and a genetic risk score that included variants previously associated with Type 2 diabetes mellitus and GDM. CONCLUSION: ADRA2A genetic variation contributes independently to the risk of GDM in Caucasian women.