Cardiology Encounters for Underrepresented Racial and Ethnic Groups with Human Immunodeficiency Virus and Borderline Cardiovascular Disease Risk.

BACKGROUND: Underrepresented racial and ethnic groups (UREGs) with HIV have a higher risk of cardiovascular disease (CVD) compared with the general population. Referral to a cardiovascular specialist improves CVD risk factor management in high-risk individuals. However, patient and provider factors impacting the likelihood of UREGs with HIV to have an encounter with a cardiologist are unknown. METHODS: We evaluated a cohort of UREGs with HIV and borderline CVD risk (10-year risk ≥ 5% by the pooled cohort equations or ≥ 7.5% by Framingham risk score). Participants received HIV-related care from 2014-2020 at four academic medical centers in the United States (U.S.). Adjusted Cox proportional hazards regression was used to estimate the association of patient and provider characteristics with time to first ambulatory cardiology encounter. RESULTS: A total of 2,039 people with HIV (PWH) and borderline CVD risk were identified. The median age was 45 years (IQR: 36-50); 52% were female; and 94% were Black. Of these participants, 283 (14%) had an ambulatory visit with a cardiologist (17% of women vs. 11% of men, p < .001). In fully adjusted models, older age, higher body mass index (BMI), atrial fibrillation, multimorbidity, urban residence, and no recent insurance were associated with a greater likelihood of an encounter with a cardiologist. CONCLUSION: In UREGs with HIV and borderline CVD risk, the strongest determinants of a cardiology encounter were diagnosed CVD, insurance type, and urban residence. Future research is needed to determine the extent to which these encounters impact CVD care practices and outcomes in this population. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04025125.

Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network.

OBJECTIVE: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network. MATERIAL AND METHODS: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population. RESULTS: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%. DISCUSSION: The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population. CONCLUSIONS: This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.

Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnet®, the National Patient-Centered Clinical Research Network.

OBJECTIVE: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network. RESULTS: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet®). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens. We queried the sites for patients with a clinical encounter in 2018 or 2019 and matched their tokens to determine whether overlap existed. We described patient overlap among the sites and generated a "deduplicated" table of patient demographic characteristics. Overlapping patients were found in 3 of the 6 site-pairs. Following deduplication, the total patient count was 3,108,515 (0.11% reduction), with the largest reduction in count for patients with an "Other/Missing" value for Sex; from 198 to 163 (17.6% reduction). The PPRL solution successfully links patients across data sources using distributed queries without directly accessing patient PII. The overlap queries and analysis performed in this pilot is being replicated across the full network to provide additional insight into patient linkages among a distributed research network.

Diabetes medication regimens and patient clinical characteristics in the national patient-centered clinical research network, PCORnet.

We used electronic medical record (EMR) data in the National Patient-Centered Clinical Research Network (PCORnet) to characterize "real-world" prescription patterns of Type 2 diabetes (T2D) medications. We identified a retrospective cohort of 613,203 adult patients with T2D from 33 datamarts (median patient number: 12,711) from 2012 through 2017 using a validated computable phenotype. We characterized outpatient T2D prescriptions for each patient in the 90 days before and after cohort entry, as well as demographics, comorbidities, non-T2D prescriptions, and clinical and laboratory variables in the 730 days prior to cohort entry. Approximately half of the individuals in the cohort were females and 20% Black. Hypertension (60.3%) and hyperlipidemia (50.5%) were highly prevalent. Most patients were prescribed either a single T2D drug class (42.2%) or had no evidence of a T2D prescription in the EMR (42.4%). A smaller percentage was prescribed multiple T2D drug types (15.4%). Among patients prescribed a single T2D drug type, metformin was the most common (42.6%), followed by insulin (18.2%) and sulfonylureas (13.9%). Newer classes represented approximately 13% of single T2D drug type prescriptions (dipeptidyl peptidase-4 inhibitors [6.6%], glucagon-like peptide-1 receptor agonists [2.5%], thiazolidinediones [2.0%], and sodium-glucose cotransporter-2 inhibitors [1.6%]). Among patients prescribed multiple T2D drug types, the most common combination was metformin and sulfonylureas (63.5%). Metformin-based regimens were highly prevalent in PCORnet's T2D population, whereas newer agents were prescribed less frequently. PCORnet is a novel source for the potential conduct of observational studies among patients with T2D.

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).

INTRODUCTION: Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data. METHODS: PCORnet's Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals. RESULTS: The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners. DISCUSSION: The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

Supporting open access to clinical trial data for researchers: The Duke Clinical Research Institute-Bristol-Myers Squibb Supporting Open Access to Researchers Initiative.

There are growing calls for sponsors to increase transparency by providing access to clinical trial data. In response, Bristol-Myers Squibb and the Duke Clinical Research Institute have collaborated on a new initiative, Supporting Open Access to Researchers. The aim is to facilitate open sharing of Bristol-Myers Squibb trial data with interested researchers. Key features of the Supporting Open Access to Researchers data sharing model include an independent review committee that ensures expert consideration of each proposal, stringent data deidentification/anonymization and protection of patient privacy, requirement of prespecified statistical analysis plans, and independent review of manuscripts before submission for publication. We believe that these approaches will promote open science by allowing investigators to verify trial results as well as to pursue interesting secondary uses of trial data without compromising scientific integrity.

The ethics and regulatory landscape of including vulnerable populations in pragmatic clinical trials.

Policies have been developed to protect vulnerable populations in clinical research, including the US federal research regulations (45 Code of Federal Regulations 46 Subparts B, C, and D). These policies generally recognize vulnerable populations to include pregnant women, fetuses, neonates, children, prisoners, persons with physical handicaps or mental disabilities, and disadvantaged persons. The aim has been to protect these populations from harm, often by creating regulatory and ethical checks that may limit their participation in many clinical trials. The recent increase in pragmatic clinical trials raises at least two questions about this approach. First, is exclusion itself a harm to vulnerable populations, as these groups may be denied access to understanding how health interventions work for them in clinical settings? Second, are groups considered vulnerable in traditional clinical trials also vulnerable in pragmatic clinical trials? We argue first that excluding vulnerable subjects from participation in pragmatic clinical trials can be harmful by preventing acquisition of data to meaningfully inform clinical decision-making in the future. Second, we argue that protections for vulnerable subjects in traditional clinical trial settings may not be translatable, feasible, or even ethical to apply in pragmatic clinical trials. We conclude by offering specific recommendations for appropriately protecting vulnerable research subjects in pragmatic clinical trials, focusing on pregnant women, fetuses, neonates, children, prisoners, persons with physical handicaps or mental disabilities, and disadvantaged persons.