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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.

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.

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.

We describe the steps taken to assess and improve the research readiness of data within PCORnet®, specifically focusing on the results of the PCORnet data curation process between Cycle 7 (October 2019) and Cycle 16 (October 2024). We describe the process for extending the PCORnet® CDM and for creating data checks. We highlight growth in the number of records available across PCORnet between data curation Cycles 7 and 16 (e.g., diagnoses increasing from ∼3.7B to ∼6.9B and laboratory results from ∼7.7B to ∼15.1B among legacy DataMarts), present the current list of data checks and describe performance of the network. We highlight examples of data checks with relatively stable performance (e.g., future dates), those where performance has improved (e.g., RxNorm mapping), and others performance is more variable (e.g., persistence of records). Studies are a crucial source of information on the design of new data checks. The attention of PCORnet partners is focused primarily on those metrics that are generally modifiable. A transparent data curation process is an essential component of PCORnet, allowing network partners to learn from one another, while also informing the decisions of study investigators on which sites to include in their projects. The quality issues that exist within PCORnet stem from the way that data are captured within healthcare generally. We have been able to make to make great strides on improving data quality and research readiness. Many of the techniques piloted within PCORnet will be broadly applicable to other efforts.

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.

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.