The FDA Sentinel Real World Evidence Data Enterprise (RWE-DE).

PURPOSE: The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making. METHODS: In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. RESULTS: We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed. CONCLUSIONS: Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.

Public Health Surveillance in Electronic Health Records: Lessons From PCORnet.

Introduction: PCORnet, the National Patient-Centered Clinical Research Network, is a large research network of health systems that map clinical data to a standardized data model. In 2018, we expanded existing infrastructure to facilitate use for public health surveillance. We describe benefits and challenges of using PCORnet for surveillance and describe case studies. Methods: In 2018, infrastructure enhancements included addition of a table to store patients' residential zip codes and expansion of a modular program to generate population health statistics across conditions. Chronic disease surveillance case studies conducted in 2019 assessed atrial fibrillation (AF) and cirrhosis. In April 2020, PCORnet established an infrastructure to support COVID-19 surveillance with institutions frequently updating their electronic health record data. Results: By August 2023, 53 PCORnet sites (84%) had a 5-digit zip code available on at least 95% of their patient populations. Among 148,223 newly diagnosed AF patients eligible for oral anticoagulant (OAC) therapy, 43.3% were on any OAC (17.8% warfarin, 28.5% any novel oral anticoagulant) within a year of the AF diagnosis. Among 60,268 patients with cirrhosis (2015-2019), common documented etiologies included unknown (48%), hepatitis C infection (23%), and alcohol use (22%). During October 2022 through December 2023, across 34 institutions, the proportion of COVID-19 patients who were cared for in the inpatient setting was 9.1% among 887,051 adults aged 20 years or older and 6.0% among 139,148 children younger than 20 years. Conclusions: PCORnet provides important data that may augment traditional public health surveillance programs across diverse conditions. PCORnet affords longitudinal population health assessments among large catchments of the population with clinical, treatment, and geographic information, with capabilities to deliver rapid information needed during public health emergencies.

Participant choices for return of genomic results in the eMERGE Network.

PURPOSE: Secondary findings are typically offered in an all or none fashion when sequencing is used for clinical purposes. This study aims to describe the process of offering categorical and granular choices for results in a large research consortium. METHODS: Within the third phase of the electronic MEdical Records and GEnomics (eMERGE) Network, several sites implemented studies that allowed participants to choose the type of results they wanted to receive from a multigene sequencing panel. Sites were surveyed to capture the details of the implementation protocols and results of these choices. RESULTS: Across the ten eMERGE sites, 4664 participants including adolescents and adults were offered some type of choice. Categories of choices offered and methods for selecting categories varied. Most participants (94.5%) chose to learn all genetic results, while 5.5% chose subsets of results. Several sites allowed participants to change their choices at various time points, and 0.5% of participants made changes. CONCLUSION: Offering choices that include learning some results is important and should be a dynamic process to allow for changes in scientific knowledge, participant age group, and individual preference.

Correction: The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

PURPOSE: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. METHODS: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. RESULTS: Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. CONCLUSIONS: The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Leveraging electronic health records for clinical research.

Electronic health records (EHRs) can be a major tool in the quest to decrease costs and timelines of clinical trial research, generate better evidence for clinical decision making, and advance health care. Over the past decade, EHRs have increasingly offered opportunities to speed up, streamline, and enhance clinical research. EHRs offer a wide range of possible uses in clinical trials, including assisting with prestudy feasibility assessment, patient recruitment, and data capture in care delivery. To fully appreciate these opportunities, health care stakeholders must come together to face critical challenges in leveraging EHR data, including data quality/completeness, information security, stakeholder engagement, and increasing the scale of research infrastructure and related governance. Leaders from academia, government, industry, and professional societies representing patient, provider, researcher, industry, and regulator perspectives convened the Leveraging EHR for Clinical Research Now! Think Tank in Washington, DC (February 18-19, 2016), to identify barriers to using EHRs in clinical research and to generate potential solutions. Think tank members identified a broad range of issues surrounding the use of EHRs in research and proposed a variety of solutions. Recognizing the challenges, the participants identified the urgent need to look more deeply at previous efforts to use these data, share lessons learned, and develop a multidisciplinary agenda for best practices for using EHRs in clinical research. We report the proceedings from this think tank meeting in the following paper.

Implementation of a Regional Perinatal Data Repository from Clinical and Billing Records.

Objectives To describe the implementation of the first phase of a regional perinatal data repository and to provide a roadmap for others to navigate technical, privacy, and data governance concerns in implementing similar resources. Methods Our implementation integrated regional physician billing records with maternal and infant electronic health records from an academic delivery hospital. These records, representing births during 2013-2015, constituted a data core supporting linkage to additional ancillary data sets. Measures obtained from pediatric follow-up, urgent care, emergency, and inpatient encounters were linked at the individual level as were measures obtained by home visitors during pre- and postnatal encounters. Residential addresses were geocoded supporting linkage to area-level measures. Results Integrated data contained regional billing records for 69,290 newborns representing approximately 81% of all regional live births and nearly 95% of live births in the region's most populous county. Billing records linked to 7293 infant delivery hospital records and 7107 corresponding maternal hospital records. Manual review demonstrated 100% validity of matches among audited records. Additionally, 2430 home visiting records were linked to the data core as were pediatric primary care, urgent care, emergency department, and inpatient visits representing 42,541 children. More than 99% of the newborn billing records were geocoded and assigned a census tract identifier. Conclusions for Practice Our approach to methodological and regulatory challenges affords opportunities for expansion of systems to integrate electronic health records originating from additional medical centers as well as individual- and area-level linkage to additional data sets relevant to perinatal health.

Pediatric Eosinophilic Esophagitis Symptom Scores (PEESS v2.0) identify histologic and molecular correlates of the key clinical features of disease.

BACKGROUND: The Pediatric Eosinophilic Esophagitis Symptom Score (PEESS v2.0) measures patient-relevant outcomes. However, whether patient-identified domains (dysphagia, gastroesophageal reflux disease [GERD], nausea/vomiting, and pain) align with clinical symptomology and histopathologic and molecular features of eosinophilic esophagitis (EoE) is unclear. OBJECTIVE: The purpose of this study was to determine whether clinical features of EoE, measured through PEESS v2.0, associate with histopathologic and molecular features of EoE. This represents a novel approach for analysis of allergic diseases, given the availability of allergic tissue biopsy specimens. METHODS: We systematically recruited treated and untreated pediatric patients with EoE (aged 2-18 years) and examined parent proxy-reported symptoms using the PEESS v2.0. Clinical symptomology was collected by questionnaire. Esophageal biopsy samples were quantified for levels of eosinophils, eosinophil peroxidase (EPX) immunohistochemical staining, and mast cells. Molecular features were assessed by using the EoE Diagnostic Panel (94 EoE-related gene transcripts). Associations between domain scores and clinical symptoms and biological features were analyzed with Wilcoxon rank sum and Spearman correlation. RESULTS: The PEESS v2.0 domains correlated to specific parent-reported symptoms: dysphagia (P = .0012), GERD (P = .0001), and nausea/vomiting (P < .0001). Pain correlated with multiple symptoms (P < .0005). Dysphagia correlated most strongly with overall histopathology, particularly in the proximal esophagus (P ≤ .0049). Markers of esophageal activity (EPX) were significantly associated with dysphagia (strongest r = 0.37, P = .02). Eosinophil levels were more associated with pain (r = 0.27, P = .06) than dysphagia (r = 0.24, P = .13). The dysphagia domain correlated most with esophageal gene transcript levels, predominantly with mast cell-specific genes. CONCLUSION: We have (1) established a validated, parent proxy-reported measure for pediatric EoE, the PEESS v2.0; (2) verified that the parent proxy effectively captures symptoms; (3) determined that the dysphagia domain most closely aligns with symptoms and tissue-based molecular biomarkers; (4) established that symptoms correlate with EPX staining; and (5) observed association between mast cells and dysphagia.

Increased prevalence of eosinophilic gastrointestinal disorders in pediatric PTEN hamartoma tumor syndromes.

OBJECTIVES: The PTEN hamartoma tumor syndromes (PHTSs) are a collection of disorders caused by germline mutations of the tumor suppressor gene PTEN. Eosinophilic gastrointestinal disorders (EGIDs) are rare diseases characterized by food-induced, eosinophil-dominant inflammation in various segments of the gastrointestinal tract. On the basis of our clinical observations of several patients with EGID-PHTS, we investigated whether there is an association between these 2 disorders. METHODS: The Cincinnati Children's Hospital Medical Center (CCHMC) Informatics for Integrating Biology and the Bedside (i2b2) warehouse was queried for the years 2007 to 2012 using International Classification of Diseases-9 codes for PTEN-related diseases; the results were cross-referenced with participants enrolled in the Cincinnati Center for Eosinophilic Disorder's EGID database to identify patients with both disorders. In an effort to replicate our findings, the Cleveland Clinic Genomic Medicine Institute PTEN database was queried for cases between 2005 and 2012. Inclusion criteria were age ≤ 18 years, history of PHTS, and an esophagogastroduodenoscopy (EGD) and/or colonoscopy with at least 1 histologic EGID diagnosis confirmed by a CCHMC pathologist. The Pearson χ(2) test was used to determine the odds of EGID enrichment in PHTS. RESULTS: Of the 1,058,260 CCHMC distinct patients identified by the i2b2 search, 53 had clinical diagnoses suggestive of PHTS. Thirteen of the 53 had PTEN mutations, with 8 of 13 (62%) having had an EGD and/or colonoscopy. Five of the 8 had confirmed EGID. At the Cleveland Clinic, 3 of 75 patients (3/4 who had EGD and/or colonoscopy) with PHTS had confirmed EGID. CCHMC i2b2 query data showed a substantial enrichment of EGIDs in PHTSs (odds ratio  272; confidence interval 89-831, P < 0.0001). An EGID prevalence estimate from the i2b2 query supported a marked enrichment of EGIDs in PHTSs in the Cleveland Clinic database (P < 0.0001). Among the 8 subjects with EGIDs and PHTSs, the age at EGID and PHTS diagnosis was 7.6 ± 3.2 and 7.9 ± 5.8 years, respectively. Patients with EGID-PHTS had excess eosinophils in biopsies of the esophagus (75%), stomach (38%), and colon (13%), with a notable presence of eosinophil-rich gastrointestinal polyposis (88%). CONCLUSIONS: EGID is a previously unrecognized comorbid disease in pediatric patients with PHTS. These data suggest a potential role of PTEN in contributing to EGID susceptibility.

Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research.

OBJECTIVE: The current study aims to fill the gap in available healthcare de-identification resources by creating a new sharable dataset with realistic Protected Health Information (PHI) without reducing the value of the data for de-identification research. By releasing the annotated gold standard corpus with Data Use Agreement we would like to encourage other Computational Linguists to experiment with our data and develop new machine learning models for de-identification. This paper describes: (1) the modifications required by the Institutional Review Board before sharing the de-identification gold standard corpus; (2) our efforts to keep the PHI as realistic as possible; (3) and the tests to show the effectiveness of these efforts in preserving the value of the modified data set for machine learning model development. MATERIALS AND METHODS: In a previous study we built an original de-identification gold standard corpus annotated with true Protected Health Information (PHI) from 3503 randomly selected clinical notes for the 22 most frequent clinical note types of our institution. In the current study we modified the original gold standard corpus to make it suitable for external sharing by replacing HIPAA-specified PHI with newly generated realistic PHI. Finally, we evaluated the research value of this new dataset by comparing the performance of an existing published in-house de-identification system, when trained on the new de-identification gold standard corpus, with the performance of the same system, when trained on the original corpus. We assessed the potential benefits of using the new de-identification gold standard corpus to identify PHI in the i2b2 and PhysioNet datasets that were released by other groups for de-identification research. We also measured the effectiveness of the i2b2 and PhysioNet de-identification gold standard corpora in identifying PHI in our original clinical notes. RESULTS: Performance of the de-identification system using the new gold standard corpus as a training set was very close to training on the original corpus (92.56 vs. 93.48 overall F-measures). Best i2b2/PhysioNet/CCHMC cross-training performances were obtained when training on the new shared CCHMC gold standard corpus, although performances were still lower than corpus-specific trainings. DISCUSSION AND CONCLUSION: We successfully modified a de-identification dataset for external sharing while preserving the de-identification research value of the modified gold standard corpus with limited drop in machine learning de-identification performance.

High prevalence of eosinophilic esophagitis in patients with inherited connective tissue disorders.

BACKGROUND: Eosinophilic esophagitis (EoE) is an emerging chronic inflammatory disease mediated by immune hypersensitization to multiple foods and strongly associated with atopy and esophageal remodeling. OBJECTIVE: We provide clinical and molecular evidence indicating a high prevalence of EoE in patients with inherited connective tissue disorders (CTDs). METHODS: We examined the rate of EoE among patients with CTDs and subsequently analyzed esophageal mRNA transcript profiles in patients with EoE with or without CTD features. RESULTS: We report a cohort of 42 patients with EoE with a CTD-like syndrome, representing 0.8% of patients with CTDs and 1.3% of patients with EoE within our hospital-wide electronic medical record database and our EoE research registry, respectively. An 8-fold risk of EoE in patients with CTDs (relative risk, 8.1; 95% confidence limit, 5.1-12.9; χ(2)1 = 112.0; P < 10(-3)) was present compared with the general population. Esophageal transcript profiling identified a distinct subset of genes, including COL8A2, in patients with EoE and CTDs. CONCLUSION: There is a remarkable association of EoE with CTDs and evidence for a differential expression of genes involved in connective tissue repair in this cohort. Thus, we propose stratification of patients with EoE and CTDs into a subset referred to as EoE-CTD.

Applications of Business Analytics in Healthcare.

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and as they begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics, namely the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently for improving healthcare delivery, and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and how analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

Importance: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA advisory committee meetings, and other regulatory decisions. However, observational data are frequently deemed insufficient for reliable evaluation of safety concerns owing to limitations in underlying data or methodology. Advances in large language models (LLMs) provide new opportunities to address some of these limitations. However, careful consideration is necessary for how and where LLMs can be effectively deployed for these purposes. Observations: LLMs may provide new avenues to support signal-identification activities to identify novel adverse event signals from narrative text of electronic health records. These algorithms may be used to support epidemiologic investigations examining the causal relationship between exposure to a medical product and an adverse event through development of probabilistic phenotyping of health outcomes of interest and extraction of information related to important confounding factors. LLMs may perform like traditional natural language processing tools by annotating text with controlled vocabularies with additional tailored training activities. LLMs offer opportunities for enhancing information extraction from adverse event reports, medical literature, and other biomedical knowledge sources. There are several challenges that must be considered when leveraging LLMs for postmarket surveillance. Prompt engineering is needed to ensure that LLM-extracted associations are accurate and specific. LLMs require extensive infrastructure to use, which many health care systems lack, and this can impact diversity, equity, and inclusion, and result in obscuring significant adverse event patterns in some populations. LLMs are known to generate nonfactual statements, which could lead to false positive signals and downstream evaluation activities by the FDA and other entities, incurring substantial cost. Conclusions and Relevance: LLMs represent a novel paradigm that may facilitate generation of information to support medical product postmarket surveillance activities that have not been possible. However, additional work is required to ensure LLMs can be used in a fair and equitable manner, minimize false positive findings, and support the necessary rigor of signal detection needed for regulatory activities.

Association of Cardiologist Clinic Visits with Cardiovascular Primary Prevention Outcomes Among People with HIV from Underrepresented Racial and Ethnic Groups in the Southern United States.

Background: People with HIV (PWH) are at elevated risk for atherosclerotic cardiovascular disease (ASCVD). Underrepresented racial and ethnic groups (UREGs) with HIV in the southern U.S. are disproportionately affected, yet whether cardiology specialist care for this at-risk group improves blood pressure and lipid control or prevents cardiovascular events is unknown. Methods: We evaluated a cohort of PWH from UREGs at elevated ASCVD risk without known cardiovascular disease who received HIV-related care from 2015-2018 at four academic medical centers in the Southern United States with follow up through 2020. Primary outcomes were blood pressure control (<140/90 mmHg) and lipid control (LDL-C ≤ 100 mg/dl) over 2 years and time to first major adverse cardiovascular (MACE) event. Statistical analyses were adjusted for cohort/site and patient factors including HIV measures and comorbidities. Results: Among 3972 included PWH (median age 47 years old, 32.6% female) without diagnosed cardiovascular disease, 276 (6.9%) had a cardiology clinic visit. Cardiology clinic visits were not significantly associated with subsequent blood pressure control (adjusted OR 0.78, 95% CI 0.49-1.24, p=0.29) or lipid control (adjusted OR 2.25, 95% CI 0.72-7.01, p=0.16). Over a median follow up of 5 years, patients who had a cardiology clinic visit had higher risk of MACE, overall mortality, and falsification endpoints (hospitalization or death from accident/trauma and pneumonia/sepsis) indicating a higher risk group overall, even after adjusting for measured risk factors. Conclusions: Among UREG PWH at elevated cardiovascular risk, a cardiology clinic visit was not associated with improved cardiovascular risk factors or reduced risk of cardiovascular events. Our study suggests that seeing a cardiologist is not alone sufficient to promote cardiovascular health or prevent cardiovascular events among PWH, but with low confidence given the higher risk among those who had a cardiology visit.

Kidney and Cardiovascular Effectiveness of Empagliflozin Compared With Dipeptidyl Peptidase-4 Inhibitors in Patients With Type 2 Diabetes.

Placebo-controlled trials of sodium-glucose co-transporter-2 inhibitors demonstrate kidney and cardiovascular benefits for patients with type 2 diabetes and chronic kidney disease (CKD). We used real-world data to compare the kidney and cardiovascular effectiveness of empagliflozin to dipeptidyl peptidase-4 inhibitors (DPP4is), a commonly prescribed antiglycemic medication, in a diverse population with and without CKD. Using electronic health record data from 20 large US health systems, we leveraged propensity overlap weighting to compare the outcomes for empagliflozin and DPP4i initiators with type 2 diabetes between 2016 and 2020. The primary composite kidney outcome included 40% estimated glomerular filtration rate decrease, incident end-stage kidney disease, or all-cause mortality through 2 years or censoring. We also assessed cardiovascular and safety outcomes. Of 62,197 new users, 20,279 initiated empagliflozin and 41,918 initiated DPP4i. Over a median follow-up of 1.1 years, empagliflozin prescription was associated with a lower risk of the primary outcome (hazard ratio [HR] 0.75, 95% confidence interval [CI] 0.65 to 0.87) than DPP4is. The risks for mortality (HR 0.76, 95% CI 0.62 to 0.92) and a cardiovascular composite of stroke, myocardial infarction, or all-cause mortality (HR 0.81, 95% CI 0.70 to 0.95) were also lower for empagliflozin initiators. No difference in heart failure hospitalization risk between groups was observed. Genital mycotic infections were more common in patients prescribed empagliflozin (HR 1.72, 95% CI 1.58 to 1.88). Empagliflozin was associated with a lower risk of the primary outcome in patients with CKD (HR 0.68, 95% CI 0.53 to 0.88) and those without CKD (HR 0.79, 95% CI 0.67 to 0.94). In conclusion, the initiation of empagliflozin was associated with a significantly lower risk of kidney and cardiovascular outcomes than DPP4is over a median of just over 1 year. The association with a lower risk for clinical outcomes was apparent even for patients without known CKD at baseline.

Kidney and Cardiovascular Effectiveness of SGLT2 Inhibitors vs GLP-1 Receptor Agonists in Type 2 Diabetes.

BACKGROUND: Emerging data suggest that glucagon-like peptide-1 receptor agonists (GLP-1 RAs) improve kidney outcomes for people with type 2 diabetes (T2D). Direct comparisons of the kidney and cardiovascular effectiveness of GLP-1 RA with sodium-glucose cotransporter 2 inhibitors (SGLT2i), a first-line therapy for this population, are needed. OBJECTIVES: The authors compared kidney and cardiovascular outcomes for new users of SGLT2i and GLP-1 RAs with T2D. METHODS: Using propensity score overlap weighting, we analyzed electronic health record data from 20 U.S. health systems contributing to PCORnet between 2015 and 2020. The primary kidney outcome was a composite of sustained 40% estimated glomerular filtration rate (eGFR) decline, incident end-stage kidney disease, or all-cause mortality over 2 years or until censoring. In addition, we examined cardiovascular and safety outcomes. RESULTS: The weighted study cohort included 35,004 SGLT2i and 47,268 GLP-1 RA initiators. Over a median of 1.2 years, the primary outcome did not differ between treatments (HR: 0.91; 95% CI: 0.81-1.02), although SGLT2i were associated with a lower risk of 40% eGFR decline (HR: 0.77; 95% CI: 0.65-0.91). Risks of mortality (HR: 1.08; 95% CI: 0.92-1.27), a composite of stroke, myocardial infarction, or death (HR: 1.03; 95% CI: 0.93-1.14), and heart failure hospitalization (HR: 0.95; 95% CI: 0.80-1.13) did not differ. Genital mycotic infections were more common for SGLT2i initiators, but other safety outcomes did not differ. The results were similar regardless of chronic kidney disease status. CONCLUSIONS: SGLT2i and GLP-1 RAs led to similar kidney and cardiovascular outcomes in people with T2D, though SGLT2i initiation was associated with a lower risk of 40% eGFR decline. (Evaluating Comparative Effectiveness of Empagliflozin in Type 2 Diabetes Population With and Without Chronic Kidney Disease; NCT05465317).

Concordance With Screening and Treatment Guidelines for Chronic Kidney Disease in Type 2 Diabetes.

Importance: Chronic kidney disease (CKD) is an often-asymptomatic complication of type 2 diabetes (T2D) that requires annual screening to diagnose. Patient-level factors linked to inadequate screening and treatment can inform implementation strategies to facilitate guideline-recommended CKD care. Objective: To identify risk factors for nonconcordance with guideline-recommended CKD screening and treatment in patients with T2D. Design, Setting, and Participants: This retrospective cohort study was performed at 20 health care systems contributing data to the US National Patient-Centered Clinical Research Network. To evaluate concordance with CKD screening guidelines, adults with an outpatient clinician visit linked to T2D diagnosis between January 1, 2015, and December 31, 2020, and without known CKD were included. A separate analysis reviewed prescription of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) and sodium-glucose cotransporter 2 (SGLT2) inhibitors in adults with CKD (estimated glomerular filtration rate [eGFR] of 30-90 mL/min/1.73 m2 and urinary albumin-to-creatinine ratio [UACR] of 200-5000 mg/g) and an outpatient clinician visit for T2D between October 1, 2019, and December 31, 2020. Data were analyzed from July 8, 2022, through June 22, 2023. Exposures: Demographics, lifestyle factors, comorbidities, medications, and laboratory results. Main Outcomes and Measures: Screening required measurement of creatinine levels and UACR within 15 months of the index visit. Treatment reflected prescription of ACEIs or ARBs and SGLT2 inhibitors within 12 months before or 6 months following the index visit. Results: Concordance with CKD screening guidelines was assessed in 316 234 adults (median age, 59 [IQR, 50-67] years), of whom 51.5% were women; 21.7%, Black; 10.3%, Hispanic; and 67.6%, White. Only 24.9% received creatinine and UACR screening, 56.5% received 1 screening measurement, and 18.6% received neither. Hispanic ethnicity was associated with lack of screening (relative risk [RR], 1.16 [95% CI, 1.14-1.18]). In contrast, heart failure, peripheral arterial disease, and hypertension were associated with a lower risk of nonconcordance. In 4215 patients with CKD and albuminuria, 3288 (78.0%) received an ACEI or ARB; 194 (4.6%), an SGLT2 inhibitor; and 885 (21.0%), neither therapy. Peripheral arterial disease and lower eGFR were associated with lack of CKD treatment, while diuretic or statin prescription and hypertension were associated with treatment. Conclusions and Relevance: In this cohort study of patients with T2D, fewer than one-quarter received recommended CKD screening. In patients with CKD and albuminuria, 21.0% did not receive an SGLT2 inhibitor or an ACEI or an ARB, despite compelling indications. Patient-level factors may inform implementation strategies to improve CKD screening and treatment in people with T2D.

Collecting patient-reported outcome measures in the electronic health record: Lessons from the NIH pragmatic trials Collaboratory.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 27 embedded pragmatic clinical trials, and many of the studies collect patient reported outcome measures as primary or secondary outcomes. Study teams have encountered challenges in the collection of these measures, including challenges related to competing health care system priorities, clinician's buy-in for adoption of patient-reported outcome measures, low adoption and reach of technology in low resource settings, and lack of consensus and standardization of patient-reported outcome measure selection and administration in the electronic health record. In this article, we share case examples and lessons learned, and suggest that, when using patient-reported outcome measures for embedded pragmatic clinical trials, investigators must make important decisions about whether to use data collected from the participating health system's electronic health record, integrate externally collected patient-reported outcome data into the electronic health record, or collect these data in separate systems for their studies.