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Objective To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.Design Federated pharmacoepidemiological evaluation in LEGEND-T2DM.Setting 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.Participants 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.Exposure The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.Main outcomes measures The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.Results 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.Conclusions Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Background & aimsGiven ongoing challenges in non-invasive non-alcoholic liver disease (NAFLD) diagnosis, we sought to validate an ALT-based NAFLD phenotype using measures readily available in electronic health records (EHRs) and population-based studies by leveraging the clinical and genetic data in the Million Veteran Program (MVP), a multi-ethnic mega-biobank of US Veterans.MethodsMVP participants with alanine aminotransferases (ALT) >40 units/L for men and >30 units/L for women without other causes of liver disease were compared to controls with normal ALT. Genetic variants spanning eight NAFLD risk or ALT-associated loci (LYPLAL1, GCKR, HSD17B13, TRIB1, PPP1R3B, ERLIN1, TM6SF2, PNPLA3) were tested for NAFLD associations with sensitivity analyses adjusting for metabolic risk factors and alcohol consumption. A manual EHR review assessed performance characteristics of the NAFLD phenotype with imaging and biopsy data as gold standards. Genetic associations with advanced fibrosis were explored using FIB4, NAFLD Fibrosis Score and platelet counts.ResultsAmong 322,259 MVP participants, 19% met non-invasive criteria for NAFLD. Trans-ethnic meta-analysis replicated associations with previously reported genetic variants in all but LYPLAL1 and GCKR loci (PConclusionsWe validate a simple, non-invasive ALT-based NAFLD phenotype using EHR data by leveraging previously established NAFLD risk-associated genetic polymorphisms.

OBJECTIVE:We identified patients with non-tuberculous mycobacterial (NTM) disease in the US Veterans Health Administration (VHA), examined the distribution of diseases by NTM species, and explored the association between NTM disease and the frequency of clinic visits and mortality. METHODS:We combined mycobacterial isolate (from natural language processing) with ICD-9-CM diagnoses from VHA data between 2008 and 2012 and then applied modified ATS/IDSA guidelines for NTM diagnosis. We performed validation against a reference standard of chart review. Incidence rates were calculated. Two nested case-control studies (matched by age and location) were used to measure the association between NTM disease and each of 1) the frequency of outpatient clinic visits and 2) mortality, both adjusted by chronic obstructive pulmonary disease (COPD), other structural lung diseases, and immunomodulatory factors. RESULTS:NTM cases were identified with a sensitivity of 94%, a specificity of >99%. The incidence of NTM was 12.6/100k patient-years. COPD was present in 68% of pulmonary NTM. NTM incidence was highest in the southeastern US. Extra-pulmonary NTM rates increased during the study period. The incidence rate ratio of clinic visits in the first year after diagnosis was 1.3 [95%CI 1.34-1.35]. NTM patients had a hazard ratio of mortality of 1.4 [95%CI 1.1-1.9] in the 6 months after NTM identification compared to controls and 1.99 [95%CI 1.8-2.3] thereafter. CONCLUSIONS:In VHA, pulmonary NTM disease is commonly associated with COPD, with the highest rates in the southeastern US. After adjustment, NTM patients had more clinic visits and greater mortality compared to matched patients.

Purpose: Alpha-1 blockers, often used to treat benign prostatic hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storm release. The proposed treatment based on this hypothesis currently lacks support from reliable real-world evidence, however. We leverage an international network of large-scale healthcare databases to generate comprehensive evidence in a transparent and reproducible manner.Methods: In this international cohort study, we deployed electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We assessed association between alpha-1 blocker use and risks of three COVID-19 outcomes—diagnosis, hospitalization, and hospitalization requiring intensive services—using a prevalent-user active-comparator design. We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We pooled database-specific estimates through random effects meta-analysis.Results: Our study overall included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH medications. We observed no significant difference in their risks for any of the COVID-19 outcomes, with our meta-analytic HR estimates being 1.02 (95% CI: 0.92–1.13) for diagnosis, 1.00 (95% CI: 0.89–1.13) for hospitalization, and 1.15 (95% CI: 0.71–1.88) for hospitalization requiring intensive services.Conclusion: We found no evidence of the hypothesized reduction in risks of the COVID-19 outcomes from the prevalent-use of alpha-1 blockers—further research is needed to identify effective therapies for this novel disease.

Background: Randomized clinical trials are the gold standard for evaluating healthcare interventions and, more generally, add to the medical knowledge related to the treatment, diagnosis and prevention of diseases and conditions. Recent literature continues to identify health informatics methods that can help improve study efficiency throughout the life cycle of a clinical trial. Electronic medical record (EMR) data provides a mechanism to facilitate clinical trial research during the study planning and execution phases, and ultimately, can be utilized to enhance recruitment. The Department of Veterans Affairs (VA) has a strong history of clinical and epidemiological research with over four decades of data collected from Veterans it has served nationwide. The VA Informatics and Computing Infrastructure (VINCI) provides VA research investigators with a nationwide view of high-value VA patient data. Within VA, the Cooperative Studies Program (CSP) Network of Dedicated Enrollment Sites (NODES) is a consortium of nine sites that are part of an embedded clinical research infrastructure intended to provide systematic site-level solutions to issues that arise during the conduct of VA CSP clinical research. This paper describes the collaboration initiated by the Salt Lake City (SLC) node site to bring informatics and clinical trials together to enhance study planning and recruitment within the VA. Methods: The SLC VA Medical Center physically houses both VINCI and a node site and the co-location of these two groups prompted a natural collaboration on both a local and national level. One of the functions of the SLC NODES is to enhance recruitment and promote the success of CSP projects. VINCI supports these efforts by providing VA researchers access to potential population pools. VINCI can provide 1) feasibility data during study planning, and 2) active patient lists during recruitment. The process for CSP study teams to utilize these services involves regulatory documentation, development of queries, revisions to the initial data request, and ongoing communications with several key study personnel including the requesting research team, study statisticians, and VINCI data managers. Results: The early efforts of SLC NODES and VINCI aimed to provide patient lists exclusively to the SLC CSP study teams for the following purposes: 1) increasing recruitment for trials that were struggling to meet their respective enrollment goals, and 2) decreasing the time required by study coordinators to complete chart review activities. This effort was expanded to include multiple CSP sites and studies. To date, SLC NODES has facilitated the delivery of these VINCI services to nine active CSP studies. Conclusion: The ability of clinical trial study teams to successfully plan and execute their respective trials is contingent upon their proficiency in obtaining data that will help them efficiently and effectively recruit and enroll eligible participants. This collaboration demonstrates that the utilization of a model that partners two distinct entities, with similar objectives, was effective in the provision of feasibility and patient lists to clinical trial study teams and facilitation of clinical trial research within a large, integrated healthcare system. Keywords: Clinical trial recruitment, Clinical informatics, Cooperative studies program (CSP), Network of dedicated enrollment sites (NODES), VA informatics and computing infrastructure (VINCI)

Abstract Background Current clinical guidelines recommend epidermal growth factor receptor (EGFR) mutational testing in patients with metastatic non-small cell lung cancer (NSCLC) to predict the benefit of the tyrosine kinase inhibitor erlotinib as first-line treatment. Proteomic (VeriStrat) testing is recommended for patients with EGFR negative or unknown status when erlotinib is being considered. Departure from this clinical algorithm can increase costs and may result in worse outcomes. We examined EGFR and proteomic testing among patients with NSCLC within the Department of Veterans Affairs (VA). We explored adherence to guidelines and the impact of test results on treatment decisions and cost of care. Methods Proteomic and EGFR test results from 2013 to 2015 were merged with VA electronic health records and pharmacy data. Chart reviews were conducted. Cases were categorized based on the appropriateness of testing and treatment. Results Of the 69 patients with NSCLC who underwent proteomic testing, 33 (48%) were EGFR-negative and 36 (52%) did not have documented EGFR status. We analyzed 138 clinical decisions surrounding EGFR/proteomic testing and erlotinib treatment. Most decisions (105, or 76%) were concordant with clinical practice guidelines. However, for 24 (17%) decisions documentation of testing or justification of treatment was inadequate, and 9 (7%) decisions represented clear departures from guidelines. Conclusion EGFR testing, the least expensive clinical intervention analyzed in this study, was significantly underutilized or undocumented. The records of more than half of the patients lacked information on EGFR status. Our analysis illustrated several clinical scenarios where the timing of proteomic testing and erlotinib diverged from the recommended algorithm, resulting in excessive costs of care with no documented improvements in health outcomes.

Background Canadian Cardiovascular Society (CCS) angina severity classification is associated with mortality, myocardial infarction, and coronary revascularization in clinical trial and registry data. The objective of this study was to determine associations between CCS class and all‐cause mortality and healthcare utilization, using natural language processing to extract CCS classifications from clinical notes. Methods and Results In this retrospective cohort study of veterans in the United States with stable angina from January 1, 2006, to December 31, 2013, natural language processing extracted CCS classifications. Veterans with a prior diagnosis of coronary artery disease were excluded. Outcomes included all‐cause mortality (primary), all‐cause and cardiovascular‐specific hospitalizations, coronary revascularization, and 1‐year healthcare costs. Of 299 577 veterans identified, 14 216 (4.7%) had ≥1 CCS classification extracted by natural language processing. The mean age was 66.6±9.8 years, 99% of participants were male, and 81% were white. During a median follow‐up of 3.4 years, all‐cause mortality rates were 4.58, 4.60, 6.22, and 6.83 per 100 person‐years for CCS classes I, II, III, and IV, respectively. Multivariable adjusted hazard ratios for all‐cause mortality comparing CCS II, III, and IV with those in class I were 1.05 (95% CI, 0.95–1.15), 1.33 (95% CI, 1.20–1.47), and 1.48 (95% CI, 1.25–1.76), respectively. The multivariable hazard ratio comparing CCS IV with CCS I was 1.20 (95% CI, 1.09–1.33) for all‐cause hospitalization, 1.25 (95% CI, 0.96–1.64) for acute coronary syndrome hospitalizations, 1.00 (95% CI, 0.80–1.26) for heart failure hospitalizations, 1.05 (95% CI, 0.88–1.25) for atrial fibrillation hospitalizations, 1.92 (95% CI, 1.40–2.64) for percutaneous coronary intervention, and 2.51 (95% CI, 1.99–3.16) for coronary artery bypass grafting surgery. Conclusions Natural language processing–extracted CCS classification was positively associated with all‐cause mortality and healthcare utilization, demonstrating the prognostic importance of anginal symptom assessment and documentation.