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BackgroundUniversity of California at Los Angeles Health implemented a Best Practice Advisory (BPA) alert for the initiation of an angiotensin-converting enzyme inhibitor (ACEI) or angiotensin-receptor blocker (ARB) for individuals with diabetes. The BPA alert was configured with a “chart closure” hard stop, which demanded a response before closing the chart. ObjectiveThe aim of the study was to evaluate whether the implementation of the BPA was associated with changes in ACEI and ARB prescribing during primary care encounters for patients with diabetes. MethodsWe defined ACEI and ARB prescribing opportunities as primary care encounters in which the patient had a diabetes diagnosis, elevated blood pressure in recent encounters, no active ACEI or ARB prescription, and no contraindications. We used a multivariate logistic regression model to compare the change in the probability of an ACEI or ARB prescription during opportunity encounters before and after BPA implementation in primary care sites that did (n=30) and did not (n=31) implement the BPA. In an additional subgroup analysis, we compared ACEI and ARB prescribing in BPA implementation sites that had also implemented a pharmacist-led medication management program. ResultsWe identified a total of 2438 opportunity encounters across 61 primary care sites. The predicted probability of an ACEI or ARB prescription increased significantly from 11.46% to 22.17% during opportunity encounters in BPA implementation sites after BPA implementation. However, in the subgroup analysis, we only observed a significant improvement in ACEI and ARB prescribing in BPA implementation sites that had also implemented the pharmacist-led program. Overall, the change in the predicted probability of an ACEI or ARB prescription from before to after BPA implementation was significantly greater in BPA implementation sites compared with nonimplementation sites (difference-in-differences of 11.82; P

ImportanceAmong patients with type 2 diabetes (T2D), Hispanic individuals are more likely than non-Hispanic White individuals to develop diabetes-related complications.ObjectiveTo examine the association of a pharmacist-led intervention (UCMyRx) with hemoglobin A1c (HbA1c) and systolic blood pressure (SBP) among Hispanic patients with T2D.Design, setting, and participantsThis quality improvement study used electronic health record data and a difference-in-differences study design to evaluate the association of UCMyRx exposure with changes in HbA1c concentration and SBP among Hispanic patients with T2D, relative to usual care, at University of California, Los Angeles primary care clinics between February and April of 2023. The study population included patients with an International Classification of Diseases, Ninth Revision/International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis of T2D, self-reporting Hispanic ethnicity, age 18 years or older, with 1 or more visits with a UCMyRx pharmacist (treatment) or 2 or more visits, 2 or more years apart, during the study window (comparison). Additionally, patients had to have the following observations during the study window (March 2, 2013-December 31, 2018): (1) a HbA1c 8% or higher, anywhere between 365 days before and 14 days after the index date (date of the first UCMyRx visit or a randomly generated index date) and a follow-up HbA1c measure within 120 to 365 days after the index date (n = 396) and/or (2) a SBP 140 mm Hg or higher between 365 days before and 14 days after the index date, and a follow-up SBP measure within 120 to 450 days after the index date (n = 795).ExposurePharmacists review laboratory results/vital signs, perform medication reconciliation, and develop personally tailored interventions to address adherence barriers and increase guideline-concordant care.Main outcomes and measuresPre- to post-index date changes in HbA1c and SBP.ResultsOf the 931 unique patients with T2D analyzed, the mean (SD) age was 64 (14.1) years, and 552 (59.3%) were female. In adjusted analyses, having 1 or more UCMyRx visits was associated with a reduction in HbA1c concentration (β = -0.46%; 95% CI, -0.84% to -0.07%) but no change in SBP (β = -1.71 mm Hg; 95% CI, -4.00 to 0.58 mm Hg).Conclusions and relevanceIn this quality improvement study of UCMyRx among Hispanic patients with T2D, a negative association was observed between UCMyRx exposure and HbA1c concentration but not SBP. Pharmacist-led intervention may be a strategy for improving outcomes among Hispanic patients with T2D.

ObjectiveWe aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions.Materials and methodsWe preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution.ResultsThe smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline.DiscussionThe low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows.ConclusionBlockchain technology can be used to support federated data analysis among multiple institutions.

Background: The risks of osteoporosis and breast cancer are increasing in elderly women. Bisphosphonates and denosumab are recommended for treatment of osteoporosis. They have different and overlapping pharmacodynamics and previous studies have shown conflicting results regarding their risk association with breast cancer. We intend to further look into this issue through a comparative study. Methods: Electronic health records of 91,626 women older than 50 years with no previous history of malignancy and no nonbreast cancer during follow-up were retrieved from southern California and retrospectively analyzed using univariate, bivariate, and log-rank tests. Medication use, breast cancer risk, and associated demographic and clinical history were assessed. Results: Over an average of 3.6 years follow-up, the breast cancer relative risks (RRs) counted after 365 days of latency are 1.12 (95% confidence interval [CI]: 0.64-1.97) for denosumab ever users and 0.37 (95% CI: 0.21-0.66) for bisphosphonates ever users, when covariates are comparable. The significant difference is supported by the Log-rank test (p = 0.0004). Excluding statins coprescribers, the breast cancer RR is 1.31 (0.71, 2.43) in denosumab group and 0.26 (0.11, 0.62) in bisphosphonates group. There is a reduced RR in statins ever users (0.47, 95% CI: 0.38-0.58), and the breast cancer risk difference is not significant between concomitant denosumab/statins and bisphosphonates/statins ever users with RR 0.65 (0.16, 2.58) versus 0.55 (0.26, 1.16), p = 0.692. Conclusions: Our data support an association of lower breast cancer risk with bisphosphonates use in elderly women. We did not observe a lower breast cancer risk in denosumab group; however, our data revealed a potential lower breast cancer risk in denosumab users with concurrent statins use and this requires further study.

ObjectiveTo utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and methodsWe developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.ResultsOur public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.Discussion and conclusionsWe present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

ObjectiveThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.Materials and methodsTwelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.ResultsThe full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.DiscussionWe developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.ConclusionsWe developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

BackgroundBlack individuals with type 2 diabetes suffer disproportionate morbidity and mortality relative to whites with type 2 diabetes, irrespective of health insurance coverage.ObjectiveExamine the impact of a primary care-embedded clinical pharmacist-led intervention (UCMyRx) on cardiovascular risk factor control among blacks with type 2 diabetes in a large healthcare system.DesignWe used data extracted from the electronic health records (EHR) system and a difference-in-differences study design with a propensity-matched comparison group to evaluate the impact of UCMyRx on HbA1c and systolic blood pressure (SBP) among black patients with type 2 diabetes, relative to usual care.ParticipantsIndividuals with type 2 diabetes identified as either black or African American in the EHR that were ≥ 18 years of age that had the following observations during the study window (03/02/2013-12/31/18: (1) HbA1C ≥ 8%, at least once, anywhere between 365 days before and 14 days after the UCMyRx visit and a follow-up HbA1c measure within 120 to 365 days after the visit and/or (2) SBP ≥ 140 mmHg at least once between 365 days before and 14 days after the UCMyRx visit that had a follow-up SBP measure within 120 to 450 days after the visit.InterventionUCMyRx pharmacists review labs and vital signs, perform medication reconciliation, use a standardized survey to assess barriers to medication adherence, and develop tailored interventions to improve medication adherence.Main measuresChange in HbA1c and change in SBP from before to after the first UCMyRx visit.Key resultsHaving at least one visit with a UCMyRx clinical pharmacist was associated with a significant reduction in HbA1c (- 0.4%, p value = .01); however, there was no significant impact on SBP (- .051 mmHg, p value = 0.74).ConclusionsThe UCMyRx intervention is a useful strategy for improving HbA1c control among blacks with type 2 diabetes.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

BACKGROUND University of California at Los Angeles Health implemented a Best Practice Advisory (BPA) alert for the initiation of an angiotensin-converting enzyme inhibitor (ACEI) or angiotensin-receptor blocker (ARB) for individuals with diabetes. The BPA alert was configured with a “chart closure” hard stop, which demanded a response before closing the chart. OBJECTIVE The aim of the study was to evaluate whether the implementation of the BPA was associated with changes in ACEI and ARB prescribing during primary care encounters for patients with diabetes. METHODS We defined ACEI and ARB prescribing opportunities as primary care encounters in which the patient had a diabetes diagnosis, elevated blood pressure in recent encounters, no active ACEI or ARB prescription, and no contraindications. We used a multivariate logistic regression model to compare the change in the probability of an ACEI or ARB prescription during opportunity encounters before and after BPA implementation in primary care sites that did (n=30) and did not (n=31) implement the BPA. In an additional subgroup analysis, we compared ACEI and ARB prescribing in BPA implementation sites that had also implemented a pharmacist-led medication management program. RESULTS We identified a total of 2438 opportunity encounters across 61 primary care sites. The predicted probability of an ACEI or ARB prescription increased significantly from 11.46% to 22.17% during opportunity encounters in BPA implementation sites after BPA implementation. However, in the subgroup analysis, we only observed a significant improvement in ACEI and ARB prescribing in BPA implementation sites that had also implemented the pharmacist-led program. Overall, the change in the predicted probability of an ACEI or ARB prescription from before to after BPA implementation was significantly greater in BPA implementation sites compared with nonimplementation sites (difference-in-differences of 11.82; P

Introduction: Trapped lung, characterized by atelectatic lung unable to reexpand and fill the thoracic cavity due to a restricting fibrous visceral pleural peel, is occasionally seen in patients with end-stage liver disease complicated by hepatic hydrothorax. Limited data suggest that trapped lung prior to orthotopic liver transplantation may be associated with poor outcomes. Research Question: What is the clinical significance of trapped lung in patients receiving orthotopic liver transplantation? Design: We performed a retrospective analysis of patients who underwent liver transplantation over an 8-year period. Baseline clinical characteristics and postoperative outcomes of adult patients with trapped lung were analyzed and compared to the overall cohort of liver transplant recipients and controls matched 3:1 based on age, sex, Model for End-Stage Liver Disease (MELD) score, and presence of pleural effusion. Results: Of the 1193 patients who underwent liver transplantation, we identified 20 patients (1.68%) with trapped lung. The probability of 1 and 2-year survival were 75.0% and 57.1%, compared to 85.6% and 80.4% (p = 0.02) in all liver transplant recipients and 87.9% and 81.1% (p = 0.03) in matched controls respectively. Patients with trapped lung had a longer hospital length of stay compared to the total liver transplant population (geometric mean 54.9 ± 8.4 vs. 27.2 ± 0.7 days, p ≤ 0.001), when adjusted for age and MELD score. Discussion: Patients with trapped prior to orthotopic liver transplantation have increased probability of mortality as well as increased health care utilization. This is a small retrospective analysis, and further prospective investigation is warranted. [ABSTRACT FROM AUTHOR]

Objective: To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic., Design, Setting and Participants: This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic., Primary and Secondary Outcome Measures: The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation., Results: Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001)., Conclusions: Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic., Objective: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic., Methods: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19., Results: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain., Conclusions: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve., (©Griffin M Weber, Harrison G Zhang, Sehi L'Yi, Clara-Lea Bonzel, Chuan Hong, Paul Avillach, Alba Gutiérrez-Sacristán, Nathan P Palmer, Amelia Li Min Tan, Xuan Wang, William Yuan, Nils Gehlenborg, Anna Alloni, Danilo F Amendola, Antonio Bellasi, Riccardo Bellazzi, Michele Beraghi, Mauro Bucalo, Luca Chiovato, Kelly Cho, Arianna Dagliati, Hossein Estiri, Robert W Follett, Noelia García Barrio, David A Hanauer, Darren W Henderson, Yuk-Lam Ho, John H Holmes, Meghan R Hutch, Ramakanth Kavuluru, Katie Kirchoff, Jeffrey G Klann, Ashok K Krishnamurthy, Trang T Le, Molei Liu, Ne Hooi Will Loh, Sara Lozano-Zahonero, Yuan Luo, Sarah Maidlow, Adeline Makoudjou, Alberto Malovini, Marcelo Roberto Martins, Bertrand Moal, Michele Morris, Danielle L Mowery, Shawn N Murphy, Antoine Neuraz, Kee Yuan Ngiam, Marina P Okoshi, Gilbert S Omenn, Lav P Patel, Miguel Pedrera Jiménez, Robson A Prudente, Malarkodi Jebathilagam Samayamuthu, Fernando J Sanz Vidorreta, Emily R Schriver, Petra Schubert, Pablo Serrano Balazote, Byorn WL Tan, Suzana E Tanni, Valentina Tibollo, Shyam Visweswaran, Kavishwar B Wagholikar, Zongqi Xia, Daniela Zöller, The Consortium For Clinical Characterization Of COVID-19 By EHR (4CE), Isaac S Kohane, Tianxi Cai, Andrew M South, Gabriel A Brat. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.10.2021.)

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)