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Researchers across the translational research continuum have emphasized the importance of integrating genomics into their research program. To date capacity and resources for genomics research have been limited; however, a recent population-wide genomic screening initiative launched at the Medical University of South Carolina in partnership with Helix has rapidly advanced the need to develop appropriate infrastructure for genomics research at our institution. We conducted a survey with researchers from across our institution (n = 36) to assess current knowledge about genomics health, barriers, and facilitators to uptake, and next steps to support translational research using genomics. We also completed 30-minute qualitative interviews with providers and researchers from diverse specialties (n = 8). Quantitative data were analyzed using descriptive analyses. A rapid assessment process was used to develop a preliminary understanding of each interviewee's perspective. These interviews were transcribed and coded to extract themes. The codes included types of research, alignment with precision health, opportunities to incorporate precision health, examples of researchers in the field, barriers, and facilitators to uptake, educational activity suggestions, questions to be answered, and other observations. Themes from the surveys and interviews inform implementation strategies that are applicable not only to our institution, but also to other organizations interested in making genomic data available to researchers to support genomics-informed translational research., (© Society of Behavioral Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

We use the implementation science framework RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) to describe outcomes of In Our DNA SC, a population-wide genomic screening (PWGS) program. In Our DNA SC involves participation through clinical appointments, community events, or at home collection. Participants provide a saliva sample that is sequenced by Helix, and those with a pathogenic variant or likely pathogenic variant for CDC Tier 1 conditions are offered free genetic counseling. We assessed key outcomes among the first cohort of individuals recruited. Over 14 months, 20,478 participants enrolled, and 14,053 samples were collected. The majority selected at-home sample collection followed by clinical sample collection and collection at community events. Participants were predominately female, White (self-identified), non-Hispanic, and between the ages of 40-49. Participants enrolled through community events were the most racially diverse and the youngest. Half of those enrolled completed the program. We identified 137 individuals with pathogenic or likely pathogenic variants for CDC Tier 1 conditions. The majority (77.4%) agreed to genetic counseling, and of those that agreed, 80.2% completed counseling. Twelve clinics participated, and we conducted 108 collection events. Participants enrolled at home were most likely to return their sample for sequencing. Through this evaluation, we identified facilitators and barriers to implementation of our state-wide PWGS program. Standardized reporting using implementation science frameworks can help generalize strategies and improve the impact of PWGS., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Objective: Disparities in pregnancy outcomes among women with SLE remain understudied, with few available racially diverse datasets. We sought to identify disparities between Black and White women in pregnancy outcomes within academic institutions in the United States., Methods: Using the Common Data Model electronic medical record (EMR)-based datasets within the Carolinas Collaborative, we identified women with pregnancy delivery data (2014-2019) and ≥1 SLE International Classification of Diseases 9 or 10 code (ICD9/10) code. From this dataset, we identified four cohorts of SLE pregnancies, three based on EMR-based algorithms and one confirmed with chart review. We compared the pregnancy outcomes identified in each of these cohorts for Black and White women., Results: Of 172 pregnancies in women with ≥1 SLE ICD9/10 code, 49% had confirmed SLE. Adverse pregnancy outcomes occurred in 40% of pregnancies in women with ≥1 ICD9/10 SLE code and 52% of pregnancies with confirmed SLE. SLE was frequently over-diagnosed in women who were White, resulting in 40-75% lower rates of adverse pregnancy outcomes in EMR-derived vs confirmed SLE cohorts. Over-diagnosis was less common for Black women with pregnancy outcomes 12-20% lower in EMR-derived vs confirmed SLE cohorts. Black women had higher rates of adverse pregnancy outcomes than White women in the EMR-derived, but not the confirmed cohorts., Conclusion: EMR-derived cohorts of pregnancies in women who are Black, but not White, provided accurate estimations of pregnancy outcomes. The data from the confirmed SLE pregnancies suggest that all women with SLE, regardless of race, referred to academic centres remain at very high risk for adverse pregnancy outcome., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Emerging evidence suggests the potential importance of inspiratory driving pressure (DP) and respiratory system elastance (E RS ) on outcomes among patients with the acute respiratory distress syndrome. Their association with outcomes among heterogeneous populations outside of a controlled clinical trial is underexplored. We used electronic health record (EHR) data to characterize the associations of DP and E RS with clinical outcomes in a real-world heterogenous population., Design: Observational cohort study., Setting: Fourteen ICUs in two quaternary academic medical centers., Patients: Adult patients who received mechanical ventilation for more than 48 hours and less than 30 days., Interventions: None., Measurements and Main Results: EHR data from 4,233 ventilated patients from 2016 to 2018 were extracted, harmonized, and merged. A minority of the analytic cohort (37%) experienced a Pao 2 /Fio 2 of less than 300. A time-weighted mean exposure was calculated for ventilatory variables including tidal volume (V T ), plateau pressures (P PLAT ), DP, and E RS . Lung-protective ventilation adherence was high (94% with V T < 8.5 mL/kg, time-weighted mean V T = 6. 8 mL/kg, 88% with P PLAT ≤ 30 cm H 2 O). Although time-weighted mean DP (12.2 cm H 2 O) and E RS (1.9 cm H 2 O/[mL/kg]) were modest, 29% and 39% of the cohort experienced a DP greater than 15 cm H 2 O or an E RS greater than 2 cm H 2 O/(mL/kg), respectively. Regression modeling with adjustment for relevant covariates determined that exposure to time-weighted mean DP (> 15 cm H 2 O) was associated with increased adjusted risk of mortality and reduced adjusted ventilator-free days independent of adherence to lung-protective ventilation. Similarly, exposure to time-weighted mean E RS greater than 2 cm H 2 O/(mL/kg) was associated with increased adjusted risk of mortality., Conclusions: Elevated DP and E RS are associated with increased risk of mortality among ventilated patients independent of severity of illness or oxygenation impairment. EHR data can enable assessment of time-weighted ventilator variables and their association with clinical outcomes in a multicenter real-world setting., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

The potential utilization of a cold-contact approach to research recruitment, where members of the research team are unknown to the patient, has grown with the expanded use of electronic health records (EHRs) and affiliated patient portals. Institutions that permit this strategy vary in their implementation and management of it but tend to lean towards more conservative approaches. This process paper describes the Medical University of South Carolina's transition to an opt-out model of "cold-contact" recruitment (known as patient outreach recruitment or POR), wherein patients can be contacted so long as they do not express an unwillingness to receive such communication. The work highlights the benefits of this model by explaining how it, in many ways, supports and protects autonomy, beneficence, and justice for patients. The paper then describes the process of standing up the recruitment strategy, communicating the change to patients and the community, and documenting study team contact and patient research preference. Data supporting increased access to potentially eligible patients of greater diversity as well as initial researcher feedback on perceived success of POR is also shared. The paper ends with a discussion of next steps to enhance the POR process via more detailed data collection and reengagement with community stakeholders., Competing Interests: The authors have no conflicts of interest to disclose., (© The Author(s) 2023.)

Existing recommendations for mechanical ventilation are based on studies that under-sampled or excluded obese and severely obese individuals., Objective: To determine if driving pressure (DP) and total respiratory system elastance (E rs ) differ among normal/overweight (body mass index [BMI] < 30 kg/m 2 ), obese, and severely obese ventilator-dependent respiratory failure (VDRF) patients and if there any associations with clinical outcomes., Design Setting and Participants: Retrospective observational cohort study during 2016-2018 at two tertiary care academic medical centers using electronic health record data from the first 2 full days of mechanical ventilation. The cohort was stratified by BMI classes to measure median DP, time-weighted mean tidal volume, plateau pressure, and E rs for each BMI class., Setting and Participants: Mechanically ventilated patients in medical and surgical ICUs., Main Outcomes and Measures: Primary outcome and effect measures included relative risk of in-hospital mortality, ventilator-free days, ICU length of stay, and hospital length of stay with multivariable adjustment., Results: The cohort included 3,204 patients with 976 (30.4%) and 382 (11.9%) obese and severely obese patients, respectively. Severe obesity was associated with a DP greater than or equal to 15 cm H 2 O (relative risk [RR], 1.51 [95% CI, 1.26-1.82]) and E rs greater than or equal to 2 cm H 2 O/(mL/kg) (RR, 1.31 [95% CI, 1.14-1.49]). Despite elevated DP and E rs , there were no differences in in-hospital mortality, ventilator-free days, or ICU length of stay among all three groups., Conclusions and Relevance: Despite higher DP and E RS among obese and severely obese VDRF patients, there were no differences in in-hospital mortality or duration of mechanical ventilation, suggesting that DP has less prognostic value in obese and severely obese VDRF patients., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

COVID-19 vaccination uptake has been suboptimal, even in high-risk populations. New approaches are needed to bring vaccination data to the groups leading outreach efforts. This article describes work to make state-level vaccination data more accessible by extending the Bulk Fast Healthcare Interoperability Resource (FHIR) standard to better support the repeated retrieval of vaccination data for coordinated outreach efforts. We also describe a corresponding low-foot-print software for population outreach that automates repeated checks of state-level immunization data and prioritizes outreach by social determinants of health. Together this software offers an integrated approach to addressing vaccination gaps. Several extensions to the Bulk FHIR protocol were needed to support bulk query of immunization records. These are described in detail. The results of a pilot study, using the outreach tool to target a population of 1500 patients are also described. The results confirmed the limitations of current patient-by-patient approach for querying state immunizations systems for population data and the feasibility of a Bulk FHIR approach., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Electronic health records (EHRs) represent powerful tools to study rare diseases. Our objective was to develop and validate EHR algorithms to identify systemic lupus erythematosus (SLE) births across centers., Methods: We developed algorithms in a training set using an EHR with over 3 million subjects and validated the algorithms at 2 other centers. Subjects at all 3 centers were selected using ≥1 code for SLE International Classification of Diseases, Ninth Revision (ICD-9) or SLE International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification (ICD-10-CM) and ≥1 ICD-9 or ICD-10-CM delivery code. A subject was a case if diagnosed with SLE by a rheumatologist and had a birth documented. We tested algorithms using SLE ICD-9 or ICD-10-CM codes, antimalarial use, a positive antinuclear antibody ≥1:160, and ever checked double-stranded DNA or complement, using both rule-based and machine learning methods. Positive predictive values (PPVs) and sensitivities were calculated. We assessed the impact of case definition, coding provider, and subject race on algorithm performance., Results: Algorithms performed similarly across all 3 centers. Increasing the number of SLE codes, adding clinical data, and having a rheumatologist use the SLE code all increased the likelihood of identifying true SLE patients. All the algorithms had higher PPVs in African American versus White SLE births. Using machine learning methods, the total number of SLE codes and an SLE code from a rheumatologist were the most important variables in the model for SLE case status., Conclusion: We developed and validated algorithms that use multiple types of data to identify SLE births in the EHR. Algorithms performed better in African American mothers than in White mothers., (© 2020 American College of Rheumatology.)

Low-accruing clinical trials delay translation of research breakthroughs into the clinic, expose participants to risk without providing meaningful clinical insight, increase the cost of therapies, and waste limited resources. By tracking patient accrual, Clinical and Translational Science Awards hubs can identify at-risk studies and provide them the support needed to reach recruitment goals and maintain financial solvency. However, tracking accrual has proved challenging because relevant patient- and protocol-level data often reside in siloed systems. To address this fragmentation, in September 2020 the South Carolina Clinical and Translational Research Institute, with an academic home at the Medical University of South Carolina, implemented a clinical trial management system (CTMS), with its access to patient-level data, and incorporated it into its Research Integrated Network of Systems (RINS), which links study-level data across disparate systems relevant to clinical research. Within the first year of CTMS implementation, 324 protocols were funneled through CTMS/RINS, with more than 2600 participants enrolled. Integrated data from CTMS/RINS have enabled near-real-time assessment of patient accrual and accelerated reimbursement from industry sponsors. For institutions with bioinformatics or programming capacity, the CTMS/RINS integration provides a powerful model for tracking and improving clinical trial efficiency, compliance, and cost-effectiveness., Competing Interests: The authors have no conflicts of interest to disclose., (© The Author(s) 2022.)

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.)

Objective: Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data from those systems., Materials and Methods: In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes., Results: Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting., Discussion: Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart., Conclusion: By applying data warehousing principles to federate data at the "study" level, the RINS project reduced data fragmentation and promoted research systems integration., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., Competing Interests: Competing interestsR.B. and A.M. are shareholders of Biomeris s.r.l., (© The Author(s) 2020.)