Patients hospitalized with complicated urinary tract infections (cUTI) caused by multidrug-resistant uropathogens often need intravenous (IV) carbapenems, which can prolong inpatient (IP) stay and lead to increased adverse outcomes and resource utilization. This study aimed to determine the difference in duration between clinically ready for discharge (CRFD) status and actual hospital discharge date for hospitalized patients with cUTI treated with IV carbapenems. Patients aged ≥18 years who received IV carbapenem for cUTI with a positive urine culture between January 2019 –June 2024 were assessed retrospectively using structured electronic health records and unstructured clinical notes from the Mass General Brigham Research Patient Data Registry. Stringent (missing data based on the last observation of CRFD criterion carried forward) and lenient (missing data assumed to indicate CRFD criterion met) rule-based definitions for CRFD (Table 1), based on literature review and clinical guidelines and developed following a feasibility assessment of data availability and completeness, were used to determine when CRFD status was achieved. Patients (N=333) had a median (interquartile range [IQR]) age of 73.0 (63.0–83.0) years, 58.3% were female and 79.6% were White (Table 2). The median (IQR) duration of IP stay was 6.0 (4.0–9.0) days. Among patients tested, 74.1% (n=229/309) had extended-spectrum β-lactamase-producing Enterobacterales; 78.4% (n=196/250) and 54.7% (n=174/318) had isolates not-susceptible to fluoroquinolones and trimethoprim-sulfamethoxazole, respectively (Table 2). The stringent definition identified 35.1% of patients as CRFD before actual discharge, with a mean [median] difference between CRFD and actual discharge dates of 2.9 [2.0] days (range: 1.0–8.0 days). Using the lenient definition, 57.1% of patients were CRFD by a mean [median] of 3.2 [3.0] days (range: 1.0–10.0 days) before actual discharge. This study suggests that, even by stringent application of criteria, more than a third of patients with cUTI remained hospitalized longer than clinically necessary, possibly to receive IV carbapenem treatment. These findings emphasize the unmet need for new effective oral antibiotic treatments. Funding: GSK study 221816. Serena P. Koenig, MPH, MD, Brigham and Women's Hospital: Employee|GSK: Grant/Research Support Jeffrey J. Ellis, PharmD, MS, GSK: Employee|GSK: Stocks/Bonds (Public Company) Douglas Boettner, PhD, GSK: Employee|GSK: Stocks/Bonds (Public Company) Lindsey Parker, PharmD, GSK: Employee|GSK: Stocks/Bonds (Public Company) Rose Chang, ScD, Analysis Group: Employee|GSK: Grant/Research Support Louise Yu, MS, Analysis Group: Employee|GSK: Grant/Research Support Emily Reichert, MS, Analysis Group, Inc.: Employee|GSK: Grant/Research Support Joanne Chukwueke, MPH, Analysis Group, Inc.: Employee|GSK: Grant/Research Support Zheyi Cao, MS, Analysis Group, Inc.: Employee|GSK: Grant/Research Support Yichuan Grace Hseih, PhD, GSK: Grant/Research Support|Mass General Brigham: Employee Christopher Herrick, MBA, GSK: Grant/Research Support|Mass General Brigham: Employee Mei Sheng Duh, MPH, ScD, Analysis Group: Employee|GSK: Grant/Research Support Shawn N. Murphy, MD, PhD, GSK: Grant/Research Support|Mass General Brigham: Employee

BACKGROUNDPrevious epidemiologic studies of autoimmune diseases in the US have included a limited number of diseases or used metaanalyses that rely on different data collection methods and analyses for each disease.METHODSTo estimate the prevalence of autoimmune diseases in the US, we used electronic health record data from 6 large medical systems in the US. We developed a software program using common methodology to compute the estimated prevalence of autoimmune diseases alone and in aggregate that can be readily used by other investigators to replicate or modify the analysis over time.RESULTSOur findings indicate that over 15 million people, or 4.6% of the US population, have been diagnosed with at least 1 autoimmune disease from January 1, 2011, to June 1, 2022, and 34% of those are diagnosed with more than 1 autoimmune disease. As expected, females (63% of those with autoimmune disease) were almost twice as likely as males to be diagnosed with an autoimmune disease. We identified the top 20 autoimmune diseases based on prevalence and according to sex and age.CONCLUSIONHere, we provide, for what we believe to be the first time, a large-scale prevalence estimate of autoimmune disease in the US by sex and age.FUNDINGAutoimmune Registry Inc., the National Heart Lung and Blood Institute, the National Center for Advancing Translational Sciences, the Intramural Research Program of the National Institute of Environmental Health Sciences.

Background: Essential thrombocythemia (ET) is a rare, chronic myeloproliferative neoplasm characterized by sustained thrombocytosis, risk of thrombosis and bleeding, and progression to post-ET myelofibrosis (MF) or blast-phase disease. Cytoreductive therapy is recommended for thrombosis prevention in high-risk or symptomatic patients, but despite standard of care (SOC) therapies, many ET patients continue to experience disease complications, inadequate symptom control and suboptimal outcomes. Contemporary real-world studies are needed to evaluate disease burden and unmet treatment needs in ET. Aims: To characterize the treatment landscape and clinical outcomes among patients with ET treated with cytoreductive therapy in the U.S. Methods: Adult patients who met the following inclusion criteria were identified from the Mass General Brigham Research Patient Data Registry between January 1, 2012 and September 5, 2024: 1) an ET diagnosis code (by ICD10) associated with a hematologist/oncologist visit (i.e., first ET diagnosis); 2) a subsequent physician visit with an associated ET diagnosis code; 3) elevated platelet count (≥450x109/L) in the 6-months prior to first ET diagnosis; and (4) prescription for a SOC cytoreductive therapy on or after the first ET diagnosis. The prescription date of the first-observed cytoreductive agent (i.e., first-line [1L] therapy) was defined as the index date. 1L treatment discontinuation was defined as a switch to a different 2L therapy, or as a gap of >60 days following the end of 1L therapy unless a subsequent prescription of the same agent was observed after the 60 day gap (defined as treatment interruption). Clinical outcomes including thrombotic events and major bleeding events were summarized from the index date to the earliest of 1L treatment discontinuation, progression to MF or blast phase disease, or end of follow-up. Blast phase disease was defined by ≥1 acute myeloid leukemia diagnosis code. Major bleeding events were defined using modified ISTH criteria including those occurring in a critical area or organ in an inpatient setting, fatal bleeding (defined as bleeding in a critical area or organ preceding death by ≤45 days), or symptomatic bleeding (defined as bleeding leading to a drop in hemoglobin ≥2g/dL or red blood cell transfusion within 48 hours). Results: Among 673 patients who met the inclusion criteria, median (range) age at the index date was 70 (20 - 96) years, 67.3% were female, 88.0% were white. 14.1% had a history of thrombosis in the 6-months prior to 1L initiation. The mean (SD) follow-up time was 41.0 (31.3) months. The most common 1L treatment was hydroxyurea (94.1%); use of other treatments (e.g., interferon alfa [1.8%], ruxolitinib [1.5%], anagrelide [1.3%], and busulfan [0.3%]) was rare. 1L treatment modifications occurred frequently: 235 patients (34.9%) experienced treatment interruptions and 131 (19.5%) discontinued treatment. Among those who discontinued, median (interquartile range) time from 1L initiation to discontinuation was 14.1 (5.1 – 36.6) months; of these, 75 (57.3%) received no further treatment during our follow-up. Among all 1L treated patients, 56 (8.3%) switched to 2L therapy. Overall, 30.5% of patients experienced ≥1 ET-related complication after the index date, including thrombotic events (23.2%), major bleeding events (3.6%), and progression to MF (4.9%) or blast phase disease (2.1%). Sixty-six patients (9.8%) died of any cause. Among patients with available labs, 65.6% (368/561) of patients had an average platelet count ≥600 x 109/L in the first 6 months post-index, and 30.1% (137/455) during months 6-12. Conclusions: Despite SOC treatment with cytoreductive therapy, a significant proportion (30.5%) of ET patients experienced disease-related complications including thrombosis, disease progression, and major bleeding. These complications may be due in part to frequent therapy interruption or discontinuation (47.7% of patients), underscoring the limitations of SOC ET therapy. Our findings highlight ongoing challenges in ET management and the need for alternative therapeutic strategies to improve long-term outcomes in ET.

OBJECTIVE: To assess risk of major adverse cardiovascular events associated with acute antimigraine treatments in patients with preexisting cardiovascular conditions. PATIENTS AND METHODS: In this retrospective, longitudinal, observational cohort study, we examined data from the Mass General Brigham Research Patient Data Registry on adults who received one or more prescriptions for acute migraine (index date) between January 2006 and December 2020 and who had one or more diagnoses of a cardiovascular condition during 12 months prior to index date. Study endpoints were nonfatal myocardial infarction, nonfatal stroke, and in facility all cause mortality, assessed separately and together as a composite major adverse cardiovascular events proxy endpoint. RESULTS: Prescriptions were identified for 4016 oral triptans, 6084 opioids/butalbital, and 2021 NSAIDs. Hazard ratios (HRs) for major adverse cardiovascular events were 0.38 (95% CI: 0.22, 0.67; P=.001) comparing oral triptans with opioids/butalbital and 0.46 (95% CI: 0.29, 0.71; P<.001) with NSAIDs. There was no increased risk of nonfatal stroke comparing oral triptans with opioids/butalbital (HR 0.39; 95% CI 0.22, 0.69; P=.001) and with NSAIDs (HR 0.44; 95% CI 0.26, 0.76; P=.003). In a subgroup analysis of patients taking sumatriptan, HRs for major adverse cardiovascular events were 0.34 (95% CI: 0.21, 0.56; P<.001) when comparing oral sumatriptan with opioids/butalbital and 0.47 (95% CI: 0.28, 0.79; P=.004) with NSAIDs. CONCLUSIONS: The risk of adverse cardiovascular events observed with oral triptans is not greater than opioids/butalbital and NSAIDs in patients with migraine and preexisting cardiovascular conditions. ### Competing Interest Statement Jessica Ailani reports consulting (honoraria) fees from AbbVie, Amgen, Aeon, Axsome, Biohaven, BioDelivery Sciences International, Eli Lilly, GlaxoSmithKline, Lundbeck, Linpharma, Impel, Miravio, Pfizer, Neurolief, Neso, Satsuma, Theranica, and Teva; research grants paid to institution from AbbVie, Biohaven, Eli Lilly, Satsuma, Zosano; ownership in stock options from Ctrl M Health; and editorial board membership/steering committee participation with Medscape, NeurologyLive, Current Pain and Headache (editor, Unusual Headache Syndromes), and SELF magazine (medical editor). Azeem Banatwala is a current employee of Analysis Group, which received funding from GSK to conduct this study. Greg Belsky is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Maral DerSarkissian is a current employee of Analysis Group, which received funding from GSK to conduct this study. Janet Boyle-Kelly is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Jennifer Costello is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. David W. Dodick declares the following within the past 5 years: Consulting: Amgen, Atria, CapiThera Ltd., Cerecin, Ceruvia Lifesciences LLC, CoolTech, Ctrl M, Allergan, AbbVie, Biohaven, Escient, GlaxoSmithKline, Haleon, Lundbeck, Eli Lilly, Novartis, Impel, Satsuma, Theranica, WL Gore, Genentech, Nocira, Perfood, Praxis, AYYA Biosciences, Revance, Pfizer. Honoraria: American Academy of Neurology, Headache Cooperative of the Pacific, Headache Cooperative of New England, Canadian Headache Society, MF Med Ed Research, Biopharm Communications, CEA Group Holding Company (Clinical Education Alliance LLC), Teva (speaking), Amgen Japan (speaking), Eli Lilly Canada (speaking), Lundbeck (speaking), Pfizer (speaking), Vector Psychometric Group, Clinical Care Solutions, CME Outfitters, Curry Rockefeller Group, DeepBench, Global Access Meetings, KLJ Associates, Academy for Continued Healthcare Learning, Majallin LLC, Medlogix Communications, Medica Communications LLC, MJH Lifesciences, Miller Medical Communications, WebMD Health/Medscape, Wolters Kluwer, Oxford University Press, Cambridge University Press. Non-profit board membership: American Brain Foundation, American Migraine Foundation, ONE Neurology, Precon Health Foundation, Global Patient Advocacy Coalition, Atria Health Collaborative, Atria Academy of Science and Medicine, Arizona Brain Injury Alliance, Domestic Violence HOPE Foundation/Panfila, CSF Leak Foundation. Research support: Department of Defense, National Institutes of Health, Henry Jackson Foundation, Sperling Foundation, American Migraine Foundation, Henry Jackson Foundation, Patient Centered Outcomes Research Institute (PCORI). Stock options/shareholder/patents/board of directors: Ctrl M (options), Aural analytics (options), Axon Therapeutics (board/options), ExSano (options), Palion (options), Keimon Medical (Options), Man and Science, Healint (options), Theranica (options), Second Opinion/Mobile Health (options), Epien (options), Nocira (options), Matterhorn (shares), Ontologics (shares), King-Devick Technologies (options/board), Precon Health (options/board), ScotiaLyfe (Board), EigenLyfe (Options/Board), AYYA Biosciences (options), Axon Therapeutics (options/board), Cephalgia Group (options/board), Atria Health (options/employee). Patent 17189376.1-1466:vTitle: Onabotulinum Toxin Dosage Regimen for Chronic Migraine Prophylaxis (non-royalty bearing). Patent application submitted: Synaquell (Precon Health) Mei Sheng Duh is a current employee of Analysis Group, which received funding from GSK to conduct this study. Matt Fisher is a former employee and current shareholder of GSK, and a current employee of Haleon. Chi Gao is a current employee of Analysis Group, which received funding from GSK to conduct this study. Christopher Herrick is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Yichuan Grace Hsieh is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Marykate Murphy is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Shawn N. Murphy is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Travis Wang: is a current employee of Analysis Group, which received funding from GSK to conduct this study. Rory B. Weiner is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Amy K. Wong is a current employee of Mass General Brigham, a hospital and physician network that received funding from GSK for this research study. Louise H. Yu is a current employee of Analysis Group, which received funding from GSK to conduct this study. ### Funding Statement The study was funded by Haleon (formerly GSK Consumer Healthcare). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Mass General Brigham Institutional Review Board reviewed (protocol 2021P001844) and allowed an exemption for the study so no informed consent was required. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Anonymized individual participant data and study documents can be requested for further research from https://www.gsk-studyregister.com

This study assessed the real-world effectiveness of immunoglobulin replacement therapy (IgRT) for treatment of hypogammaglobulinemia and infections in patients with multiple myeloma (MM). A retrospective study was conducted on adult patients diagnosed with MM on or after 1 January 2010 using the Mass General Brigham Research Patient Data Registry. Infections were compared before and after IgRT initiation. Generalized estimating equation logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). In patients with accessible serum protein electrophoresis (SPEP) test results, a Natural Language Processing program supported the extraction of immunoglobulin G (IgG) data. The IgG assessments and incidence of hypogammaglobulinemia (defined as IgG level <500 mg/dL) were compared before and after IgRT initiation. The results were reported using descriptive statistics. A total of 6062 patients with MM were identified (56.2% male; median age, 65.0 years). Of the 6062 patients, 471 (7.8%) received ≥1 IgRT administrations. At 3 months, significantly lower odds of infections (OR, 0.71; 95% CI, 0.56-0.89; P = .0004) were observed after IgRT initiation than before IgRT. Among patients with accessible SPEP results (n = 3405), 3231 (94.9%) underwent ≥1 IgG test with a median of 18.0 (interquartile range, 7.0-40.0) IgG tests per patient. Hypogammaglobulinemia was experienced by 2075 of the 3231 patients (64.2%) who had ≥1 IgG test. Significantly fewer patients had hypogammaglobulinemia after IgRT initiation. In conclusion, IgRT use was associated with significant reductions in hypogammaglobulinemia and infections. Although IgRT is currently used for MM treatment, there is potential to optimize its dosing and treatment duration to reduce the morbidity and mortality associated with infections.

Patients with chronic lymphocytic leukemia (CLL) and non-Hodgkin lymphoma (NHL) can develop hypogammaglobulinemia, a form of secondary immune deficiency (SID), from the disease and treatments. Patients with hypogammaglobulinemia with recurrent infections may benefit from immunoglobulin replacement therapy (IgRT). This study evaluated patterns of IgG testing and the effectiveness of IgRT in real-world patients with CLL or NHL. A retrospective, longitudinal study was conducted among adult patients diagnosed with CLL or NHL. Clinical data from the Massachusetts General Brigham Research Patient Data Registry were used. IgG testing, infections, and antimicrobial use were compared before vs. 3, 6, and 12 months after IgRT initiation. Generalized estimating equation logistic regression models were used to estimate odds ratios (OR), 95% Confidence Intervals (CIs), and P-values. The study population included 17,192 patients (CLL: N=3,960; median age, 68 years; NHL: N=13,232; median age, 64 years). In the CLL and NHL cohorts, 67% and 51.2% had IgG testing and 6.5% and 4.7% received IgRT, respectively. Following IgRT initiation, the proportion of patients with hypogammaglobulinemia, the odds of infections or severe infections, and associated antimicrobial use, decreased significantly. Increased frequency of IgG testing was associated with a significantly lower likelihood of severe infection. In conclusion, in real-world patients with CLL or NHL, IgRT was associated with significant reductions in hypogammaglobulinemia, infections, severe infections, and associated antimicrobials. Optimizing IgG testing and IgRT are warranted for the comprehensive management of SID in patients with CLL or NHL.

ABSTRACT Purpose This study assessed the performance of International Classification of Diseases 10th Revision, Clinical Modification (ICD-10-CM) coronavirus disease 2019 (COVID-19) diagnostic code U07.1 against polymerase chain reaction (PCR) test results (Objective 1), and electronic medical record (EMR)-based codified algorithm for severe COVID-19 illness based on endpoints used in the Pfizer-BioNTech COVID-19 vaccine trial against chart review (Objective 2). Methods This retrospective, longitudinal cohort study used EMR data from the Mass General Brigham COVID-19 Data Mart (3/1/2020–11/19/2020) for adult patients with ≥1 PCR test, antigen test, or code U07.1 (Objective 1) and adult patients with a positive PCR test hospitalized with COVID-19 (Objective 2). Results Among 354,124 patients in Objective 1, 96% had ≥1 PCR test (including 6% with ≥1 positive PCR test; 11% with ≥1 code U07.1). Code U07.1 had low sensitivity (54%) and positive predictive value (PPV; 63%) but high specificity (97%) against the PCR test. Among 300 patients hospitalized for COVID-19 randomly sampled for chart review in Objective 2, the EMR-based case definition for severe COVID-19 illness had high PPV (>95%), showing better performance than severe/critical COVID-19 endpoints defined by the World Health Organization (PPV: 79%). Conclusions COVID-19 diagnosis based on ICD-10-CM code U07.1 had inadequate sensitivity and requires confirmation by PCR testing. The EMR-based case definition showed high PPV and can be used to identify cases of severe COVID-19 illness in real-world datasets. These findings highlight the importance of validating outcomes in real-world data, and can guide researchers analyzing COVID-19 data when PCR tests are not readily available. KEY POINTS This study evaluated the performance of International Classification of Diseases 10th Revision, Clinical Modification (ICD-10-CM) codes and an electronic medical record (EMR)-based algorithm for identifying coronavirus disease 2019 (COVID-19) diagnosis and severe COVID-19 illness in real-world data. ICD-10-CM code U07.1 for COVID-19 had low sensitivity and positive predictive value (PPV) against PCR tests. The EMR-based algorithm for severe COVID-19 illness developed from the Pfizer– BioNTech COVID-19 vaccine trial had high PPV against chart review, and may be used to identify severe cases in real-world data. These results highlight the importance of validating outcomes when conducting analyses of real-world datasets. PLAIN LANGUAGE SUMMARY As polymerase chain reaction (PCR) tests for coronavirus disease 2019 (COVID-19) diagnosis are becoming less frequently used and there is no standard definition of severe COVID-19 illness, it is important to have a way of correctly identifying COVID-19 diagnosis or severe COVID-19 illness in real-world data (e.g., electronic medical records [EMRs]). This study examined: 1) how a diagnosis code for COVID-19 used in EMRs (i.e., U07.1) compares to PCR test results in terms of accurately identifying patients with COVID-19; and 2) whether a definition for severe COVID-19 illness developed based on the Pfizer–BioNTech COVID-19 vaccine trial and a definition used by the World Health Organization [WHO]) can be used to accurately identify patients with severe COVID-19 illness in EMRs. The results showed that code U07.1 was not very accurate in identifying patients with COVID-19. On the other hand, the developed definition for severe COVID-19 illness was more accurate than the WHO definition and was able to identify most patients with severe COVID-19 illness in real-world data.

AbstractObjectiveIntegrating and harmonizing disparate patient data sources into one consolidated data portal enables researchers to conduct analysis efficiently and effectively.Materials and MethodsWe describe an implementation of Informatics for Integrating Biology and the Bedside (i2b2) to create the Mass General Brigham (MGB) Biobank Portal data repository. The repository integrates data from primary and curated data sources and is updated weekly. The data are made readily available to investigators in a data portal where they can easily construct and export customized datasets for analysis.ResultsAs of July 2021, there are 125 645 consented patients enrolled in the MGB Biobank. 88 527 (70.5%) have a biospecimen, 55 121 (43.9%) have completed the health information survey, 43 552 (34.7%) have genomic data and 124 760 (99.3%) have EHR data. Twenty machine learning computed phenotypes are calculated on a weekly basis. There are currently 1220 active investigators who have run 58 793 patient queries and exported 10 257 analysis files.DiscussionThe Biobank Portal allows noninformatics researchers to conduct study feasibility by querying across many data sources and then extract data that are most useful to them for clinical studies. While institutions require substantial informatics resources to establish and maintain integrated data repositories, they yield significant research value to a wide range of investigators.ConclusionThe Biobank Portal and other patient data portals that integrate complex and simple datasets enable diverse research use cases. i2b2 tools to implement these registries and make the data interoperable are open source and freely available.

The wide gap between a care provider's conceptualization of electronic health record (EHR) and the structures for electronic health record (EHR) data storage and transmission, presents a multitude of obstacles for development of innovative Health IT applications. While developers model the EHR view of the clinicians at one end, they work with a different data view to construct health IT applications. Although there has been considerable progress to bridge this gap by evolution of developer friendly standards and tools for terminology mapping and data warehousing, there is a need for a simplified framework to facilitate development of interoperable applications. To this end, we propose a framework for creating a layer of semantic abstraction on the EHR and describe preliminary work on the implementation of this framework for management of hyperlipidemia and hypertension. Our goal is to facilitate the rapid development and portability of Health IT applications.

BACKGROUND:The conventional approach for clinical studies is to identify a cohort of potentially eligible patients and then screen for enrollment. In an effort to reduce the cost and manual effort involved in the screening process, several studies have leveraged electronic health records (EHR) to refine cohorts to better match the eligibility criteria, which is referred to as phenotyping. We extend this approach to dynamically identify a cohort by repeating phenotyping in alternation with manual screening.METHODS:Our approach consists of multiple screen cycles. At the start of each cycle, the phenotyping algorithm is used to identify eligible patients from the EHR, creating an ordered list such that patients that are most likely eligible are listed first. This list is then manually screened, and the results are analyzed to improve the phenotyping for the next cycle. We describe the preliminary results and challenges in the implementation of this approach for an intervention study on heart failure.RESULTS:A total of 1,022 patients were screened, with 223 (23%) of patients being found eligible for enrollment into the intervention study. The iterative approach improved the phenotyping in each screening cycle. Without an iterative approach, the positive screening rate (PSR) was expected to dip below the 20% measured in the first cycle; however, the cyclical approach increased the PSR to 23%.CONCLUSIONS:Our study demonstrates that dynamic phenotyping can facilitate recruitment for prospective clinical study. Future directions include improved informatics infrastructure and governance policies to enable real-time updates to research repositories, tooling for EHR annotation, and methodologies to reduce human annotation.

Objective: Healthcare organizations use research data models supported by projects and tools that interest them, which often means organizations must support the same data in multiple models. The healthcare research ecosystem would benefit if tools and projects could be adopted independently from the underlying data model. Here, we introduce the concept of a reusable application programming interface (API) for healthcare and show that the i2b2 API can be adapted to support diverse patient-centric data models. Materials and Methods: We develop methodology for extending i2b2's pre-existing API to query additional data models, using i2b2's recent "multi-fact-table querying" feature. Our method involves developing data-model-specific i2b2 ontologies and mapping these to query non-standard table structure. Results: We implement this methodology to query OMOP and PCORnet models, which we validate with the i2b2 query tool. We implement the entire PCORnet data model and a five-domain subset of the OMOP model. We also demonstrate that additional, ancillary data model columns can be modeled and queried as i2b2 "modifiers." Discussion: i2b2' s REST API can be used to query multiple healthcare data models, enabling shared tooling to have a choice of backend data stores. This enables separation between data model and software tooling for some of the more popular open analytic data models in healthcare. Conclusion: This methodology immediately allows querying OMOP and PCORnet using the i2b2 API. It is released as an open-source set of Docker images, and also on the i2b2 community wiki.

Left ventricular ejection fraction (LVEF) is an important prognostic indicator of cardiovascular outcomes. It is used clinically to determine the indication for several therapeutic interventions. LVEF is most commonly derived using in-line tools and some manual assessment by cardiologists from standardized echocardiographic views. LVEF is typically documented in free-text reports, and variation in LVEF documentation pose a challenge for the extraction and utilization of LVEF in computer-based clinical workflows. To address this problem, we developed a computerized algorithm to extract LVEF from echocardiography reports for the identification of patients having heart failure with reduced ejection fraction (HFrEF) for therapeutic intervention at a large healthcare system. We processed echocardiogram reports for 57,158 patients with coded diagnosis of Heart Failure that visited the healthcare system over a two-year period. Our algorithm identified a total of 3910 patients with reduced ejection fraction. Of the 46,634 echocardiography reports processed, 97% included a mention of LVEF. Of these reports, 85% contained numerical ejection fraction values, 9% contained ranges, and the remaining 6% contained qualitative descriptions. Overall, 18% of extracted numerical LVEFs were?≤?40%. Furthermore, manual validation for a sample of 339 reports yielded an accuracy of 1.0. Our study demonstrates that a regular expression-based approach can accurately extract LVEF from echocardiograms, and is useful for delineating heart-failure patients with reduced ejection fraction.

Though related to clinical electronic medical records, health research information technology poses unique data management challenges. The repurposing of clinical care data for research must be performed with full awareness of its limitations. The clinical care data are often vast, and, unexpectedly, data transformations allowing application of high-performance indexing systems must often be implemented to enable cross-patient queries. The clinical care data are highly diverse, and a health-care atomic “fact” is often defined in the data to unify it for cross-patient queries. The codes used in the clinical data to describe patient-related concepts are often at different levels of granularity, and hierarchical schemes must be implemented to allow consistent queries to be performed. Once these problems are solved, the research data warehouse allows an elegant solution to the vexing problem of study participant recruitment. The creation of specialized data registries from the data can allow many complex types of epidemiological and bioinformatics problems to be addressed. Rounding out the application of large databases to clinical research are trial management systems, publicly available biomedical literature databases, and emerging approaches to data integration using new cloud and big data technologies.