Applying Diagnosis Support Systems in Electronic Health Records to Identify wild-type Transthyretin Amyloid Cardiomyopathy Risk

Willis, Connor; Watanabe, Alexandre H.; Hughes, Justin M.; Nolen, Kim; O'Meara, Jason; Schepart, Alexander; Bruno, Marianna; Biskupiak, Joseph; Kawamoto, Kensaku; Shara, Nawar; Kannampallil, Thomas

doi:10.2217/fca-2021-0122

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Federman et al [53], 2017; Goehler et al [54], 2019; Horton et al [55], 2018; Howell et al [56], 2014; Klang et al [57], 2021; Li et al [58], 2020; Lindholm et al [59], 2010; Melnick et al [60], 2022; Milne Adult et al [61], 2020; Mitchell et al [62], 2022; Mulhem et al [63], 2020; Park et al [64], 2021; Ritchey et al [65], 2016; Rose et al [66], 2018; Ryan et al [67], 2021; Shelley et al [68], 2017; Sroujieh et al [69], 2016; Toscos et al [70], 2020; Toscos et al [71], 2020; Willis et al [72], 2022 Del Fiol et al [73], 2020; DeLozier [74], 2021; Jones et al [75], 2022; Jose et al [76], 2020; Keizur et al [77], 2022; Kolb et al [78], 2016; Lilih et al [79], 2017; Nikolic et al [80], 2017; Ray et al [81], 2018; Adult and pediatric Shelden et al [82], 2021; Siff and Emerman [83], 2016; Sonstein et al [84], 2014; Straub et al [85], 2013; Stutz et al [86], 2018; Tham et al [87], 2016; Wendel et al [88], 2023; Zorn et al [89], 2022 Tsai et al [90], 2015 Not specified Goldberg et al [91], 2016; Hojat et al [92], 2020; Simon et al [93], 2023; Tham et al…”

Section: Clinical Decision Supportmentioning

confidence: 99%

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

Chishtie,

Sapiro,

Wiebe

et al. 2023

J Med Internet Res

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Background Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research. Objective The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences. Methods We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results. Results We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized. Conclusions The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

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Section: Clinical Decision Supportmentioning

confidence: 99%

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

Chishtie,

Sapiro,

Wiebe

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

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Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

Faviez,

Chen,

Garcelon

et al. 2024

BMC Med Inform Decis Mak

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Background There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients’ care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies. Methods Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology. Results A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as “expert-level”. Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases. Conclusion Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

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Comparative Structure of Male Mortality From Cardiac Causes in Five-Year Age Groups

Драпкина

Samorodskaya

2023

Kardiologiia

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Aim To study the nosological structure of male mortality in 5-year age groups (15–85+) and the contribution of cardiac causes to all-cause mortality in 2020; to discuss the correctness of statistical recording of causes of cardiac death.Material and methods Data source: Center for Demographic Research of the Russian School of Economy http://demogr.nes.ru / index.php / ru / demogr_indicat / agreement. The selected indexes were all-cause death, causes of the class of circulatory diseases (CD) according to the International Classification of Diseases, Tenth Revision (ICD-10) (class IX, codes I00–I99), and cardiac causes of death (codes I00–I40, I70, I67.4, Q20–28) in 5-year age groups.Results Proportions of CD and cardiac causes in the male all-cause mortality were almost identical in the age groups younger than 30 years. Then the proportion of cardiac deaths remained almost unchanged (30–34 %) in contrast to the rapid growth of the CD proportion (to 51 % with a maximum at 75–79 years). Until the age of 45 years, more than 50% of cardiac deaths were caused by heart defects and cardiomyopathies and more than 25% by acute forms of ischemic heart disease (IHD); in older groups, their proportions decreased but the mortality increased. In the age groups younger than 50 years, the mortality from “Other forms of acute IHD” (ICD codes I20, I24.1–9 counted as one line) was higher than the mortality from myocardial infarction (MI); after 50 years, the MI mortality became higher. The combined proportion of two groups in the mortality from cardiac causes was maximal at the age of 20–24 years (31 %), then it decreased to a minimum of 9 % at the age of 85+. The mortality from and the proportions of chronic forms of IHD (more than 50% of which have no clear criteria for diagnosis and death), arterial hypertension, “Myocardial degeneration” (ICD code I51.5), and “Pulmonary heart and pulmonary circulation disorders” (ICD codes I26–I28) rapidly grow with increasing age. Existing approaches to recording the causes of death do not allow assessment of the contribution and mortality rates from a number of cardiac diseases.Conclusion Mortality reduction programs should provide more accurate recording of the causes of death and take into account age-related features of the nosological structure of cardiac mortality.

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Applying Diagnosis Support Systems in Electronic Health Records to Identify wild-type Transthyretin Amyloid Cardiomyopathy Risk

Cited by 3 publications

References 21 publications

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

Comparative Structure of Male Mortality From Cardiac Causes in Five-Year Age Groups

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