Abstract 20: Cardiovascular Health Trends in Electronic Health Record Data (2010-2015): the Guideline Advantage

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Randi Foraker ◽  
Sejal Patel ◽  
Yosef Khan ◽  
Mary Ann Bauman ◽  
Julie Bower
Keyword(s):  
Large Scale ◽  
Cardiovascular Health ◽  
Treatment Guidelines ◽  
Smoking Status ◽  
Density Lipoprotein ◽  
Heart Association ◽  
Electronic Health Record Data ◽  
Cancer Society ◽  
Health Trends ◽  
Electronic Health

Background: Electronic health records (EHRs) are an increasingly valuable data source for monitoring population health. However, EHR data are rarely shared across health system borders, limiting their utility to researchers and policymakers. The Guideline Advantage™ (TGA) program, a joint initiative by the American Heart Association (AHA), American Cancer Society, and American Diabetes Association, brings together data from EHRs across the country to support disease prevention and management efforts in the outpatient setting. Methods: We analyzed TGA EHR data from >70 clinics comprising 281,837 adult patients from 2010 to 2015. We used the first available measure per patient for each calendar year to characterize trends in the proportion of patients in “ideal”, “intermediate”, and “poor” CVH categories for blood pressure (BP), body mass index (BMI) and smoking. Total cholesterol and fasting glucose values were not reported to TGA. Thus, we used low-density lipoprotein (LDL) and hemoglobin A1c (A1c) treatment guidelines to classify patients into CVH categories for the respective metrics. Results: Patients were an average of 50 years old, and 57.4% were female. Of records with complete data on race, 70.9% of patients were white. Over 6 years of observation, we documented increases in the proportion of patients at ideal levels for BP, smoking, LDL, and A1c, but decreases in the proportion of patients at an ideal level for BMI (Figure). Conclusions: TGA data provide a large-scale perspective of outpatient CVH, yet we acknowledge limitations associated with using EHR data to assess trends in CVH. Specifically, EHR data entry is clinically-driven - BP and BMI values are likely to be updated at each visit for each patient, while smoking status, LDL, and A1c are not. Our analysis lays the groundwork for EHR analyses as these data become less siloed and more accessible to stakeholders. Figure. Trends in CVH from 2010 to 2015: The Guideline Advantage™

scholarly journals Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aixia Guo ◽  
Rahmatollah Beheshti ◽  
Yosef M. Khan ◽  
James R. Langabeer ◽  
Randi E. Foraker
Keyword(s):  
United States ◽  
Cardiovascular Health ◽  
Short Term Memory ◽  
Health Management ◽  
Smoking Status ◽  
Low Density Lipoprotein ◽  
Model Performance ◽  
Density Lipoprotein ◽  
The United States ◽  
Electronic Health

Abstract Background Cardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help providers better manage patients’ CVH. We hypothesized that CVH measures can be predicted based on previous measurements from longitudinal electronic health record (EHR) data. Methods The Guideline Advantage (TGA) dataset was used and contained EHR data from 70 outpatient clinics across the United States (US). We studied predictions of 5 CVH submetrics: smoking status (SMK), body mass index (BMI), blood pressure (BP), hemoglobin A1c (A1C), and low-density lipoprotein (LDL). We applied embedding techniques and long short-term memory (LSTM) networks – to predict future CVH category levels from all the previous CVH measurements of 216,445 unique patients for each CVH submetric. Results The LSTM model performance was evaluated by the area under the receiver operator curve (AUROC): the micro-average AUROC was 0.99 for SMK prediction; 0.97 for BMI; 0.84 for BP; 0.91 for A1C; and 0.93 for LDL prediction. Model performance was not improved by using all 5 submetric measures compared with using single submetric measures. Conclusions We suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients’ future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.

scholarly journals Structured Approach for Evaluating Strategies for Cancer Ascertainment Using Large-Scale Electronic Health Record Data

2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ashley Earles ◽  
Lin Liu ◽  
Ranier Bustamante ◽  
Pat Coke ◽  
Julie Lynch ◽  
...  
Keyword(s):  
Large Scale ◽  
High Sensitivity ◽  
Performance Comparison ◽  
Administrative Claims ◽  
Electronic Health Record Data ◽  
Perfect Agreement ◽  
Electronic Health ◽  
Record Review ◽  
Structured Approach ◽  
Manual Record

Purpose Cancer ascertainment using large-scale electronic health records is a challenge. Our aim was to propose and apply a structured approach for evaluating multiple candidate approaches for cancer ascertainment using colorectal cancer (CRC) ascertainment within the US Department of Veterans Affairs (VA) as a use case. Methods The proposed approach for evaluating cancer ascertainment strategies includes assessment of individual strategy performance, comparison of agreement across strategies, and review of discordant diagnoses. We applied this approach to compare three strategies for CRC ascertainment within the VA: administrative claims data consisting of International Classification of Diseases, Ninth Revision (ICD9) diagnosis codes; the VA Central Cancer Registry (VACCR); and the newly accessible Oncology Domain, consisting of cases abstracted by local cancer registrars. The study sample consisted of 1,839,043 veterans with index colonoscopy performed from 1999 to 2014. Strategy-specific performance was estimated based on manual record review of 100 candidate CRC cases and 100 colonoscopy controls. Strategies were further compared using Cohen’s κ and focused review of discordant CRC diagnoses. Results A total of 92,197 individuals met at least one CRC definition. All three strategies had high sensitivity and specificity for incident CRC. However, the ICD9-based strategy demonstrated poor positive predictive value (58%). VACCR and Oncology Domain had almost perfect agreement with each other (κ, 0.87) but only moderate agreement with ICD9-based diagnoses (κ, 0.51 and 0.57, respectively). Among discordant cases reviewed, 15% of ICD9-positive but VACCR- or Oncology Domain–negative cases had incident CRC. Conclusion Evaluating novel strategies for identifying cancer requires a structured approach, including validation against manual record review, agreement among candidate strategies, and focused review of discordant findings. Without careful assessment of ascertainment methods, analyses may be subject to bias and limited in clinical impact.

scholarly journals Stratifying risk for dementia onset using large‐scale electronic health record data: A retrospective cohort study

2020 ◽  
Vol 16 (3) ◽  
pp. 531-540 ◽  
Author(s):  
Thomas H. McCoy ◽  
Larry Han ◽  
Amelia M. Pellegrini ◽  
Rudolph E. Tanzi ◽  
Sabina Berretta ◽  
...  
Keyword(s):  
Cohort Study ◽  
Electronic Health Record ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Large Scale ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Dementia Onset

scholarly journals An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

2021 ◽  
Author(s):  
Sergiusz Wesolowski ◽  
Gordon Howard Lemmon ◽  
Edgar J Hernandez ◽  
Alex Ryan Henrie ◽  
Thomas A Miller ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Large Scale ◽  
Cardiovascular Health ◽  
Cardiovascular Outcomes ◽  
Health Records ◽  
University Of Utah ◽  
Explainable Artificial Intelligence ◽  
Electronic Health ◽  
Discovery Method

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.

scholarly journals Urinary Tartaric Acid, a Biomarker of Wine Intake, Correlates with Lower Total and LDL Cholesterol

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2883
Author(s):  
Inés Domínguez-López ◽  
Isabella Parilli-Moser ◽  
Camila Arancibia-Riveros ◽  
Anna Tresserra-Rimbau ◽  
Miguel Angel Martínez-González ◽  
...  
Keyword(s):  
Lipid Profile ◽  
Postmenopausal Women ◽  
Tartaric Acid ◽  
Cardiovascular Health ◽  
Ldl Cholesterol ◽  
Smoking Status ◽  
Strong Association ◽  
Density Lipoprotein ◽  
P Value ◽  
Wine Consumption

Postmenopausal women are at higher risk of developing cardiovascular diseases due to changes in lipid profile and body fat, among others. The aim of this study was to evaluate the association of urinary tartaric acid, a biomarker of wine consumption, with anthropometric (weight, waist circumference, body mass index (BMI), and waist-to-height ratio), blood pressure, and biochemical variables (blood glucose and lipid profile) that may be affected during the menopausal transition. This sub-study of the PREDIMED (Prevención con Dieta Mediterránea) trial included a sample of 230 women aged 60–80 years with high cardiovascular risk at baseline. Urine samples were diluted and filtered, and tartaric acid was analyzed by liquid chromatography coupled to electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). Correlations between tartaric acid and the study variables were adjusted for age, education level, smoking status, physical activity, BMI, cholesterol-lowering, antihypertensive, and insulin treatment, total energy intake, and consumption of fruits, vegetables, and raisins. A strong association was observed between wine consumption and urinary tartaric acid (0.01 μg/mg (95% confidence interval (CI): 0.01, 0.01), p-value < 0.001). Total and low-density lipoprotein (LDL) cholesterol were inversely correlated with urinary tartaric acid (−3.13 μg/mg (−5.54, −0.71), p-value = 0.016 and −3.03 μg/mg (−5.62, −0.42), p-value = 0.027, respectively), whereas other biochemical and anthropometric variables were unrelated. The results suggest that wine consumption may have a positive effect on cardiovascular health in postmenopausal women, underpinning its nutraceutical properties.

scholarly journals Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 570-579 ◽  
Author(s):  
Na Hong ◽  
Andrew Wen ◽  
Feichen Shen ◽  
Sunghwan Sohn ◽  
Chen Wang ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Data ◽  
Large Scale ◽  
Structured Data ◽  
Health Record ◽  
Data Normalization ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Clinical Resource

Abstract Objective To design, develop, and evaluate a scalable clinical data normalization pipeline for standardizing unstructured electronic health record (EHR) data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification. Methods We established an FHIR-based clinical data normalization pipeline known as NLP2FHIR that mainly comprises: (1) a module for a core natural language processing (NLP) engine with an FHIR-based type system; (2) a module for integrating structured data; and (3) a module for content normalization. We evaluated the FHIR modeling capability focusing on core clinical resources such as Condition, Procedure, MedicationStatement (including Medication), and FamilyMemberHistory using Mayo Clinic’s unstructured EHR data. We constructed a gold standard reusing annotation corpora from previous NLP projects. Results A total of 30 mapping rules, 62 normalization rules, and 11 NLP-specific FHIR extensions were created and implemented in the NLP2FHIR pipeline. The elements that need to integrate structured data from each clinical resource were identified. The performance of unstructured data modeling achieved F scores ranging from 0.69 to 0.99 for various FHIR element representations (0.69–0.99 for Condition; 0.75–0.84 for Procedure; 0.71–0.99 for MedicationStatement; and 0.75–0.95 for FamilyMemberHistory). Conclusion We demonstrated that the NLP2FHIR pipeline is feasible for modeling unstructured EHR data and integrating structured elements into the model. The outcomes of this work provide standards-based tools of clinical data normalization that is indispensable for enabling portable EHR-driven phenotyping and large-scale data analytics, as well as useful insights for future developments of the FHIR specifications with regard to handling unstructured clinical data.

scholarly journals Dietary Cholesterol and Cardiovascular Risk: A Science Advisory From the American Heart Association

Circulation ◽  
2020 ◽  
Vol 141 (3) ◽  
Author(s):  
Jo Ann S. Carson ◽  
Alice H. Lichtenstein ◽  
Cheryl A.M. Anderson ◽  
Lawrence J. Appel ◽  
Penny M. Kris-Etherton ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Dietary Patterns ◽  
Cardiovascular Health ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Low Density Lipoprotein ◽  
Dietary Cholesterol ◽  
Density Lipoprotein ◽  
Heart Association ◽  
Meta Analyses

The elimination of specific dietary cholesterol target recommendations in recent guidelines has raised questions about its role with respect to cardiovascular disease. This advisory was developed after a review of human studies on the relationship of dietary cholesterol with blood lipids, lipoproteins, and cardiovascular disease risk to address questions about the relevance of dietary cholesterol guidance for heart health. Evidence from observational studies conducted in several countries generally does not indicate a significant association with cardiovascular disease risk. Although meta-analyses of intervention studies differ in their findings, most associate intakes of cholesterol that exceed current average levels with elevated total or low-density lipoprotein cholesterol concentrations. Dietary guidance should focus on healthy dietary patterns (eg, Mediterranean-style and DASH [Dietary Approaches to Stop Hypertension]–style diets) that are inherently relatively low in cholesterol with typical levels similar to the current US intake. These patterns emphasize fruits, vegetables, whole grains, low-fat or fat-free dairy products, lean protein sources, nuts, seeds, and liquid vegetable oils. A recommendation that gives a specific dietary cholesterol target within the context of food-based advice is challenging for clinicians and consumers to implement; hence, guidance focused on dietary patterns is more likely to improve diet quality and to promote cardiovascular health.

scholarly journals Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes

ACI Open ◽  
2019 ◽  
Vol 03 (01) ◽  
pp. e44-e62
Author(s):  
Fabrizio Pecoraro ◽  
Daniela Luzi ◽  
Fabrizio L. Ricci
Keyword(s):  
Conceptual Framework ◽  
Data Warehouse ◽  
Large Scale ◽  
Heterogeneous Systems ◽  
Target Population ◽  
Electronic Health Record Data ◽  
Health Level 7 ◽  
Electronic Health ◽  
Unique Source ◽  
Definition Of

Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse. Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach. Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse.

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