Dataset Information

Identifying heart failure using EMR-based algorithms.

ABSTRACT: BACKGROUND:Heart failure (HF) is a major clinical and public health problem, the management of which will benefit from large-scale pragmatic research that leverages electronic medical records (EMR). Requisite to using EMRs for HF research is the development of reliable algorithms to identify HF patients. We aimed to develop and validate computable phenotype algorithms to identify patients with HF using standardized data elements defined by the Patient Centered Outcomes Research Network (PCORnet) Common Data Model (CDM). METHODS:We built HF computable phenotypes utilizing the data domains of HF diagnosis codes, prescribed HF-related medications and N-terminal B-type natriuretic peptide (NT-proBNP). Algorithms were validated in a cohort (n?=?76,254) drawn from Olmsted County, MN between 2010-2012 a sample of whose records were manually reviewed to confirm HF according to Framingham criteria. RESULTS:The different algorithms we tested provided different tradeoffs between sensitivity and positive predictive value (PPV). The highest sensitivity (78.7%) algorithm utilized one HF diagnosis code and had the lowest PPV (68.5%). The addition of more algorithm components, such as additional HF diagnosis codes, HF medications or elevated NT-proBNP, improved the PPV while reducing sensitivity. When added to a diagnostic code, the addition of NT-proBNP (>450?pg/mL) had a similar impact compared to additional HF medication criteria, increasing PPV by ?3-4% and decreasing sensitivity by ?7-10%. CONCLUSIONS:Algorithms derived from PCORnet CDM elements can be used to identify patients with HF without manual adjudication with reasonable sensitivity and PPV. Algorithm choice should be driven by the goal of the research.

SUBMITTER: Tison GH

PROVIDER: S-EPMC6233734 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Identifying heart failure using EMR-based algorithms.

Tison Geoffrey H GH Chamberlain Alanna M AM Pletcher Mark J MJ Dunlay Shannon M SM Weston Susan A SA Killian Jill M JM Olgin Jeffrey E JE Roger Véronique L VL

International journal of medical informatics 20180919

<h4>Background</h4>Heart failure (HF) is a major clinical and public health problem, the management of which will benefit from large-scale pragmatic research that leverages electronic medical records (EMR). Requisite to using EMRs for HF research is the development of reliable algorithms to identify HF patients. We aimed to develop and validate computable phenotype algorithms to identify patients with HF using standardized data elements defined by the Patient Centered Outcomes Research Network ( ...[more]

PMID: 30409334

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Project description:BackgroundThe genetic factors and pathogenesis of idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) have not been understood thoroughly; there is a lack of specific diagnostic markers and treatment methods for the disease. Hence, we aimed to identify the mechanisms of action at the molecular level and potential molecular markers for this disease.MethodsGene expression profiles of IDCM-HF and non-heart failure (NF) specimens were acquired from the database of Gene Expression Omnibus (GEO). We then identified the differentially expressed genes (DEGs) and analyzed their functions and related pathways by using "Metascape". Weighted gene co-expression network analysis (WGCNA) was utilized to search for key module genes. Candidate genes were identified by intersecting the key module genes identified via WGCNA with DEGs and further screened via the support vector machine-recursive feature elimination (SVM-RFE) method and the least absolute shrinkage and selection operator (LASSO) algorithm. At last, the biomarkers were validated and evaluated the diagnostic efficacy by the area under curve (AUC) value and further confirmed the differential expression in the IDCM-HF and NF groups using an external database.ResultsWe detected 490 genes exhibiting differential expression between IDCM-HF and NF specimens from the GSE57338 dataset, with most of them being concentrated in the extracellular matrix (ECM) of cells related to biological processes and pathways. After screening, 13 candidate genes were identified. Aquaporin 3 (AQP3) and cytochrome P450 2J2 (CYP2J2) showed high diagnostic efficacy in the GSE57338 and GSE6406 datasets, respectively. In comparison to the NF group, AQP3 was significantly down-regulated in the IDCM-HF group, while CYP2J2 was significantly up-regulated.ConclusionAs far as we know, this is the first study that combines WGCNA and machine learning algorithms to screen for potential biomarkers of IDCM-HF. Our findings suggest that AQP3 and CYP2J2 could be used as novel diagnostic markers and treatment targets of IDCM-HF.

Project description:Databases of medical claims can be valuable resources for cardiovascular research, such as comparative effectiveness and pharmacovigilance studies of cardiovascular medications. However, claims data do not include all of the factors used for risk stratification in clinical care. We sought to develop claims-based algorithms to identify individuals at high estimated risk for coronary heart disease (CHD) events, and to identify uncontrolled low-density lipoprotein (LDL) cholesterol among statin users at high risk for CHD events.We conducted a cross-sectional analysis of 6,615 participants ?66 years old using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study baseline visit in 2003-2007 linked to Medicare claims data. Using REGARDS data we defined high risk for CHD events as having a history of CHD, at least 1 risk equivalent, or Framingham CHD risk score >20%. Among statin users at high risk for CHD events we defined uncontrolled LDL cholesterol as LDL cholesterol ?100 mg/dL. Using Medicare claims-based variables for diagnoses, procedures, and healthcare utilization, we developed algorithms for high CHD event risk and uncontrolled LDL cholesterol.REGARDS data indicated that 49% of participants were at high risk for CHD events. A claims-based algorithm identified high risk for CHD events with a positive predictive value of 87% (95% CI: 85%, 88%), sensitivity of 69% (95% CI: 67%, 70%), and specificity of 90% (95% CI: 89%, 91%). Among statin users at high risk for CHD events, 30% had LDL cholesterol ?100 mg/dL. A claims-based algorithm identified LDL cholesterol ?100 mg/dL with a positive predictive value of 43% (95% CI: 38%, 49%), sensitivity of 19% (95% CI: 15%, 22%), and specificity of 89% (95% CI: 86%, 90%).Although the sensitivity was low, the high positive predictive value of our algorithm for high risk for CHD events supports the use of claims to identify Medicare beneficiaries at high risk for CHD events.

Project description:ObjectivesThis study sought to describe characteristics and risk of adverse outcomes associated with the H2FPEF and HFA-PEFF scores among participants in the community with unexplained dyspnea.BackgroundDiagnosing heart failure with preserved ejection fraction (HFpEF) can be challenging. The H2FPEF and HFA-PEFF scores have recently been developed to estimate the likelihood that HFpEF is present among patients with unexplained dyspnea.MethodsThe study included 4,892 ARIC (Atherosclerosis Risk In Communities) study participants 67 to 90 years of age at visit 5 (2011 to 2013) without other common cardiopulmonary causes of dyspnea. Participants were categorized as asymptomatic (76.6%), having known HFpEF (10.3%), and having tertiles of each score among those with ≥moderate, self-reported dyspnea (13.1%). The primary outcome was heart failure (HF) hospitalization or death.ResultsMean age was 75 ± 5 years, 58% were women, and 22% were black. After a mean follow-up of 5.3 ± 1.2 years, rates of HF hospitalization or death per 1,000 person-years for asymptomatic and known HFpEF were 20.7 (95% confidence interval [CI]: 18.9 to 22.7) and 71.6 (95% CI: 61.6 to 83.3), respectively. Among 641 participants with unexplained dyspnea, rates were 27.7 (95% CI: 18.2 to 42.1), 44.9 (95% CI: 34.9 to 57.7), and 47.3 (95% CI: 36.5 to 61.3) (tertiles of H2FPEF score) and 31.8 (95% CI: 20.3 to 49.9), 32.4 (95% CI: 23.4 to 44.9), and 54.3 (95% CI: 43.8 to 67.3) (tertiles of HFA-PEFF score). Participants with unexplained dyspnea and scores above the diagnostic threshold suggested for each algorithm, H2FPEF score ≥6 and HFA-PEFF score ≥5, had equivalent risk of HF hospitalization or death compared with known HFpEF. Among those with unexplained dyspnea, 28% had "discordant" findings (only high risk by 1 algorithm), while 4% were high risk by both.ConclusionsParticipants with unexplained dyspnea and higher H2FPEF or HFA-PEFF scores face substantial risks of HF hospitalization or death. A significant fraction of patients are classified discordantly by using both algorithms.

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Identifying heart failure using EMR-based algorithms.

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Identifying heart failure using EMR-based algorithms.

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