Literature DB >> 25966015

Use of electronic health records to ascertain, validate and phenotype acute myocardial infarction: A systematic review and recommendations.

Bruna Rubbo1, Natalie K Fitzpatrick2, Spiros Denaxas2, Marina Daskalopoulou3, Ning Yu2, Riyaz S Patel4, Harry Hemingway2.   

Abstract

Electronic health records (EHRs) offer the opportunity to ascertain clinical outcomes at large scale and low cost, thus facilitating cohort studies, quality of care research and clinical trials. For acute myocardial infarction (AMI) the extent to which different EHR sources are accessible and accurate remains uncertain. Using MEDLINE and EMBASE we identified thirty three studies, reporting a total of 128658 patients, published between January 2000 and July 2014 that permitted assessment of the validity of AMI diagnosis drawn from EHR sources against a reference such as manual chart review. In contrast to clinical practice, only one study used EHR-derived markers of myocardial necrosis to identify possible AMI cases, none used electrocardiogram findings and one used symptoms in the form of free text combined with coded diagnosis. The remaining studies relied mostly on coded diagnosis. Thirty one studies reported positive predictive value (PPV)≥ 70% between AMI diagnosis from both secondary care and primary care EHRs and the reference. Among fifteen studies reporting EHR-derived AMI phenotypes, three cross-referenced ST-segment elevation AMI diagnosis (PPV range 71-100%), two non-ST-segment elevation AMI (PPV 91.0, 92.1%), three non-fatal AMI (PPV range 82-92.2%) and six fatal AMI (PPV range 64-91.7%). Clinical coding of EHR-derived AMI diagnosis in primary care and secondary care was found to be accurate in different clinical settings and for different phenotypes. However, markers of myocardial necrosis, ECG and symptoms, the cornerstones of a clinical diagnosis, are underutilised and remain a challenge to retrieve from EHRs.
Copyright © 2015. Published by Elsevier Ireland Ltd.

Entities:  

Keywords:  Acute coronary syndrome; Clinical coding; Electronic health records; Myocardial infarction; Phenotype; Validation studies

Mesh:

Year:  2015        PMID: 25966015     DOI: 10.1016/j.ijcard.2015.03.075

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  22 in total

1.  PheValuator: Development and evaluation of a phenotype algorithm evaluator.

Authors:  Joel N Swerdel; George Hripcsak; Patrick B Ryan
Journal:  J Biomed Inform       Date:  2019-07-29       Impact factor: 6.317

Review 2.  A Survey of the Literature on Unintended Consequences Associated with Health Information Technology: 2014-2015.

Authors:  K Zheng; J Abraham; L L Novak; T L Reynolds; A Gettinger
Journal:  Yearb Med Inform       Date:  2016-11-10

Review 3.  Large-Scale Genomic Biobanks and Cardiovascular Disease.

Authors:  Aeron M Small; Christopher J O'Donnell; Scott M Damrauer
Journal:  Curr Cardiol Rep       Date:  2018-03-08       Impact factor: 2.931

4.  Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review.

Authors:  Sonal Singh; Hassan Fouayzi; Kathryn Anzuoni; Leah Goldman; Jea Young Min; Marie Griffin; Carlos G Grijalva; James A Morrow; Christine C Whitmore; Charles E Leonard; Mano Selvan; Vinit Nair; Yunping Zhou; Sengwee Toh; Andrew Petrone; James Williams; Elnara Fazio-Eynullayeva; Richard Swain; D Tyler Coyle; Susan Andrade
Journal:  Drug Saf       Date:  2019-04       Impact factor: 5.606

5.  Validity of Cardiovascular Data From Electronic Sources: The Multi-Ethnic Study of Atherosclerosis and HealthLNK.

Authors:  Faraz S Ahmad; Cheeling Chan; Marc B Rosenman; Wendy S Post; Daniel G Fort; Philip Greenland; Kiang J Liu; Abel N Kho; Norrina B Allen
Journal:  Circulation       Date:  2017-07-07       Impact factor: 29.690

6.  Use of troponins in the classification of myocardial infarction from electronic health records. The Atherosclerosis Risk in Communities (ARIC) Study.

Authors:  Anna M Kucharska-Newton; Matthew Shane Loop; Manuela Bullo; Carlton Moore; Stephanie W Haas; Lynne Wagenknecht; Eric A Whitsel; Gerardo Heiss
Journal:  Int J Cardiol       Date:  2021-12-16       Impact factor: 4.164

7.  Factors Associated With Cardiac Rehabilitation Participation in Older Adults After Myocardial Infarction: THE SILVER-AMI STUDY.

Authors:  David W Goldstein; Alexandra M Hajduk; Xuemei Song; Sui Tsang; Mary Geda; John A Dodson; Daniel E Forman; Harlan Krumholz; Sarwat I Chaudhry
Journal:  J Cardiopulm Rehabil Prev       Date:  2022-03-01       Impact factor: 2.081

8.  Does a Code for Acute Myocardial Infarction Mean the Same in All Norwegian Hospitals? A Likelihood Approach to a Medical Record Review.

Authors:  Jon Helgeland; Doris Tove Kristoffersen; Katrine Damgaard Skyrud
Journal:  Clin Epidemiol       Date:  2022-10-13       Impact factor: 5.814

9.  Data Resource Profile: The Scottish National Prescribing Information System (PIS).

Authors:  Samantha Alvarez-Madrazo; Stuart McTaggart; Clifford Nangle; Elizabeth Nicholson; Marion Bennie
Journal:  Int J Epidemiol       Date:  2016-05-10       Impact factor: 7.196

10.  Epidemiology of arthritis, chronic back pain, gout, osteoporosis, spondyloarthropathies and rheumatoid arthritis among 1.5 million patients in Australian general practice: NPS MedicineWise MedicineInsight dataset.

Authors:  David Alejandro González-Chica; Simon Vanlint; Elizabeth Hoon; Nigel Stocks
Journal:  BMC Musculoskelet Disord       Date:  2018-01-18       Impact factor: 2.362

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.