title: Use of electronic health records to ascertain, validate and phenotype acute myocardial infarction: A systematic review and recommendations.
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.
authors.affiliation: Farr Institute of Health Informatics Research, University College London, UK. Electronic address: b.rubbo@ucl.ac.uk.
authors.forename: Bruna
authors.initials: B
authors.lastname: Rubbo
authors.affiliation: Farr Institute of Health Informatics Research, University College London, UK.
authors.forename: Natalie K
authors.initials: NK
authors.lastname: Fitzpatrick
authors.affiliation: Farr Institute of Health Informatics Research, University College London, UK.
authors.forename: Spiros
authors.initials: S
authors.lastname: Denaxas
authors.affiliation: Department of Infection & Population Health, The Royal Free Hospital NHS Trust, London, UK.
authors.forename: Marina
authors.initials: M
authors.lastname: Daskalopoulou
authors.affiliation: Farr Institute of Health Informatics Research, University College London, UK.
authors.forename: Ning
authors.initials: N
authors.lastname: Yu
authors.affiliation: Farr Institute of Health Informatics Research, University College London, UK; The Heart Hospital, University College London NHS Trust, London, UK.
authors.forename: Riyaz S
authors.initials: RS
authors.lastname: Patel
authors.affiliation: Farr Institute of Health Informatics Research, University College London, UK.
authors.forename: Harry
authors.initials: H
authors.lastname: Hemingway
mesh_terms: D057286:Electronic Health Records; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000235:genetics; D010641:Phenotype; D015203:Reproducibility of Results
keywords: Acute coronary syndrome; Clinical coding; Electronic health records; Myocardial infarction; Phenotype; Validation studies