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.

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

“… 39 In many cases this limitation can be overcome by linkage to secondary care or local datasets, and case identification can be optimized with the corresponding algorithm as has been done in other data resources. 43 , 44 …”

Validity of Cardiovascular Data From Electronic Sources

“…Few prior studies on EHR detection algorithms for MI go beyond diagnosis codes to use data such as ECGs, biomarkers, and text extracted by natural language processing. 46 Using diverse data types may lead to EHR algorithms with better detection performance and the ability to classify events based on criteria established in consensus statements, including differentiation between Type 1 and Type 2 MIs based on the Third Universal Definition of Myocardial Infarction. 47 …”

UK phenomics platform for developing and validating EHR phenotypes: CALIBER

“…In contrast with the US, only recently have scalable methods been developed to access the entire hospital record for expert review [33] and text corpora are not available at scale [34]. There have been few previous studies [35] of the validity of International Classification of Disease and Health Related Problems, 10th Revision (ICD-10)…”