Automated Identification and Extraction of Exercise Treadmill Test Results

Abstract

Background Noninvasive cardiac tests, including exercise treadmill tests (ETTs), are commonly utilized in the evaluation of patients in the emergency department with suspected acute coronary syndrome. However, there are ongoing debates on their clinical utility and cost‐effectiveness. It is important to be able to use ETT results for research, but manual review is prohibitively time‐consuming for large studies. We developed and validated an automated method to interpret ETT results from electronic health records. To demonstrate the algorithm's utility, we tested the associations between ETT results with 30‐day patient outcomes in a large population. Methods and Results A retrospective analysis of adult emergency department encounters resulting in an ETT within 30 days was performed. A set of randomly selected reports were double‐blind reviewed by 2 physicians to validate a natural language processing algorithm designed to categorize ETT results into normal, ischemic, nondiagnostic, and equivocal categories. Natural language processing then searched and categorized results of 5214 ETT reports. The natural language processing algorithm achieved 96.4% sensitivity and 94.8% specificity in identifying normal versus all other categories. The rates of 30‐day death or acute myocardial infarction varied ( P <0.001) by categories for normal (0.08%), ischemic (1.9%), nondiagnostic (0.77%), and equivocal (0.58%) groups achieving good discrimination (C‐statistic, 0.81; 95% CI, 0.7–0.92). Conclusions Natural language processing is an accurate and efficient strategy to facilitate large‐scale outcome studies of noninvasive cardiac tests. We found that most patients are at low risk and have normal ETT results, while those with abnormal, nondiagnostic, or equivocal results have slightly higher risks and warrant future investigation.

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports

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“…4,17,26,40,57,109 Among some of the challenges to be faced, the incorporation of new technologies (such as those encompassing artificial intelligence) could further change the landscape by optimizing its assessment and scope. 23,110,111 Indeed, as digital tools become increasingly intertwined with several diagnostic modalities, so too may ExECG be affected by these applications. 11,[110][111][112] When considering ExECG in contemporary settings, an integrated assessment should be undertaken focusing not only on the ST segment but also on all the different and interlinked components that take part in the cardiovascular response to exercise and can be dynamically influenced by pathologic processes.…”

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