Research and Evaluation Department, Kaiser Permanente Southern California, 100 S Los Robles Ave, 2nd Floor, Pasadena, CA, 91101, USA.
Department of Emergency Medicine and Leonard Davis Institute, University of Pennsylvania, Philadelphia, PA, USA.
J Nucl Cardiol. 2022 Jun;29(3):1178-1187. doi: 10.1007/s12350-020-02401-z. Epub 2020 Nov 5.
Findings and interpretations of myocardial perfusion imaging (MPI) studies are documented in free-text MPI reports. MPI results are essential for research, but manual review is prohibitively time consuming. This study aimed to develop and validate an automated method to abstract MPI reports.
We developed a natural language processing (NLP) algorithm to abstract MPI reports. Randomly selected reports were double-blindly reviewed by two cardiologists to validate the NLP algorithm. Secondary analyses were performed to describe patient outcomes based on abstracted-MPI results on 16,957 MPI tests from adult patients evaluated for suspected ACS.
The NLP algorithm achieved high sensitivity (96.7%) and specificity (98.9%) on the MPI categorical results and had a similar degree of agreement compared to the physician reviewers. Patients with abnormal MPI results had higher rates of 30-day acute myocardial infarction or death compared to patients with normal results. We identified issues related to the quality of the reports that not only affect communication with referring physicians but also challenges for automated abstraction.
NLP is an accurate and efficient strategy to abstract results from the free-text MPI reports. Our findings will facilitate future research to understand the benefits of MPI studies but requires validation in other settings.
心肌灌注成像（MPI）研究的结果和解释以 MPI 报告的自由文本形式呈现。MPI 结果对研究至关重要，但手动审查非常耗时。本研究旨在开发和验证一种自动提取 MPI 报告的方法。
我们开发了一种自然语言处理（NLP）算法来提取 MPI 报告。随机选择的报告由两名心脏病专家进行双盲审查，以验证 NLP 算法的准确性。在对 16957 例疑似 ACS 患者进行 MPI 评估的患者中，基于提取的 MPI 结果进行了二次分析，以描述患者的结局。
NLP 算法在 MPI 分类结果上具有很高的灵敏度（96.7%）和特异性（98.9%），与医师审阅者具有相似的一致性程度。MPI 结果异常的患者与 MPI 结果正常的患者相比，30 天内急性心肌梗死或死亡的发生率更高。我们发现了与报告质量相关的问题，这些问题不仅影响与转诊医生的沟通，而且对自动提取也构成挑战。
NLP 是一种从自由文本 MPI 报告中提取结果的准确且高效的策略。我们的研究结果将有助于未来研究了解 MPI 研究的益处，但需要在其他环境中进行验证。
