This study aimed to (1) develop and validate a natural language processing model to identify the presence of pulmonary embolism (PE) based on real-time radiology reports and (2) identify low-risk PE patients based on previously validated risk stratification scores using variables extracted from the electronic health record at the time of diagnosis. The combination of these approaches yielded an natural language processing-based clinical decision support tool that can identify patients presenting to the emergency department (ED) with low-risk PE as candidates for outpatient management. Data were curated from all patients who received a PE-protocol computed tomography pulmonary angiogram (PE-CTPA) imaging study in the ED of a 3-hospital academic health system between June 1, 2018 and December 31, 2020 (n=12,183). The “preliminary” radiology reports from these imaging studies made available to ED clinicians at the time of diagnosis were adjudicated as positive or negative for PE by the clinical team. The reports were then divided into development, internal validation, and temporal validation cohorts in order to train, test, and validate an natural language processing model that could identify the presence of PE based on unstructured text. For risk stratification, patient- and encounter-level data elements were curated from the electronic health record and used to compute a real-time simplified pulmonary embolism severity (sPESI) score at the time of diagnosis. Chart abstraction was performed on all low-risk PE patients admitted for inpatient management. When applied to the internal validation and temporal validation cohorts, the natural language processing model identified the presence of PE from radiology reports with an area under the receiver operating characteristic curve of 0.99, sensitivity of 0.86 to 0.87, and specificity of 0.99. Across cohorts, 10.5% of PE-CTPA studies were positive for PE, of which 22.2% were classified as low-risk by the sPESI score. Of all low-risk PE patients, 74.3% were admitted for inpatient management. This study demonstrates that a natural language processing-based model utilizing real-time radiology reports can accurately identify patients with PE. Further, this model, used in combination with a validated risk stratification score (sPESI), provides a clinical decision support tool that accurately identifies patients in the ED with low-risk PE as candidates for outpatient management.

中文翻译：

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开发和验证自然语言处理模型，实时识别低风险肺栓塞，以促进安全门诊管理

本研究旨在 (1) 开发和验证自然语言处理模型，以根据实时放射学报告识别肺栓塞 (PE) 的存在，以及 (2) 根据先前验证的风险分层评分，识别低风险 PE 患者诊断时从电子健康记录中提取的变量。这些方法的结合产生了一种基于自然语言处理的临床决策支持工具，可以识别到急诊科 (ED) 就诊的低风险 PE 患者作为门诊管理的候选者。数据来源于 2018 年 6 月 1 日至 2020 年 12 月 31 日期间在 3 家医院学术医疗系统的急诊室接受 PE 协议计算机断层扫描肺血管造影 (PE-CTPA) 成像研究的所有患者 (n=12,183)。临床团队在诊断时向急诊科临床医生提供的这些影像学研究的“初步”放射学报告被判定为 PE 阳性或阴性。然后，这些报告被分为开发、内部验证和时间验证队列，以便训练、测试和验证自然语言处理模型，该模型可以根据非结构化文本识别 PE 的存在。对于风险分层，从电子健康记录中整理患者和遭遇级别的数据元素，并用于计算诊断时的实时简化肺栓塞严重程度 (sPESI) 评分。对所有入院治疗的低风险肺栓塞患者进行图表提取。当应用于内部验证和时间验证队列时，自然语言处理模型从放射学报告中识别出 PE 的存在，受试者工作特征曲线下面积为 0.99，敏感性为 0.86 至 0.87，特异性为 0.99。在各队列中，10.5% 的 PE-CTPA 研究呈 PE 阳性，其中 22.2% 根据 sPESI 评分被归类为低风险。在所有低风险肺栓塞患者中，74.3% 入院接受住院治疗。这项研究表明，利用实时放射学报告的基于自然语言处理的模型可以准确识别 PE 患者。此外，该模型与经过验证的风险分层评分 (sPESI) 结合使用，提供了一种临床决策支持工具，可以准确地将急诊室中患有低风险 PE 的患者识别为门诊管理的候选者。