Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

IF 2.5 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Journal of Investigative Medicine Pub Date : 2024-10-01 Epub Date: 2024-07-31 DOI:10.1177/10815589241258968
Andrew Talon, Chahat Puri, Dylan L Mccreary, Daniel Windschill, Weston Bowker, Yuqing A Gao, Suresh Uppalapu, Manoj Mathew
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引用次数: 0

Abstract

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI) may help improve pulmonary embolism (PE) management with early CT pulmonary angiogram (CTPA) screening and accelerate PERT coordination. This study aimed to test the clinical validity of an FDA-approved PE AI algorithm. CTPA scan data of 200 patients referred due to automated AI detection of suspected PE were retrospectively reviewed. In our institution, all patients suspected of PE received a CTPA. The AI app was then used to analyze CTPA for the presence of PE and calculate the right-ventricle/left-ventricle (RV/LV) ratio. We compared the AI's output with the radiologists' report. Inclusion criteria included segmental PE with and without RV dysfunction and high-risk PE. The primary endpoint was false positive rate. Secondary end points included clinical outcomes according to the therapy selected, including catheter-directed interventions, systemic thrombolytics, and anticoagulation. Fifty-seven of 200 exams (28.5%) were correctly identified as positive for PE by the algorithm. A total of 143 exams (71.5%) were incorrectly reported as positive. In 8% of cases, PERT was consulted. Four patients (7%) received systemic thrombolytics without any complications. There were six patients (10.5%) who developed high-risk PE and underwent thrombectomy, one of whom died. Among 46 patients with acute PE without right heart strain, 44 (95%) survived. The false positive rate of our AI algorithm was 71.5%, higher than what was reported in the AI's prior clinical validity study (91% sensitivity, 100% specificity). A high rate of discordant AI auto-detection of suspected PE raises concerns about its diagnostic accuracy. This can lead to increased workloads for PERT consultants, alarm/notification fatigue, and automation bias. The AI direct notification process to the PERT team did not improve PERT triage efficacy.

人工智能在肺栓塞管理和肺栓塞应对小组启动方面的单中心成果。
多学科肺栓塞应对小组(PERTs)的工作表明,及时分流可加快治疗。使用人工智能(AI)可能有助于通过早期 CT 肺血管造影(CTPA)筛查改善肺栓塞(PE)管理,并加快 PERT 的协调。本研究旨在测试美国食品药品管理局批准的肺栓塞人工智能算法的临床有效性。我们回顾性审查了因自动 AI 检测出疑似 PE 而转诊的 200 名患者的 CTPA 扫描数据。在我院,所有疑似 PE 患者都接受了 CTPA 扫描。然后使用 AI 应用程序分析 CTPA 是否存在 PE,并计算右心室/左心室(RV/LV)比率。我们将 AI 的输出结果与放射科医生的报告进行了比较。纳入标准包括伴有或不伴有 RV 功能障碍的节段性 PE 和高风险 PE。主要终点是假阳性率。次要终点包括根据所选疗法(包括导管引导介入疗法、全身溶栓疗法和抗凝疗法)得出的临床结果。200 次检查中有 57 次(28.5%)被算法正确识别为 PE 阳性。共有 143 项检查(71.5%)被错误报告为阳性。8%的病例咨询了 PERT。四名患者(7%)接受了全身溶栓治疗,未出现任何并发症。有六名患者(10.5%)出现高危 PE 并接受了血栓切除术,其中一人死亡。在 46 名急性 PE 且无右心肌劳损的患者中,44 人(95%)存活。我们的人工智能算法的假阳性率为 71.5%,高于人工智能之前的临床有效性研究报告(灵敏度 91%,特异性 100%)。人工智能自动检测疑似 PE 的不一致率较高,令人担忧其诊断准确性。这会导致 PERT 顾问工作量增加、警报/通知疲劳和自动化偏差。人工智能直接通知 PERT 团队的流程并未提高 PERT 分诊效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Investigative Medicine
Journal of Investigative Medicine 医学-医学:内科
CiteScore
4.90
自引率
0.00%
发文量
111
审稿时长
24 months
期刊介绍: Journal of Investigative Medicine (JIM) is the official publication of the American Federation for Medical Research. The journal is peer-reviewed and publishes high-quality original articles and reviews in the areas of basic, clinical, and translational medical research. JIM publishes on all topics and specialty areas that are critical to the conduct of the entire spectrum of biomedical research: from the translation of clinical observations at the bedside, to basic and animal research to clinical research and the implementation of innovative medical care.
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