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

Autor:	Talon A; Pulmonary/Critical Care, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA., Puri C; Pulmonary/Critical Care, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA., Mccreary DL; Internal Medicine, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA., Windschill D; Internal Medicine, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA., Bowker W; Pulmonary/Critical Care, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA., Gao YA; Pulmonary and Critical Care, UCLA Health Santa Monica Pulmonary Sleep Clinic, Santa Monica, CA, USA., Uppalapu S; Pulmonary/Critical Care, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA., Mathew M; Pulmonary/Critical Care, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA.
Jazyk:	angličtina
Zdroj:	Journal of investigative medicine : the official publication of the American Federation for Clinical Research [J Investig Med] 2024 Oct; Vol. 72 (7), pp. 652-660. Date of Electronic Publication: 2024 Jul 31.
DOI:	10.1177/10815589241258968
Abstrakt:	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. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu