Journal of vascular surgery · 2025
Utilizing natural language processing to improve abdominal aortic aneurysm surveillance.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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DiLosa Kathryn, Schneck Matthew, Xu Emily, Mushell Aida, Sayrafi Dena, Maximus Steven, Mell Matthew, Humphries Misty
Аннотация
BACKGROUND: Screening and surveillance are essential to prevent aneurysm rupture. We used natural language processing (NLP) software to evaluate efficacy of aneurysm surveillance.
METHODS: NLP software was used to review 7 years of imaging reports at a single institution to identify patients with an abdominal aortic aneurysm. After identification of a patient cohort, review of the electronic medical record was completed to collect patient demographics and information about compliance with nationally recommended aneurysm surveillance protocols.
RESULTS: NLP identified a cohort of 1424 patients with an abdominal aortic aneurysm; 1105 (77.6%) were male and the mean age was 74 ± 10 years. Of these patients, 76% were White, 6% were Black, 7% were Asian, 1% was Native American or Pacific Islander, and 11% identified as another race. At the time of data collection, 552 patients (39%) were under active surveillance, 346 patients (24%) had previously been under surveillance and subsequently lost to follow-up, and 523 patients (37%) had incidental findings without initiation of surveillance. In total, more than one-half of the cohort (869 patients [61%]) were not participating in recommended aneurysm surveillance. Findings did not differ significantly by gender, race, or ethnicity.
CONCLUSIONS: In the cohort, 39% of patients were compliant with recommended surveillance. Incorporating NLP into traditional screening and surveillance practices can allow providers to identify patients outside of recommended surveillance, bridging gaps in care.
METHODS: NLP software was used to review 7 years of imaging reports at a single institution to identify patients with an abdominal aortic aneurysm. After identification of a patient cohort, review of the electronic medical record was completed to collect patient demographics and information about compliance with nationally recommended aneurysm surveillance protocols.
RESULTS: NLP identified a cohort of 1424 patients with an abdominal aortic aneurysm; 1105 (77.6%) were male and the mean age was 74 ± 10 years. Of these patients, 76% were White, 6% were Black, 7% were Asian, 1% was Native American or Pacific Islander, and 11% identified as another race. At the time of data collection, 552 patients (39%) were under active surveillance, 346 patients (24%) had previously been under surveillance and subsequently lost to follow-up, and 523 patients (37%) had incidental findings without initiation of surveillance. In total, more than one-half of the cohort (869 patients [61%]) were not participating in recommended aneurysm surveillance. Findings did not differ significantly by gender, race, or ethnicity.
CONCLUSIONS: In the cohort, 39% of patients were compliant with recommended surveillance. Incorporating NLP into traditional screening and surveillance practices can allow providers to identify patients outside of recommended surveillance, bridging gaps in care.