RESUMO
OBJECTIVE: Despite recommendations by the United States Preventive Services Task Force and the Society for Vascular Surgery, adoption of screening for abdominal aortic aneurysms (AAAs) remains low. One challenge is the low prevalence of AAAs in the unscreened population, and therefore a low detection rate for AAA screenings. We sought to use machine learning to identify factors associated with the presence of AAAs and create a model to identify individuals at highest risk for AAAs, with the aim of increasing the detection rate of AAA screenings. METHODS: A machine-learning model was trained using longitudinal medical records containing lab results, medications, and other data from our institutional database. A retrospective cohort study was performed identifying current or past smoking in patients aged 65 to 75 years and stratifying the patients by sex and smoking status as well as determining which patients had a confirmed diagnosis of AAA. The model was then adjusted to maximize fairness between sexes without significantly reducing precision and validated using six-fold cross validation. RESULTS: Validation of the algorithm on the single-center institutional data utilized 18,660 selected patients over 2 years and identified 314 AAAs. There were 41 factors identified in the medical record included in the machine-learning algorithm, with several factors never having been previously identified to be associated with AAAs. With an estimated 100 screening ultrasounds completed monthly, detection of AAAs is increased with a lift of 200% using the algorithm as compared with screening based on guidelines. The increased detection of AAAs in the model-selected individuals is statistically significant across all cutoff points. CONCLUSIONS: By utilizing a machine-learning model, we created a novel algorithm to detect patients who are at high risk for AAAs. By selecting individuals at greatest risk for targeted screening, this algorithm resulted in a 200% lift in the detection of AAAs when compared with standard screening guidelines. Using machine learning, we also identified several new factors associated with the presence of AAAs. This automated process has been integrated into our current workflows to improve screening rates and yield of high-risk individuals for AAAs.
Assuntos
Aneurisma da Aorta Abdominal , Fumar , Humanos , Estados Unidos , Fatores de Risco , Estudos Retrospectivos , Fumar/efeitos adversos , Aneurisma da Aorta Abdominal/diagnóstico por imagem , Aneurisma da Aorta Abdominal/epidemiologia , Programas de Rastreamento/métodos , Aprendizado de Máquina , UltrassonografiaRESUMO
We present with full and proper consent of the patient, the case of a 64-year-old man with severe peripheral arterial disease and a known chronic infrarenal aortic occlusion causing severe short-distance claudication. Preoperative computed tomography angiography was significant for a new "cylindrical" calcified lesion. During the elective surgery, the lesion was confirmed to be a coronary stent. The coronary stent was confirmed to be from the patient's prior percutaneous coronary intervention to the left anterior descending artery 1 year prior. The stent was removed without complications by the surgical team. To the best of our knowledge, this is the first such case to be described in current literature. This patient is currently alive, and a revision of his left anterior descending artery intervention was found to be unwarranted on repeat coronary angiography.