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Prediction model of isolated iliac and abdominal aneurysms
Author(s) -
Lanzarone Ettore,
Finotello Alice,
Pane Bianca,
Pratesi Giovanni,
Palombo Domenico,
Conti Michele,
Spinella Giovanni
Publication year - 2021
Publication title -
european journal of clinical investigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.164
H-Index - 107
eISSN - 1365-2362
pISSN - 0014-2972
DOI - 10.1111/eci.13517
Subject(s) - abdominal aortic aneurysm , logistic regression , medicine , incidence (geometry) , body mass index , cohort , risk factor , diabetes mellitus , aneurysm , disease , cardiology , surgery , endocrinology , mathematics , geometry
Objectives We analyse the cardiovascular risk factors in patients undergoing screening for Isolated Iliac Aneurysm (IIA) and Abdominal Aortic Aneurysm (AAA) and propose a logistic regression model to indicate patients at risk of IIA and/or AAA. Methods A screening programme was carried out to identify the presence of aneurysm based on Duplex scan examination. Cardiovascular risk factors information was collected from each subject. A descriptive analysis for the incidence of IIA and AAA stratified by age and sex was carried out to evaluate factors incidence. A logistic regression model was developed to predict the probability of developing an aneurysm based on the observed risk factor levels. A threshold probability of aneurysm risk for a datum patient was also identified to effectively direct screening protocols to patients most at risk. Results A cohort of 10 842 patients was evaluated: 1.52% affected by IIA, 2.69% by AAA and 3.90% by at least one. Risk factors analysis showed that: IIA was correlated with cardiological status, diabetes, cardiovascular disease family history, and dyslipidaemia; AAA was correlated with cardiological status, body mass index, hypertension, and dyslipidaemia; diabetes and dyslipidaemia were the most relevant factors with at least one aneurysm. The prediction tool based on the logistic regression and the threshold probability predict the presence of IIA and AAA in 69.7% and 83.8% of cases, under k ‐fold cross‐validation. Conclusions The proposed regression model can represent a valid aid to predict IIA and AAA presence and to select patients to be screened.