Vezzoli Marika1, Archetti Claudia2, Bianchessi Nicola3, Bonardelli Stefano4, Garrafa Emirena1*

1Department of Molecular and Translational Medicine, University of Brescia, Brescia, 25123, Italy. 2IDS Department, ESSEC Business School in Paris, Paris, 95021 Cergy-Pontoise Cedex, France. 3Department of Informatics “Giovanni degli Antoni”, University of Milan, Milan, 20133, Italy. 4Department of Clinical and Experimental Sciences, University of Brescia, Brescia, 25123, Italy.

Abdominal Aortic Aneurysm (AAA) is a localized enlargement of the abdominal aorta, such that the diameter exceeds 30 mm. AAA is a progressive growth leading to rupture, with high risk of mortality, therefore elective surgical repair is indicated when AAA diamenter is >55 mm. Screening programs, that use morphological imaging, have been developed internationally with the aim of detecting AAA before rupture with important limitations in term of cost and benefit for patients. Furthermore, different biochemical markers have been proposed to monitor AAA progression to overcome the above-mentioned limitations but none of them is used in the clinical practice. In fact, most of the biomarkers proposed are expensive and not feasible in the majority of laboratories. Combining different methodologies coming from Statistics and Operational Research fields, we developed an algorithm able to assess the importance of common biomarkers, requested in the clinical practice to evaluate the health of patient, and therefore no exams are required. Furthermore, we develop an Easy, Affordable Statistics and Economic (EASE) model able to identify if the AAA remain below the cut off for surgical repair. This prediction can provide guidance to how closely the patient’s abdominal aorta should be monitored avoiding additional and expensive exams.

Keywords: Abdominal Aortic Aneurysm, EASE

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Vezzoli Marika, Archetti Claudia, Bianchessi Nicola, Bonardelli Stefano, Garrafa Emirena. An Easy Affordable Statistical and Economic (EASE) approach to avoid unnecessary and expensive exams to monitor patients with small AAA EASE Score. American Journal of Cardiology Research and Reviews, 2021, 4:18. DOI:10.28933/ajcrar-2021-10-1305


1. Sakalihasan, N. et al. Abdominal aortic aneurysms. Nat. Rev. Dis. Primer 4, 34 (2018).
2. Tang, A. et al. Morphologic evaluation of ruptured and symptomatic abdominal aortic aneurysm by three-dimensional modeling. J. Vasc. Surg. 59, 894-902.e3 (2014).
3. Thompson, S. G. et al. Systematic review and meta-analysis of the growth and rupture rates of small abdominal aortic aneurysms: implications for surveillance intervals and their cost-effectiveness. Health Technol. Assess. Winch. Engl. 17, 1–118 (2013).
4. Lederle, F. A. et al. Multicentre study of abdominal aortic aneurysm measurement and enlargement. Br. J. Surg. 102, 1480–1487 (2015).
5. Flondell-Sité, D., Lindblad, B., Kölbel, T. & Gottsäter, A. Cytokines and systemic bi-omarkers are related to the size of abdominal aortic aneurysms. Cytokine 46, 211–215 (2009).
6. Wanhainen, A. et al. Editor’s Choice – European Society for Vascular Surgery (ESVS) 2019 Clinical Practice Guidelines on the Management of Abdominal Aorto-iliac Artery An-eurysms. Eur. J. Vasc. Endovasc. Surg. Off. J. Eur. Soc. Vasc. Surg. 57, 8–93 (2019).
7. Garrafa, E. et al. Prediction of abdominal aortic aneurysm calcification by means of var-iation of high-sensitivity C-reactive protein. JRSM Cardiovasc. Dis. 5, 2048004016682177 (2016).
8. Mussa, F. F. Screening for abdominal aortic aneurysm. J. Vasc. Surg. 62, 774–778 (2015).
9. Guirguis-Blake, J. M., Beil, T. L., Senger, C. A. & Whitlock, E. P. Ultrasonography screening for abdominal aortic aneurysms: a systematic evidence review for the U.S. Preventive Services Task Force. Ann. Intern. Med. 160, 321–329 (2014).
10. Lindholt, J. S., Vammen, S., Fasting, H. & Henneberg, E. W. Psychological conse-quences of screening for abdominal aortic aneurysm and conservative treatment of small abdominal aortic aneurysms. Eur. J. Vasc. Endovasc. Surg. Off. J. Eur. Soc. Vasc. Surg. 20, 79–83 (2000).
11. Garrafa, E. et al. Association between human parainfluenza virus type 1 and smoking history in patients with an abdominal aortic aneurysm. J. Med. Virol. 85, 99–104 (2013).
12. Stather, P. W. et al. Meta-analysis and meta-regression analysis of biomarkers for ab-dominal aortic aneurysm. Br. J. Surg. 101, 1358–1372 (2014).
13. Vezzoli, M., Bonardelli, S., Peroni, M., Ravanelli, M. & Garrafa, E. A Simple Blood Test, Such as Complete Blood Count, Can Predict Calcification Grade of Abdominal Aortic Aneurysm. Int. J. Vasc. Med. 2017, 1370751 (2017).
14. Moris, D. et al. Novel biomarkers of abdominal aortic aneurysm disease: identifying gaps and dispelling misperceptions. BioMed Res. Int. 2014, 925840 (2014).
15. Jalalzadeh, H. et al. Inflammation as a Predictor of Abdominal Aortic Aneurysm Growth and Rupture: A Systematic Review of Imaging Biomarkers. Eur. J. Vasc. Endovasc. Surg. Off. J. Eur. Soc. Vasc. Surg. 52, 333–342 (2016).
16. Wanhainen, A., Mani, K. & Golledge, J. Surrogate Markers of Abdominal Aortic Aneu-rysm Progression. Arterioscler. Thromb. Vasc. Biol. 36, 236–244 (2016).
17. Garrafa, E. & Bonardelli, S. Re ‘Calcification of Thoracic and Abdominal Aneurysms is Associated with Mortality and Morbidity’. Abdominal Aortic Aneurysm Calcification: Are Biochemical Markers a Missing Piece of the Puzzle? Eur. J. Vasc. Endovasc. Surg. Off. J. Eur. Soc. Vasc. Surg. 55, 900 (2018).
18. Lindberg, S., Zarrouk, M., Holst, J. & Gottsäter, A. Inflammatory markers associated with abdominal aortic aneurysm. Eur. Cytokine Netw. 27, 75–80 (2016).
19. Hao, W. et al. A mathematical model of aortic aneurysm formation. PloS One 12, e0170807 (2017).
20. Savona, R. & Vezzoli, M. Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals. Oxf. Bull. Econ. Stat. 77, 66–92 (2015).
21. Doglietto, F. et al. Factors Associated With Surgical Mortality and Complications Among Patients With and Without Coronavirus Disease 2019 (COVID-19) in Italy. JAMA Surg. (2020) doi:10.1001/jamasurg.2020.2713.
22. Carpita, M. & Vezzoli, M. Statistical evidence of the subjective work quality: the fairness drivers of the job satisfaction. Electron. J. Appl. Stat. Anal. 5, 89–107 (2012).
23. Evangelista, A. Imaging aortic aneurysmal disease. Heart Br. Card. Soc. 100, 909–915 (2014).
24. Collins, J. T., Boros, M. J. & Combs, K. Ultrasound surveillance of endovascular aneu-rysm repair: a safe modality versus computed tomography. Ann. Vasc. Surg. 21, 671–675 (2007).
25. Armstrong, P. A. et al. Optimizing compliance, efficiency, and safety during surveillance of small abdominal aortic aneurysms. J. Vasc. Surg. 46, 190–195; discussion 195-196 (2007).
26. Salman, H. E., Ramazanli, B., Yavuz, M. M. & Yalcin, H. C. Biomechanical Investigation of Disturbed Hemodynamics-Induced Tissue Degeneration in Abdominal Aortic Aneu-rysms Using Computational and Experimental Techniques. Front. Bioeng. Biotechnol. 7, 111 (2019).
27. Fernandez-Delgado, M., Cernadas, E., Barro, S. & Amorim, D. Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? J. Mach. Learn. Res. 15, 3133–3181 (2014).
28. Deo Rahul C. Machine Learning in Medicine. Circulation 132, 1920–1930 (2015).
29. Giger, M. L. Machine Learning in Medical Imaging. J. Am. Coll. Radiol. JACR 15, 512–520 (2018).
30. Vezzoli, M. et al. RERT: A Novel Regression Tree Approach to Predict Extrauterine Dis-ease in Endometrial Carcinoma Patients. Sci. Rep. 7, 10528 (2017).
31. Bennett, K. P. Decision Tree Construction Via Linear Programming. in Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society Conference 97–101 (M. Evans, 1992).
32. Street, W. N. Oblique Multicategory Decision Trees Using Nonlinear Programming. Inf. J. Comput. 17, 25–31 (2005).
33. Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees. (Taylor & Francis, 1984).
34. Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).
35. Mangasarian, O. L. Linear and Nonlinear Separation of Patterns by Linear Programming. Oper. Res. 13, 444–452 (1965).
36. Mangasarian, O. Multisurface method of pattern separation. IEEE Trans. Inf. Theory 14, 801–807 (1968).

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