Geospatial epidemiology of coronary artery disease treated with percutaneous coronary intervention in Crete, Greece

Submitted: 6 November 2023
Accepted: 2 April 2024
Published: 16 May 2024
Abstract Views: 1509
PDF: 122
HTML: 1
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Coronary artery disease (CAD) constitutes a leading cause of morbidity and mortality worldwide. Percutaneous coronary intervention (PCI) is indicated in a significant proportion of CAD patients, either to improve prognosis or to relieve symptoms not responding to optimal medical therapy. Thus the annual number of patients undergoing PCI in a given geographical area could serve as a surrogate marker of the total CAD burden there. The aim of this study was to analyze the potential, spatial patterns of PCItreated CAD patients in Crete. We evaluated data from all patients subjected to PCI at the island’s sole reference centre for cardiac catheterization within a 4-year study period (2013-2016). The analysis focused on regional variations of yearly PCI rates, as well as on the effect of several clinical parameters on the severity of the coronary artery stenosis treated with PCI across Crete. A spatial database within the ArcGIS environment was created and an analysis carried out based on global and local regression using ordinary least squares (OLS) and geographically weighted regression (GWR), respectively. The results revealed significant inter-municipality variation in PCI rates and thus potentially CAD burden, while the degree and direction of correlation between key clinical factors to coronary stenosis severity demonstrated specific geographical patterns. These preliminary results could set the basis for future research, with the ultimate aim to facilitate efficient healthcare strategies planning.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Andreopoulos P, Kalogeropoulos K, Tragaki A, Stathopoulos N, 2021. Could Historical Mortality Data Predict Mortality Due to Unexpected Events? ISPRS Int J Geo-Inform 10:283. DOI: https://doi.org/10.3390/ijgi10050283
Alston L, Allender S, Peterson K, Jacobs J, Nichols M, 2017. Rural inequalities in the Australian burden of ischaemic heart disease: A systematic review. Heart Lung Circ 26:122–33. DOI: https://doi.org/10.1016/j.hlc.2016.06.1213
Bauer T, Möllmann H, Weidinger F, Zeymer U, Seabra-Gomes R, Eberli F, Serruys P, Vahanian A, Silber S, Wijns W, 2010. Predictors of hospital mortality in the elderly undergoing percutaneous coronary intervention for acute coronary syndromes and stable angina. Int J Cardiol 151:164–9. DOI: https://doi.org/10.1016/j.ijcard.2010.05.006
Berg GD, Mansley EC, 2004. Endogeneity bias in the absence of unobserved heterogeneity. Ann Epidemiol 14:561–5. DOI: https://doi.org/10.1016/j.annepidem.2003.09.020
Bhatnagar P, Wickramasinghe K, Williams J, Rayner M, Townsend N, 2014. The epidemiology of cardiovascular disease in the UK 2014. Heart 101:1182-9. DOI: https://doi.org/10.1136/heartjnl-2015-307516
Brunsdon C, Fotheringham S, Charlton M, 1998. Geographically weighted regression. J R Statist Soc D 47:431–43. DOI: https://doi.org/10.1111/1467-9884.00145
Chalkias C, Papadopoulos A.G, Kalogeropoulos K, Tambalis K, Psarra G, Sidossis L, 2013. Geographical heterogeneity of the relationship between childhood obesity and socio-environmental status: Empirical evidence from Athens, Greece. Appl Geogr 37:34–43. DOI: https://doi.org/10.1016/j.apgeog.2012.10.007
Chhabra S.T, Kaur T, Masson S, Soni R.K, Bansal N, Takkar B, Tandon R, Goyal A, Singh B, Aslam N, 2018. Early onset ACS: An age based clinico-Epidemiologic and angiographic comparison. Atherosclerosis 279;45–51. DOI: https://doi.org/10.1016/j.atherosclerosis.2018.10.017
Collet J. P, Thiele H, Barbato E, Barthélémy O, Bauersachs J, Bhatt D. L, Dendale P, Dorobantu M, Edvardsen T, Folliguet T, Gale C. P, Gilard M, Jobs A, Jüni P, Lambrinou E, Lewis B. S, Mehilli J, Meliga E, Merkely B, Mueller C, ESC Scientific Document Group,2021. 2020 Esc guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. European Heart J 42:1289–367. DOI: https://doi.org/10.15829/1560-4071-2021-4418
Donner A, 1984. Linear regression analysis with repeated measurements. J Chronic Dis 37:441–8. DOI: https://doi.org/10.1016/0021-9681(84)90027-4
Dudley RA, Harrell FE, Richard Smith L, Mark D.B, Califf RM, Pryor DB, Glower D, Lipscomb J, Hlatky M, 1993. Comparison of analytic models for estimating the effect of clinical factors on the cost of coronary artery bypass graft surgery. J Clin Epidemiol 46:261–71. DOI: https://doi.org/10.1016/0895-4356(93)90074-B
Faka A, 2020. Assessing quality of life inequalities. A geographical approach. ISPRS Int J Geoinf 9:600. DOI: https://doi.org/10.3390/ijgi9100600
Faka A, Kalogeropoulos K, Maloutas T, Chalkias C. 2021. Urban quality of life: Spatial modeling and indexing in Athens metropolitan area, Greece. ISPRS Int. J Geoinf 10:347. DOI: https://doi.org/10.3390/ijgi10050347
Faka A, Kalogeropoulos K, Maloutas T, Chalkias C,2022. Spatial variability and clustering of quality of life at local level: A geographical analysis in Athens, Greece. ISPRS Int J Geoinf 11:276. DOI: https://doi.org/10.3390/ijgi11050276
Fotheringham AS, Brunsdon C, Charlton M, 2002. Geographically weighted regression: The analysis of spatially varying relationships; New Jersey: Wiley ISBN 978-0-471-49616-8.
Gatrell A.C, Bailey T.C, Diggle P.J, Rowlingson B.S, 1996. Spatial point pattern analysis and its application in geographical epidemiology. Trans Inst Br Geogr 21:256. DOI: https://doi.org/10.2307/622936
Goodman E, Huang B, Wade TJ, Kahn RS, 2003. A multilevel analysis of the relation of socioeconomic status to adolescent depressive symptoms: Does school context matter? J Pediatr 143:451–6. DOI: https://doi.org/10.1067/S0022-3476(03)00456-6
Fischer MM, Getis A, 2010. Handbook of applied spatial analysis; Berlin Heidelberg: Springer ISBN 978-3-642-03646-0.
Hannan EL, Zhong Y, Berger PB, Jacobs AK, Walford G, Ling FSK, Venditti FJ, King SB, 2018. Association of coronary vessel characteristics with outcome in patients with percutaneous coronary interventions with incomplete revas-cularization. JAMA Cardiol 3:123. DOI: https://doi.org/10.1001/jamacardio.2017.4787
Hurvich CM, Simonoff JS, Tsai CL, 1998. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc Series B Stat Methodol 60:271–93. DOI: https://doi.org/10.1111/1467-9868.00125
Ibanez B, James S, Agewall S, Antunes M. J, Bucciarelli-Ducci C, Bueno H et al., 2017. ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 39:119–77.
Kataruka A, Maynard CC, Kearney KE, Mahmoud A, Bell S, Doll JA, McCabe JM, Bryson C, Gurm HS, Jneid H, Virani SS, Lehr E, Ring ME, Hira RS, 2020. Temporal trends in percutaneous coronary intervention and coronary artery bypass grafting: Insights from the Washington Cardiac Care Outcomes Assessment Program. J Am Heart Assoc 9:e015317. DOI: https://doi.org/10.1161/JAHA.119.015317
Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, et al., 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41;407–77.
Komajda M, Weidinger F, Kerneis M, Cosentino F, Cremonesi A, Ferrari R, Kownator S, Steg PG, Tavazzi L, Valgimigli M, Szwed H, Majda W, Olivari Z, Van Belle E, Shlyakhto EV, Mintale I, Slapikas R, Rittger H, Mendes M, Tsioufis C, Balanescu S, Laroche C, Maggioni AP, 2016. EURObservational research programme: the chronic ischaemic cardiovascular disease registry: Pilot phase (CICD-PILOT). Eur Heart J 37:52–160. DOI: https://doi.org/10.1093/eurheartj/ehv437
Mena C, Sepúlveda C, Fuentes E, Ormazábal Y, Palomo I, 2018. Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search. Geospat Health 13:587. DOI: https://doi.org/10.4081/gh.2018.587
Movsisyan NK, Vinciguerra M, Medina-Inojosa JR, Lopez-Jimenez F, 2020. Cardiovascular diseases in central and eastern Europe: A call for more surveillance and evidence-based health promotion. Ann Glob Health 86:21. DOI: https://doi.org/10.5334/aogh.2713
Namayande MS, Nejadkoorki F, Namayande SM, Dehghan H, 2016. Spatial hotspot analysis of acute myocardial infarction events in an urban population: A correlation study of health problems and industrial installation. Iran J Public Health 45:94-101.
New York State Department of Health, 2015. Percutaneous coronary interventions (PCI) in New York State 2010-2012; New York State cardiac advisory committee: Available from: https://www.health.ny.gov/statistics/diseases/cardiovascular/docs/pci_2010-2012.pdf
Panagiotakos DB, Pitsavos C, Leda Matalas A, Chrysohoou C, Stefanadis C, 2005. Geographical influences on the association between adherence to the mediterranean diet and the prevalence of acute coronary syndromes, in Greece: The CARDIO2000 study. Int J Cardiol 100:135–142. DOI: https://doi.org/10.1016/j.ijcard.2004.12.004
Patel AB, Tu JV, Waters NM, Ko DT, Eisenberg MJ, Huynh T, Rinfret S, Knudtson ML, Ghali WA, 2010. Access to primary percutaneous coronary intervention for ST-Segment elevation myocardial infarction in Canada: a geographic analysis. Open Med J 4:e13-21.
Ripley B.D,1977. Modelling spatial patterns. Journal of the Royal Statistical Society: J R Stat Soc Series B Stat Methodol 39:172–92. DOI: https://doi.org/10.1111/j.2517-6161.1977.tb01615.x
Swaminathan RV, Rao SV, McCoy LA, Kim LK, Minutello RM, Wong SC, Yang DC, Saha-Chaudhuri P, Singh HS, Bergman G, 2015. Hospital length of stay and clinical outcomes in older STEMI patients after primary PCI. J Am Coll Cardiol 65:1161–71. DOI: https://doi.org/10.1016/j.jacc.2015.01.028
Timmis A, Townsend N, Gale C, Grobbee R, Maniadakis N, Flather M, Wilkins E, Wright L, Vos R, Bax J, 2018. European society of cardiology: cardiovascular disease statistics 2017. Eur Heart J 39:508–79.
Tsatsaris A, Kalogeropoulos K, Stathopoulos N, 2023. Geospatial Technology, Spatial Epidemiology & Public Health. In: GeoInformatics for Geosciences Advanced Geospatial Analysis using RS, GIS & Soft Computing (eds. Nikolaos Stathopoulos, Andreas Tsatsaris & Kleomenis Kalogeropoulos). Elsevier. Paperback ISBN: 9780323989831. DOI: 10.1016/B978-0-323-98983-1.00002-8. 3-29. DOI: https://doi.org/10.1016/B978-0-323-98983-1.00002-8
Tong TYN, Appleby PN, Bradbury KE, Perez-Cornago A, Travis RC, Clarke R, Key TJ, 2019. Risks of ischaemic heart disease and stroke in meat eaters, fish eaters, and vegetarians over 18 years of follow-up: results from the prospective EPIC-Oxford Study. BMJ 366:l4897. DOI: https://doi.org/10.1136/bmj.l4897
WHO, 2020. Global Health Estimates. The top 10 causes of death. Available from: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death

How to Cite

Melidoniotis, E., Kalogeropoulos, K., Tsatsaris, A., Zografakis-Sfakianakis, M., Lazopoulos, G., Tzanakis, N., Anastasiou, I., & Skalidis, E. (2024). Geospatial epidemiology of coronary artery disease treated with percutaneous coronary intervention in Crete, Greece. Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1251