Population-based study of comorbidities in unruptured brain aneurysms using complex network analysis
https://doi.org/10.18699/SSMJ20240519
Abstract
Complex network analysis is a relatively new method of analysis in medical research. It can be utilized in population- based study of different comorbidities. The aim of this study was to estimate the effectiveness of complex network methodology in analysis comorbidities in unruptured brain aneurysms patients. Material and methods. A comprehensive network analysis of a sample of 628,831 individuals was performed, after constructing bipartite networks all the connections between index diagnosis and revealed comorbidities were statistically validated. Results and discussion. Altogether, 1787 patients with unruptured aneurysm were identified (ICD code I67.1). After complex network analysis bipartite networks were established based on index diagnosis, there were 182 codes of comorbidities. Of those, 150 from 182 codes (82 %) were found in people aged from 40 to 70 years, men had 67 (37 %) codes and women had 115 (63 %). In addition to traditional discirculatory and heart diseases, analysis elucidated previously scarcely described comorbidities including chronic obstructive pulmonary disease in non-smokers women older than 60 years. Conclusion. Demonstrated data shows the effectiveness of network complex analysis in population-based research of comorbidities in unruptured aneurysm patients.
About the Authors
Ju. V. Kivelev
University Clinic of Neurosurgery ; JSC “European Medical Center”
Finland
Finland
Juri V. Kivelev, candidate of medical sciences
20540, Turku University Hospital, Hämeentie st., 11
129090, Moscow, Shchepkina st., 35
A. V. Dubovoy
Ilyinskaya Hospital JSC, Head and Neck Surgery Center
Russian Federation
Russian Federation
Andrei V. Dubovoy, candidate of medical sciences
143421, Moscow region, Krasnogorsk, Rublevskoe suburb., 2
A. L. Krivoshapkin
JSC “European Medical Center” ; Meshalkin National Medical Research Center of Minzdrav of Russia ; Peoples’ Friendship University of Russia
Russian Federation
Russian Federation
Alexey L. Krivoshapkin, doctor of medical sciences, professor
129090, Moscow, Shchepkina st., 35
630055, Novosibirsk, Reshkunovskaya st., 15
117198, Moscow, Miklukho-Maklaya st., 6
References
1. West G.B. The importance of quantitative systemic thinking in medicine. Lancet (London, England). 2012;379(9825):1551–1559. doi: 10.1016/S0140-6736(12)60281-5
2. Barabási A.L. Network medicine – from obesity to the “diseasome”. N. Engl. J. Med. 2007;357(4):404– 407. doi: 10.1056/NEJMe078114
3. Goh K.I., Choi I.G. Exploring the human diseasome: the human disease network. Brief. Funct. Genomics. 2012;11(6):533–542. doi: 10.1093/bfgp/els032
4. Jiang Y., Ma S., Shia B.C., Lee T.S. An epidemiological human disease network derived from disease co-occurrence in Taiwan. Sci. Rep. 2018;8(1):4557. doi: 10.1038/s41598-018-21779-y
5. Goh K.I., Cusick M.E., Valle D., Childs B., Vidal M., Barabási A.L. The human disease network. Proc. Natl. Acad. Sci. USA. 2007;104(21):8685–8690. doi: 10.1073/pnas.0701361104
6. Hidalgo C.A., Blumm N., Barabási A.L., Christakis N.A. A dynamic network approach for the study of human phenotypes. PLoS Comput. Biol. 2009;5(4):e1000353. doi: 10.1371/journal.pcbi.1000353
7. McKinsey & Co. How healthcare systems can become digital-health leaders. Available at: https://rhapsody.health/blog/mckinsey-co-how-healthcare-systems-can-become-digital-health-leaders/
8. Kivelev Yu.V., Saarenpää I., Krivoshapkin A.L. Establishing of big data clinical dataset in brain vessel aneurysm research. Sibirskij nauchnyj medicinskij zhurnal = Siberian Scientific Medical Journal. 2023;43(3):86–94. doi: 10.18699/SSMJ20230311
9. Faggella D. Where healthcare’s big data actually comes from. Available at: https://emerj.com/ai-sector-overviews/where-healthcares-big-data-actually-comes-from/
10. Laukka D., Kivelev J., Rahi M., Vahlberg T., Paturi J., Rinne J., Hirvonen J. Detection rates and trends of asymptomatic unruptured intracranial aneurysms from 2005 to 2019. Neurosurgery. 2024;94(2):297– 306. doi: 10.1227/neu.0000000000002664
11. Tumminello M., Miccichè S., Lillo F., Piilo J., Mantegna R.N. Statistically validated networks in bipartite complex systems. PLoS One. 2011;6(3):e17994. doi: 10.1371/journal.pone.0017994
12. Lee J.S., Park I.S., Park K.B., Kang D.H., Lee C.H., Hwang S.H. Familial intracranial aneurysms. J. Korean Neurosurg. Soc. 2008;44(3):136–140. doi: 10.3340/jkns.2008.44.3.136
13. Bor A.S., Koffijberg H., Wermer M.J., Rinkel G.J. Optimal screening strategy for familial intracranial aneurysms: a cost-effectiveness analysis. Neurology. 2010;74(21):1671–1679. doi: 10.1212/WNL.0b013e3181e04297
14. Vlak M.H., Algra A., Brandenburg R., Rinkel G.J. Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: a systematic review and meta-analysis. Lancet Neurol. 2011;10(7):626–636. doi: 10.1016/S1474-4422(11)70109-0
15. Brown R.D. Jr., Broderick J.P. Unruptured intracranial aneurysms: epidemiology, natural history, management options, and familial screening. Lancet Neurol. 2014;13(4):393–404. doi: 10.1016/S1474-4422(14)70015-8
16. Cras T.Y., Bos D., Ikram M.A., Vergouwen M.D.I., Dippel D.W.J., Voortman T., Adams H.H.H., Vernooij M.W., Roozenbeek B. Determinants of the presence and size of intracranial aneurysms in the general population: the Rotterdam study. Stroke. 2020;51(7):2103–2110. doi: 10.1161/STROKEAHA.120.029296
17. Haemmerli J., Morel S., Georges M., Haidar F., Chebib F.T., Morita A., Nozaki K., Tominaga T., Bervitskiy A.V., Rzaev J., Schaller K., Bijlenga P. Characteristics and distribution of intracranial aneurysms in patients with autosomal dominant polycystic Kidney disease compared with the general population: A meta-analysis. Kidney360. 2023;4(4):e466–e475. doi: 10.34067/KID.0000000000000092
18. Beyer M.A., Laney D. The importance of “big data”: a definition. Stamford CT: Gartner; 2012.
19. Lawton M.T., Vates G.E. Subarachnoid hemorrhage. N. Engl. J. Med. 2017;377(3):257–266. doi: 10.1056/NEJMcp1605827
20. Nieuwkamp D.J., Setz L.E., Algra A., Linn F.H., de Rooij N.K., Rinkel G.J. Changes in case fatality of aneurysmal subarachnoid haemorrhage over time, according to age, sex, and region: a meta-analysis. Lan-cet Neurol. 2009;8(7):635–642. doi: 10.1016/S1474-4422(09)70126-7
21. Lall R.R., Eddleman C.S., Bendok B.R., Batjer H.H. Unruptured intracranial aneurysms and the assessment of rupture risk based on anatomical and morphological factors: sifting through the sands of data. Neurosurg. Focus. 2009;26(5):E2. doi: 10.3171/2009.2.FOCUS0921
22. Malhotra A., Wu X., Forman H.P., Matouk C.C., Gandhi D., Sanelli P. Management of tiny unruptured intracranial aneurysms: A comparative effectiveness analysis. JAMA Neurol. 2018;75(1):27–34. doi: 10.1001/jamaneurol.2017.3232
23. Pettersson S.D., Khorasanizadeh M., Maglinger B., Garcia A., Wang S.J., Taussky P., Ogilvy C.S. Trends in the age of patients treated for unruptured intracranial aneurysms from 1990 to 2020. World Neurosurg. 2023;178:233–240.e13. doi: 10.1016/j.wneu.2023.08.007
24. Jung K.H. New pathophysiological considerations on cerebral aneurysms. Neurointervention. 2018;13(2):73–83. doi: 10.5469/neuroint.2018.01011
25. Lahousse L., Tiemeier H., Ikram M.A., Brusselle G.G. Chronic obstructive pulmonary disease and cerebrovascular disease: A comprehensive review. Respir. Med. 2015;109(11):1371–1380. doi: 10.1016/j.rmed.2015.07.014
Review
For citations:
Kivelev J.V., Dubovoy A.V., Krivoshapkin A.L. Population-based study of comorbidities in unruptured brain aneurysms using complex network analysis. Сибирский научный медицинский журнал. 2024;44(5):163-171. (In Russ.) https://doi.org/10.18699/SSMJ20240519
Views:
792
Email this article 