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01-08-2014 | Original Research Article

Identification of Suicide-Related Events Through Network Analysis of Adverse Event Reports

Authors: Alwis Nazir, Takashi Ichinomiya, Nobuteru Miyamura, Yasuaki Sekiya, Yasutomi Kinosada

Published in: Drug Safety | Issue 8/2014

Abstract

Background

In the treatment of depression, it is essential to monitor for early warnings of suicide.

Objective

The aim of this study was to identify the symptoms that would suggest a high suicide risk by analyzing data obtained from the US Food and Drug Administration Adverse Event Reporting System (FAERS) of selective serotonin reuptake inhibitors.

Methods

Using FAERS reports from 1997 to the second quarter of 2012, we constructed the co-occurrence network of adverse events. From this network, we extracted the events that were strongly connected to suicidal events (suicidal attempts, suicidal ideation, suicidal behavior, and complete suicide) by means of the community detection method.

Results

We succeeded in obtaining a list of suicide-related adverse events. Owing to the randomness inherent in the algorithms of community detection, we found that the obtained list differed according to each trial of analysis. However, the lists we derived show considerable efficiency in identifying suicidal events.

Conclusion

The network analysis appears to be a promising method for identifying signals of suicide.

MedDRA^®, the Medical Dictionary for Regulatory Activities terminology, is the international medical terminology developed under the auspices of the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). The MedDRA^® trademark is owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) on behalf of ICH.

Harris EC, Barraclough B. Suicide as an outcome for mental disorders. A meta-analysis. Br J Psychiatry. 1997;170:205–28.PubMedCrossRef

Mann JJ, Waternaux C, Haas GL, Malone KM. Toward a clinical model of suicidal behavior in psychiatric patients. Am J Psychiatry. 1999;156:181–9.PubMed

Reihmer Z. Suicide and bipolar disorder. In: Zarate CA, Manji HK, editors. Bipolar depression: molecular neurobiology, clinical diagnosis and pharmacotherapy. Basel: Birkhäuser; 2009. p. 47–56.

Vaswani MK, Linda FK, Ramesh S. Role of selective serotonin reuptake inhibitors in psychiatric disorders: a comprehensive review. Prog Neuropsychopharmacol Biol Psychiatry. 2003;27:85–102.PubMedCrossRef

Szarfman A, Machado SG, O’Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s spontaneous reports database. Drug Saf. 2002;25:381–92.PubMedCrossRef

Hauben M, Madigan D, Gerrits CM, Walsh L, Van Puijenbroek EP. The role of data mining in pharmacovigilance. Expert Opin Drug Saf. 2005;4:929–48.PubMedCrossRef

Piccinni C, Motola D, Marchesini G, Poluzzi E. Assessing the association of pioglitazone use and bladder cancer through drug adverse event reporting. Diabetes Care. 2011;34:1369–71.PubMedCentralPubMedCrossRef

Gunguly N, Deutsh A, Mukherjee A, editors. Dynamics on and of complex networks: application to biology, computer science, and the social sciences. Basel: Birkhäuser; 2009.

Liljeros F, Edling CR, Amaral LAN, Stanley HE, Aberg Y. The web of human sexual contacts. Nature. 2001;411:907–8.PubMedCrossRef

10.

Pastor-Satorras R, Vespignani A. Immunization of complex networks. Phys Rev E. 2002;65:036104.CrossRef

11.

Alon U. An introduction to systems biology: design principles of biological circuits. London: Chapman and Hall; 2006.

12.

Barabási AL. Network medicine: from obesity to the “diseasome”. N Engl J Med. 2007;357:404–7.PubMedCrossRef

13.

DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting systems. Am Stat. 1999;53:177–90.

14.

Harpaz R, Chase HS, Friedman C. Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinform. 2010;11(Suppl. 9):S7.CrossRef

15.

Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci USA 2006;103:8577–8582.

16.

American Society of Health-System Pharmacists. AHFS drug information 2010. Maryland: American Society of Health-System Pharmacists; 2010.

17.

Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36:D901–6.PubMedCentralPubMedCrossRef

18.

Newman MEJ. Finding and evaluating community structure in networks. Phys Rev E. 2004;69:026113.CrossRef

19.

Guimerà R, Sales-Pardo M, Amaral LAN. Modularity from fluctuations in random graphs and complex networks. Phys Rev E. 2004;70:R025101.CrossRef

20.

Rosvall M, Bergstrom CT. An information-theoretic framework for resolving community structure in complex network. Proc Natl Acad Sci USA. 2007;104:7327–31.PubMedCentralPubMedCrossRef

21.

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech. 2008;2008:P10008.

22.

Fortunato S, Barthélemy M. Resolution limit in community detection. Proc Natl Acad Sci USA. 2007;104:36–41.PubMedCentralPubMedCrossRef

23.

Lambiotte R, Delvenne J -C, Barahona M. Laplacian dynamics and multiscale modular structure in networks [internet]. 2008. arXiv:0812.1770.

24.

Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. In: International AAAI conference on weblogs and social media; 17–20 May 2009, San Jose, USA. http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/154. Accessed 23 Mar 2014.

25.

Huang L, Zalkikar J, Tiwari RC. A likelihood ratio test based method for signal detection with application to FDA’s drug safety data. J Am Stat Assoc. 2011;106:1230–41.CrossRef

26.

Huang L, Guo T, Zalkikar JN, Tiwari RC. A review of statistical methods for safety surveillance. Ther Innov Regul Sci. 2014;48:98–108.

Title: Identification of Suicide-Related Events Through Network Analysis of Adverse Event Reports
Authors: Alwis Nazir
Takashi Ichinomiya
Nobuteru Miyamura
Yasuaki Sekiya
Yasutomi Kinosada
Publication date: 01-08-2014
Publisher: Springer International Publishing
Published in: Drug Safety / Issue 8/2014
Print ISSN: 0114-5916
Electronic ISSN: 1179-1942
DOI: https://doi.org/10.1007/s40264-014-0195-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Identification of Suicide-Related Events Through Network Analysis of Adverse Event Reports

Abstract

Background

Objective

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Objective

Methods

Results

Conclusion

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