Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
General Research Article

Causality Assessment of Adverse Drug Reaction: Controlling Confounding Induced by Polypharmacy

Author(s): Tran-Thai Dang*, Thanh-Hang Nguyen and Tu-Bao Ho

Volume 25, Issue 10, 2019

Page: [1134 - 1143] Pages: 10

DOI: 10.2174/1381612825666190416115714

Price: $65

Abstract

Background: Post-marketing pharmaceutical surveillance, a.k.a. pragmatic clinical trials (i.e., PCT), plays a vital role in preventing accidents in practical treatment. The most important and difficult task in PCT is to assess which drug causes adverse reactions (i.e., ADRs) from clinical texts. The confounding (i.e., factors cause confusions in causality assessment) is generated by the polypharmacy (i.e., multiple drugs use), which makes most of existing methods poor for detecting drugs that capably cause observed ADRs.

Objective: We aim to improve the performance of detecting drug-ADR causal relations from clinical texts. To this end, a mechanism for reducing the impact of confounding on the detecting process is needful.

Methods: We proposed a novel model which is called the analogy-based active voting (i.e., AAV) for improving the ability of detecting causal drug-ADR pairs, in case multiple drugs are prescribed for treating the comorbidity. This model is inspired by the analogy principle which was proposed by Bradford Hill.

Results: The experimental results show the improvement of recognizing causal relations between drugs and ADRs that are confirmed by the SIDER. In addition, the proposed model is promising to detect infrequently observed causal drug-ADR pairs when the drug is not commonly used.

Conclusion: The proposed model demonstrates its ability for controlling the polypharmacy-induced confounding, to improve the quality of causality assessment of ADRs. Additionally, this also shows that the analogy principle is applicable for the assessment.

Keywords: Pragmatic clinical trials, causality assessment, polypharmacy, Bradford Hill criteria, diversity, adverse reactions.

« Previous
[1]
Coloma PM, Trifirò G, Patadia V, Sturkenboom M. Postmarketing safety surveillance: where does signal detection using electronic healthcare records fit into the big picture? Drug Saf 2013; 36(3): 183-97.
[http://dx.doi.org/10.1007/s40264-013-0018-x] [PMID: 23377696]
[2]
Ho TB, Le L, Thai DT, Taewijit S. Data-driven approach to detect and predict adverse drug reactions. Curr Pharm Des 2016; 22(23): 3498-526.
[http://dx.doi.org/10.2174/1381612822666160509125047] [PMID: 27157416]
[3]
Sergi G, De Rui M, Sarti S, Manzato E. Polypharmacy in the elderly: can comprehensive geriatric assessment reduce inappropriate medication use? Drugs Aging 2011; 28(7): 509-18.
[http://dx.doi.org/10.2165/11592010-000000000-00000] [PMID: 21721596]
[4]
Lu ML, Lane HY, Lin SK, Chen KP, Chang WH. Adjunctive fluvoxamine inhibits clozapine-related weight gain and metabolic disturbances. J Clin Psychiatry 2004; 65(6): 766-71.
[http://dx.doi.org/10.4088/JCP.v65n0607] [PMID: 15291653]
[5]
Huang H, Zhang P, Qu XA, Sanseau P, Yang L. Systematic prediction of drug combinations based on clinical side-effects. Sci Rep 2014; 4: 7160.
[http://dx.doi.org/10.1038/srep07160] [PMID: 25418113]
[6]
Shah BM, Hajjar ER. Polypharmacy, adverse drug reactions, and geriatric syndromes. Clin Geriatr Med 2012; 28(2): 173-86.
[http://dx.doi.org/10.1016/j.cger.2012.01.002] [PMID: 22500537]
[7]
Hajjar ER, Cafiero AC, Hanlon JT. Polypharmacy in elderly patients. Am J Geriatr Pharmacother 2007; 5(4): 345-51.
[http://dx.doi.org/10.1016/j.amjopharm.2007.12.002] [PMID: 18179993]
[8]
Maher RL, Hanlon J, Hajjar ER. Clinical consequences of polypharmacy in elderly. Expert Opin Drug Saf 2014; 13(1): 57-65.
[http://dx.doi.org/10.1517/14740338.2013.827660] [PMID: 24073682]
[9]
Hanlon JT, Sloane RJ, Pieper CF, Schmader KE. Association of adverse drug reactions with drug-drug and drug-disease interactions in frail older outpatients. Age Ageing 2011; 40(2): 274-7.
[http://dx.doi.org/10.1093/ageing/afq158] [PMID: 21177281]
[10]
Lu ML, Lane HY, Chen KP, Jann MW, Su MH, Chang WH. Fluvoxamine reduces the clozapine dosage needed in refractory schizophrenic patients. J Clin Psychiatry 2000; 61(8): 594-9.
[http://dx.doi.org/10.4088/JCP.v61n0809] [PMID: 10982203]
[11]
Lin CH, Kuo CC, Chou LS, et al. A randomized, double-blind comparison of risperidone versus low-dose risperidone plus low-dose haloperidol in treating schizophrenia. J Clin Psychopharmacol 2010; 30(5): 518-25.
[http://dx.doi.org/10.1097/JCP.0b013e3181f28dff] [PMID: 20814315]
[12]
Lin CH, Wang FC, Lin SC, Huang YH, Chen CC, Lane HY. Antipsychotic combination using low-dose antipsychotics is as efficacious and safe as, but cheaper, than optimal-dose monotherapy in the treatment of schizophrenia: a randomized, double-blind study. Int Clin Psychopharmacol 2013; 28(5): 267-74.
[http://dx.doi.org/10.1097/YIC.0b013e3283633a83] [PMID: 23778382]
[13]
Bohnert ASB, Walton MA, Cunningham RM, et al. Overdose and adverse drug event experiences among adult patients in the emergency department. Addict Behav 2018; 86: 66-72.
[http://dx.doi.org/10.1016/j.addbeh.2017.11.030] [PMID: 29198490]
[14]
Agbabiaka TB, Savović J, Ernst E. Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf 2008; 31(1): 21-37.
[http://dx.doi.org/10.2165/00002018-200831010-00003] [PMID: 18095744]
[15]
Arimone Y, Bégaud B, Miremont-Salamé G, et al. A new method for assessing drug causation provided agreement with experts’ judgment. J Clin Epidemiol 2006; 59(3): 308-14.
[http://dx.doi.org/10.1016/j.jclinepi.2005.08.012] [PMID: 16488362]
[16]
Reps J, Garibaldi JM, Aickelin U, Soria D, Gibson JE, Hubbard RB. Attributes for causal inference in electronic healthcare databases. Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on IEEE 2013; 548-9.
[http://dx.doi.org/10.1109/CBMS.2013.6627871]
[17]
Kosslyn SM, Kerrey B. Building the Intentional University: Minerva and the Future of Higher Education 2017.
[http://dx.doi.org/10.7551/mitpress/9780262037150.001.0001] ]
[18]
Doherty MJ. Algorithms for assessing the probability of an adverse drug reaction. Respir Med Case Rep 2009; 2(2): 63-7.
[http://dx.doi.org/[https://doi.org/10.1016/j.rmedc.2009.01.004]
[19]
Naranjo CA, Busto U, Sellers EM, et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther 1981; 30(2): 239-45.
[http://dx.doi.org/10.1038/clpt.1981.154] [PMID: 7249508]
[20]
Koh Y, Yap CW, Li SC. A quantitative approach of using genetic algorithm in designing a probability scoring system of an adverse drug reaction assessment system. Int J Med Inform 2008; 77(6): 421-30.
[http://dx.doi.org/10.1016/j.ijmedinf.2007.08.010] [PMID: 17921048]
[21]
Reps J, Garibaldi JM, Aickelin U, Soria D, Gibson JE, Hubbard RB. Comparing data-mining algorithms developed for longitudinal observational databases. Computational Intelligence (UKCI), 2012 12th UK Workshop on IEEE 2012; 1-8. [http://dx.doi.org/10.1109/UKCI.2012.6335771]
[22]
Liu J, Li A, Seneff S. Automatic drug side effect discovery from online patient-submitted reviews:Focus on statin drugs. Proc 1st Int Conf Adv Inform Mining Manag (IMMM) Barcelona, Spain. Citeseer 2011; pp. 23-9.
[23]
Chen ES, Hripcsak G, Xu H, Markatou M, Friedman C. Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 2008; 15(1): 87-98.
[http://dx.doi.org/10.1197/jamia.M2401] [PMID: 17947625]
[24]
Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009; 16(3): 328-37.
[http://dx.doi.org/10.1197/jamia.M3028] [PMID: 19261932]
[25]
Zorych I, Madigan D, Ryan P, Bate A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res 2013; 22(1): 39-56.
[http://dx.doi.org/10.1177/0962280211403602] [PMID: 21878461]
[26]
Ji Y, Ying H, Dews P, et al. A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance. IEEE Trans Inf Technol Biomed 2011; 15(3): 428-37.
[http://dx.doi.org/10.1109/TITB.2011.2131669] [PMID: 21435986]
[27]
Ji Y, Ying H, Dews P, et al. An exclusive causal-leverage measure for detecting adverse drug reactions from electronic medical records. uzzy Information Processing Society (NAFIPS), 2011 Annual Meeting of the North American 2011 In: IEEE. 2011; pp. 1-6.
[http://dx.doi.org/10.1109/NAFIPS.2011.5751957]
[28]
Jin H, Chen J, He H, Kelman C, McAullay D, O’Keefe CM. Signaling potential adverse drug reactions from administrative health databases. IEEE Trans Knowl Data Eng 2010; 22(6): 839-53.
[http://dx.doi.org/10.1109/TKDE.2009.212]
[29]
Harpaz R, Haerian K, Chase HS, Friedman C. Statistical mining of potential drug interaction adverse effects in FDA’s spontaneous reporting system. AMIA Annual Symposium Proceedings 2010; 2010: 281-5.
[PMID: 21346985]
[30]
Norén GN, Hopstadius J, Bate A, Star K, Edwards IR. Temporal pattern discovery in longitudinal electronic patient records. Data Min Knowl Discov 2010; 20(3): 361-87.
[http://dx.doi.org/10.1007/s10618-009-0152-3]
[31]
Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54(4): 315-21.
[http://dx.doi.org/10.1007/s002280050466] [PMID: 9696956]
[32]
Rodrigues PP, Ferreira-Santos D, Silva A, Polónia J, Ribeiro-Vaz I. Implementing guidelines for causality assessment of adverse drug reaction reports: A bayesian network approach. Conference on Artificial Intelligence in Medicine in Europe 55-64 2017.;
[http://dx.doi.org/10.1007/978-3-319-59758-4_6]
[33]
Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 2018; 34(13): i457-66.
[http://dx.doi.org/10.1093/bioinformatics/bty294] [PMID: 29949996]
[34]
Bansal M, Yang J, Karan C, et al. A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol 2014; 32(12): 1213-22.
[http://dx.doi.org/10.1038/nbt.3052] [PMID: 25419740]
[35]
Zhang W, Yue X, Lin W, et al. Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinformatics 2018; 19(1): 233.
[http://dx.doi.org/10.1186/s12859-018-2220-4] [PMID: 29914348]
[36]
Ding Y, Tang J, Guo F. Identification of Drug-side effect association via semi-supervised model and multiple kernel learning. IEEE J Biomed Health Inform 2018.
[http://dx.doi.org/10.1109/JBHI.2018.2883834] [PMID: 30507518]
[37]
Reps JM, Aickelin U. Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data 2014.
[http://dx.doi.org/10.1109/ICDMW.2014.54] ]
[38]
Reps JM, Garibaldi JM, Aickelin U, Gibson JE, Hubbard RB. A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations. J Biomed Inform 2015; 56: 356-68.
[http://dx.doi.org/10.1016/j.jbi.2015.06.011] [PMID: 26116429]
[39]
Johnson AE, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3160035.
[http://dx.doi.org/10.1038/sdata.2016.35] [PMID: 27219127]
[40]
Dang TT, Ho TB. Mixture of language models utilization in score-based sentiment classification on clinical narratives. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems 255-68 2016.
[http://dx.doi.org/10.1007/978-3-319-42007-3_22]
[41]
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. Adv Neural Inform Process Syst 2013; 3111-9.
[42]
Aronson AR. Effective mapping of biomedical text to the umls metathesaurus: the metamap program. Proc AMIA Symp 2001; 17-21.
[PMID: 11825149]
[43]
Aronson AR, Lang FM. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 2010; 17(3): 229-36.
[http://dx.doi.org/10.1136/jamia.2009.002733] [PMID: 20442139]
[44]
Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res 2016; 44(D1): D1075-9.
[http://dx.doi.org/10.1093/nar/gkv1075] [PMID: 26481350]

Rights & Permissions Print Cite
Article Metrics
22
5
© 2024 Bentham Science Publishers | Privacy Policy