Increasing the Effectiveness of Pharmacotherapy in Psychiatry by Using a Pharmacological Interaction Database
Abstract
:1. Introduction
2. Methods
2.1. Studied Group and Procedure
2.2. Statistical Analysis
3. Results
3.1. Characteristics of the Studied Group of Psychiatrists
3.2. Effectiveness of the Pharmacological Interaction Database
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- Europe, U = 17,906; p < 0.001
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- South America, U = 1448.5; p < 0.001
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- North America, U = 3805.5; p < 0.001
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- Australia, U = 104; p < 0.001
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- Africa, U = 302; p < 0.001
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- Asia, U = 5059; p < 0.001
3.3. Interest in the Pharmacological Interaction Database
4. Discussion
5. Conclusions
6. Limitations
7. Case Reports
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Malhi, G.S.; Mann, J.J. Depression. Lancet 2018, 392, 2299–2312. [Google Scholar] [CrossRef]
- OECD; European Union. Promoting mental health in Europe: Why and how? In Health at a Glance: Europe 2018: State of Health in the EU Cycle; Revised Version, February 2019; OECD Publishing: Paris, France, 2018. [Google Scholar] [CrossRef]
- Patel, V.; Saxena, S.; Lund, C.; Thornicroft, G.; Baingana, F.; Bolton, P.; Chisholm, D.; Collins, P.Y.; Cooper, J.L.; Eaton, J.; et al. The Lancet Commission on global mental health and sustainable development. Lancet 2018, 392, 1553–1598. [Google Scholar] [CrossRef] [Green Version]
- Peacock, A.; Bruno, R.; Gisev, N.; Degenhardt, L.; Hall, W.; Sedefov, R.; White, J.; Thomas, K.V.; Farrell, M.; Griffiths, P. New psychoactive substances: Challenges for drug surveillance, control, and public health responses. Lancet 2019, 394, 1668–1684. [Google Scholar] [CrossRef]
- Schifano, F.; Orsolini, L.; Papanti, G.D.; Corkery, J.M. Novel psychoactive substances of interest for psychiatry. World Psychiatry 2015, 14, 15–26. [Google Scholar] [CrossRef] [Green Version]
- Van Buskirk, J.; Griffiths, P.; Farrell, M.; Degenhardt, L. Trends in new psychoactive substances from surface and “dark” net monitoring. Lancet Psychiatry 2017, 4, 16–18. [Google Scholar] [CrossRef]
- Ordak, M.; Nasierowski, T.; Muszynska, E. The problem of poly-pharmacotherapy in patients on a mephedrone binge. Pharmacol. Res. 2019, 143, 204. [Google Scholar] [CrossRef]
- Ordak, M.; Nasierowski, T.; Muszynska, E.; Bujalska-Zadrozny, M. Optimisation of methadone treatment in a group of patients on a mephedrone binge and dependent on many psychoactive substances. Int. J. Psychiatry Clin. Pract. 2019, 24, 38–42. [Google Scholar] [CrossRef] [PubMed]
- Maher, R.L.; Hanlon, J.; Hajjar, E.R. Clinical consequences of polypharmacy in elderly. Expert Opin. Drug Saf. 2014, 13, 57–65. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Scott, I.A.; Hilmer, S.N.; Reeve, E.; Potter, K.; Le Couteur, D.; Rigby, D.; Gnjidic, D.; Del Mar, C.N.; Roughead, E.E.; Page, A.; et al. Reducing inappropriate polypharmacy: The process of deprescribing. JAMA Intern. Med. 2015, 175, 827–834. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hill-Taylor, B.; Walsh, K.A.; Stewart, S.; Hayden, J.; Byrne, S.; Sketris, I.S. Effectiveness of the STOPP/START (Screening Tool of Older Persons’ potentially inappropriate Prescriptions/Screening Tool to Alert doctors to the Right Treatment) criteria: Systematic review and meta-analysis of randomized controlled studies. J. Clin. Pharm. Ther. 2016, 41, 158–169. [Google Scholar] [CrossRef] [PubMed]
- Taylor, S.R.; Jones, J.B.; Shah, N.R. Contracting for compliance: Using adherence as a patient-centered measure of performance. Am. Health Drug Benefits 2008, 1, 6–8. [Google Scholar]
- Veeren, J.C.; Weiss, M. Trends in emergency hospital admissions in England due to adverse drug reactions: 2008–2015. J. Pharm. Health Serv. Res. 2016, 8, 5–11. [Google Scholar] [CrossRef]
- Hartholt, K.; Van Der Velde, N.; Looman, C.; Panneman, M.; Van Beeck, E.; Patka, P.; Van Der Cammen, T. Adverse Drug Reactions Related Hospital Admissions in Persons Aged 60 Years and over, The Netherlands, 1981–2007: Less Rapid Increase, Different Drugs. PLoS ONE 2010, 5, e13977. [Google Scholar] [CrossRef] [Green Version]
- Wawruch, M.; Zikavska, M.; Wsolova, L.; Kuzelova, M.; Tisonova, J.; Gajdosik, J.; Urbanek, K.; Kristova, V. Polypharmacy in elderly hospitalized patients in Slovakia. Pharm. World Sci. 2008, 30, 235–242. [Google Scholar] [CrossRef] [PubMed]
- Corsonello, A.; Pedone, C.; Corica, F.; Incalzi, R.A. Polypharmacy in elderly patients at discharge from the acute care hospital. Ther. Clin. Risk Manag. 2007, 3, 197–203. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mizokami, F.; Koide, Y.; Noro, T.; Furuta, K. Polypharmacy with Common Diseases in Hospitalized Elderly Patients. Am. J. Geriatr. Pharmacother. 2012, 10, 123–128. [Google Scholar] [CrossRef] [PubMed]
- Wilfling, D.; Hinz, A.; Steinhäuser, J. Big data analysis techniques to address polypharmacy in patients—A scoping review. BMC Fam. Pract. 2020, 21, 180. [Google Scholar] [CrossRef]
- Błeszyńska, E.; Wierucki, Ł.; Zdrojewski, T.; Renke, M. Pharmacological Interactions in the Elderly. Medicina 2020, 56, 320. [Google Scholar] [CrossRef] [PubMed]
- Ordak, M.; Nasierowski, T. The pharmacological basis of drug interactions: An aspect overlooked in psychiatry. Lancet Psychiatry 2019, 6, 984. [Google Scholar] [CrossRef] [Green Version]
- Medscape: Drug Interaction Checker. 2020. Available online: https://reference.medscape.com/drug-interactionchecker (accessed on 8 December 2020).
- Day, R.O.; Snowden, L. Where to find information about drugs. Aust. Prescr. 2016, 39, 88–95. [Google Scholar] [CrossRef] [Green Version]
- Carmona-Huerta, J.; Obeso, S.C.-D.; Ramírez-Palomino, J.; Duran-Gutiérrez, R.; Cardona-Muller, D.; Grover-Paez, F.; Fernández-Dorantes, P.; Medina-Dávalos, R. Polypharmacy in a hospitalized psychiatric population: Risk estimation and damage quantification. BMC Psychiatry 2019, 19, 78. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Morandi, A.; Bellelli, G.; Vasilevskis, E.E.; Turco, R.; Guerini, F.; Torpilliesi, T.; Speciale, S.; Emiliani, V.; Gentile, S.; Schnelle, J.; et al. Predictors of Rehospitalization Among Elderly Patients Admitted to a Rehabilitation Hospital: The Role of Polypharmacy, Functional Status, and Length of Stay. J. Am. Med. Dir. Assoc. 2013, 14, 761–767. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sturmberg, J.P.; Bircher, J. Better and fulfilling healthcare at lower costs: The need to manage health systems as complex adaptive systems. F1000Research 2019, 8, 789. [Google Scholar] [CrossRef] [Green Version]
- Sanders, J.M.; Monogue, M.L.; Jodlowski, T.Z.; Cutrell, J.B. Pharmacologic Treatments for Coronavirus Disease 2019 (COVID-19). JAMA 2020, 323, 1824–1836. [Google Scholar] [CrossRef]
- Ocaña-Zurita, M.C.; Juárez-Rojop, I.E.; Genis, A.; Tovilla-Zárate, C.A.; González-Castro, T.B.; López-Narváez, M.L.; De La O De La O, M.E.; Nicolini, H. Potential drug–drug interaction in Mexican patients with schizophrenia. Int. J. Psychiatry Clin. Pract. 2016, 20, 249–253. [Google Scholar] [CrossRef]
- Patel, T.K.; Bhabhor, P.H.; Desai, N.; Shah, S.; Patel, P.B.; Vatsala, E.; Panigrahi, S. Adverse drug reactions in a psychiatric department of tertiary care teaching hospital in India: Analysis of spontaneously reported cases. Asian J. Psychiatry 2015, 17, 42–49. [Google Scholar] [CrossRef]
- Gustafsson, L.L.; Wettermark, B.; Godman, B.; Andersén-Karlsson, E.; Bergman, U.; Hasselström, J.; Hensjö, L.-O.; Hjemdahl, P.; Jägre, I.; Julander, M.; et al. The ‘Wise List’—A Comprehensive Concept to Select, Communicate and Achieve Adherence to Recommendations of Essential Drugs in Ambulatory Care in Stockholm. Basic Clin. Pharmacol. Toxicol. 2011, 108, 224–233. [Google Scholar] [CrossRef]
- Brinkman, D.; Tichelaar, J.; Okorie, M.; Bissell, L.; Christiaens, T.; Likic, R.; Mačìulaitis, R.; Costa, J.; Sanz, E.; Tamba, B.; et al. Pharmacology and Therapeutics Education in the European Union Needs Harmonization and Modernization: A Cross-sectional Survey Among 185 Medical Schools in 27 Countries. Clin. Pharmacol. Ther. 2017, 102, 815–822. [Google Scholar] [CrossRef]
- Drugs.com: Drugs Interaction Checker 2021. Available online: https://www.drugs.com/drug_interactions.html (accessed on 4 May 2021).
- DrugBank: Drug Interaction Checker 2021. Available online: https://go.drugbank.com/drug-interaction-checker (accessed on 3 January 2021).
Variable | n | % | Statistical Test Result * | |
---|---|---|---|---|
Continent | Europe | 771 | 35.9 | χ2(5) = 849.91; p < 0.001 |
South America | 251 | 11.7 | ||
North America | 398 | 18.5 | ||
Australia | 109 | 5.1 | ||
Africa | 145 | 6.8 | ||
Asia | 472 | 22 | ||
Sex | Male | 1331 | 63.9 | χ2(1) = 165.25; p < 0.001 |
Female | 775 | 36.1 | ||
Age (years) | <40 | 1086 | 50.6 | χ2(2) = 742.18; p < 0.001 |
41–60 | 933 | 43.5 | ||
61–80 | 127 | 5.9 | ||
Seniority (years) | 1–10 | 1525 | 71.1 | χ2(3) = 2576.27; p < 0.001 |
11–20 | 427 | 19.9 | ||
21–30 | 155 | 7.2 | ||
>30 | 39 | 1.8 |
Continent | Percentage of Correct Answers Given: Before, after the Questionnaire | M (Mean) | SD (Standard Deviation) | Me (Median) | Statistical Test Result * |
---|---|---|---|---|---|
Europe | Before | 25.94 | 17.87 | 20 | Z = 24.36; p < 0.001 |
After | 90.69 | 14.41 | 100 | ||
South America | Before | 22.39 | 15.61 | 20 | Z = 14.02; p < 0.001 |
After | 95.62 | 8.48 | 100 | ||
North America | Before | 31.45 | 19.86 | 40 | Z = 17.42; p < 0.001 |
After | 86.93 | 12.86 | 80 | ||
Australia | Before | 22.2 | 17.92 | 20 | Z = 9.13; p < 0.001 |
After | 93.39 | 9.44 | 100 | ||
Africa | Before | 23.86 | 10.35 | 20 | Z = 10.65; p < 0.001 |
After | 93.93 | 9.22 | 100 | ||
Asia | Before | 27.33 | 12.49 | 20 | Z = 19.12; p < 0.001 |
After | 87.2 | 14.59 | 100 |
Use of the Pharmacological Interaction Database | Question | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |||||||
n | % | n | % | n | % | n | % | n | % | ||
Before | Wrong answer | 1510 | 70.4 | 1100 | 53.3 | 1132 | 52.7 | 879 | 41 | 1547 | 72.1 |
Good answer | 419 | 19.5 | 549 | 25.6 | 556 | 25.9 | 990 | 46.1 | 332 | 15.5 | |
I don’t know | 217 | 10.1 | 497 | 23.2 | 458 | 21.3 | 277 | 12.9 | 267 | 12.4 | |
Statistical test result * | χ2(2) = 1352.72; p < 0.001 | χ2(2) = 312.17; p < 0.001 | χ2(2) = 370.76; p < 0.001 | χ2(2) = 411.51; p < 0.001 | χ2(2) = 1453.33; p < 0.001 | ||||||
After | Wrong answer | 212 | 9.9 | 177 | 8.2 | 350 | 16.3 | 72 | 3.4 | 163 | 7.6 |
Good answer | 1934 | 90.1 | 1969 | 91.8 | 1796 | 83.7 | 2074 | 96.6 | 1983 | 92.4 | |
I don’t know | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Statistical test result * | χ2(1) = 1381.77; p < 0.001 | χ2(1) = 1496.39; p < 0.001 | χ2(1) = 974.33; p < 0.001 | χ2(1) = 1867.66; p < 0.001 | χ2(1) = 1543.52; p < 0.001 |
Continent | Knowledge and Use of Databases of Pharmacological Interactions (before the Study) | Interest in the Pharmacological Interaction Database (after Using it) | Efficacy of the Pharmacological Interaction Database in Daily Work (after Using It) | Statistical Test Result * | |||
---|---|---|---|---|---|---|---|
n | % | n | % | n | % | ||
Europe | 101 | 13.1 | 758 | 98.3 | 771 | 100 | χ2(2) = 1269.44; p < 0.001 |
South America | 32 | 12.7 | 248 | 98.8 | 251 | 100 | χ2(2) = 432.08; p < 0.001 |
North America | 62 | 15.6 | 391 | 98.2 | 398 | 100 | χ2(2) = 656.34; p < 0.001 |
Australia | 20 | 18.3 | 107 | 98.2 | 109 | 100 | χ2(2) = 172.16; p < 0.001 |
Africa | 22 | 15.2 | 144 | 94.3 | 145 | 100 | χ2(2) = 242.05; p < 0.001 |
Asia | 65 | 13.8 | 467 | 98.9 | 472 | 100 | χ2(2) = 804.12; p < 0.001 |
Variable | M (Mean) | SE (Standard Deviation) | Me (Median) | Statistical Test Result | |||||
---|---|---|---|---|---|---|---|---|---|
Before | After | Before | After | Before | After | Before | After | ||
Age (years) * | <40 | 25.1 | 89.2 | 20 | 100 | 16.09 | 14.05 | χ2(2) = 804.12; p < 0.001 | χ2(2) = 34.5; p < 0.001 |
41–60 | 30.37 | 90.5 | 20 | 100 | 16.35 | 12.61 | |||
61–80 | 10.39 | 95.75 | 0 | 100 | 14.22 | 11.72 | |||
Seniority (years) * | 1–10 | 27.44 | 89.94 | 20 | 100 | 16.44 | 13.29 | χ2(3) = 103.82; p < 0.001 | χ2(3) = 6.29; p = 0.1 |
11–20 | 28.43 | 90.68 | 20 | 100 | 16.04 | 13.37 | |||
21–30 | 16.13 | 90.16 | 20 | 100 | 17.07 | 13.93 | |||
>30 | 11.28 | 93.33 | 20 | 100 | 11.04 | 15.45 | |||
Sex ** | Male | 27.73 | 91.4 | 20 | 100 | 17.91 | 12.72 | U = 467,389; p < 0.001 | U = 461,255; p < 0.001 |
Female | 24.38 | 87.95 | 20 | 100 | 14.35 | 14.26 |
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Ordak, M.; Nasierowski, T.; Muszynska, E.; Bujalska-Zadrozny, M. Increasing the Effectiveness of Pharmacotherapy in Psychiatry by Using a Pharmacological Interaction Database. J. Clin. Med. 2021, 10, 2185. https://doi.org/10.3390/jcm10102185
Ordak M, Nasierowski T, Muszynska E, Bujalska-Zadrozny M. Increasing the Effectiveness of Pharmacotherapy in Psychiatry by Using a Pharmacological Interaction Database. Journal of Clinical Medicine. 2021; 10(10):2185. https://doi.org/10.3390/jcm10102185
Chicago/Turabian StyleOrdak, Michal, Tadeusz Nasierowski, Elzbieta Muszynska, and Magdalena Bujalska-Zadrozny. 2021. "Increasing the Effectiveness of Pharmacotherapy in Psychiatry by Using a Pharmacological Interaction Database" Journal of Clinical Medicine 10, no. 10: 2185. https://doi.org/10.3390/jcm10102185