151
Views
3
CrossRef citations to date
0
Altmetric
Original Research

Evaluating the Effectiveness of an Additional Risk Minimization Measure to Reduce the Risk of Prescribing Mirabegron to Patients with Severe Uncontrolled Hypertension in Four European Countries

ORCID Icon, , , , , ORCID Icon, , , , , , & show all
Pages 423-433 | Published online: 01 May 2020

References

  • Staskin D, Herschorn S, Fialkov J, LM T, Walsh T, Schermer CR. A prospective, double-blind, randomized, two-period crossover, multicenter study to evaluate tolerability and patient preference between mirabegron and tolterodine in patients with overactive bladder (PREFER study). Int Urogynecol J. 2018;29(2):273–283. doi:10.1007/s00192-017-3377-528620791
  • Sicras-Mainar A, Navarro-Artieda R, Ruiz-Torrejon A, Saez M, Coll-de Tuero G, Sanchez L. [A retrospective, observational and multicentre study on patients with hyperactive bladder on treatment with mirabegron and oxybutinine under usual clinical practice conditions]. Semergen. 2017;43(4):277–288. doi:10.1016/j.semerg.2016.05.00627371430
  • Meves SH, Hummel T, Endres HG, et al. Effectiveness of antiplatelet therapy in atherosclerotic disease: comparing the ASA low-response prevalence in CVD, CAD and PAD. J Thromb Thrombolysis. 2014;37(2):190–201. doi:10.1007/s11239-013-0919-723553246
  • Guideline on Good Pharmacovigilance Practices (GVP) - Module IX – Signal Management (Rev 1). Vol. 2017 European Medicines Agency and Heads of Medicines Agencies; 2017.
  • Drug utilization study of mirabegron (Betmiga®) using real-world healthcare databases from the Netherlands. EU PAS Register Number 15063. Spain, UK and Finland 2017 Available from: http://www.encepp.eu/encepp/viewResource.htm?id=29595. Accessed 313, 2020.
  • WHO Collaborating Centre for Drug Statistics Methodology. ATC/DDD index. Available from: https://www.whocc.no/atc_ddd_index/. Accessed 313, 2020 2019.
  • Catalan V, Lelorier J. Predictors of long-term persistence on statins in a subsidized clinical population. Value Health. 2000;3(6):417–426. doi:10.1046/j.1524-4733.2000.36006.x16464201
  • Thomsen TL, Heintjes EM, Penning-van Beest FJA, Christensen TE, Herings RMC. Fewer treatment changes with premixed insulin analogues compared to premixed human insulin – a real-life treatment pattern analysis of patients with type 2 diabetes in the Netherlands. Value Health. 132010.
  • Heintjes EM, Thomsen TL, Penning-van Beest FJA, Christensen TE, Herings RMC. Glycaemic control and insulin utilisation in patients with type 2 diabetes initiated on a long-acting insulin analogue in a Dutch real-life setting. Value Health. 2010;132010:A55.
  • Bartelink ME, Elsman BH, Oostindjer Aet al,. NHG-Standaard Cardiovascular risicomanagement(Tweede herziening). Huisarts Wet. 2012;55(1):15.
  • Agency EM. Summary of Product Characteristics Betmiga; 2012.
  • Kontopantelis E, Doran T, Springate DA, Buchan I, Reeves D. Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ. 2015;350:h2750. doi:10.1136/bmj.h275026058820
  • Hawley S, Leal J, Delmestri A, et al. Anti-osteoporosis medication prescriptions and incidence of subsequent fracture among primary hip fracture patients in England and Wales: an interrupted time-series analysis. J Bone Miner Res. 2016;31(11):2008–2015. doi:10.1002/jbmr.v31.1127377877
  • Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299–309. doi:10.1046/j.1365-2710.2002.00430.x12174032
  • Zhang F, Wagner AK, Soumerai SB, Ross-Degnan D. Methods for estimating confidence intervals in interrupted time series analyses of health interventions. J Clin Epidemiol. 2009;62(2):143–148. doi:10.1016/j.jclinepi.2008.08.00719010644
  • Hawley S, MS A, Berencsi K, Judge A, Prieto-Alhambra D. Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study. Clin Epidemiol. 2019;11(197–205):197–205. doi:10.2147/CLEP30881136
  • Kilpelainen K, Tuomi-Nikula A, Thelen J, et al. Health indicators in Europe: availability and data needs. Eur J Public Health. 2012;22(5):716–721. doi:10.1093/eurpub/ckr19522294775
  • Marston L, Carpenter JR, Walters KR, Morris RW, Nazareth I, Petersen I. Issues in multiple imputation of missing data for large general practice clinical databases. Pharmacoepidemiol Drug Saf. 2010;19(6):618–626. doi:10.1002/pds.v19:620306452
  • Phelan M, Bhavsar NA, Goldstein BA. Illustrating informed presence bias in electronic health records data: how patient interactions with a health system can impact inference. EGEMS (Washington, DC). 2017;5(1):22.
  • Stevens SL, McManus RJ, Stevens RJ. Current practice of usual clinic blood pressure measurement in people with and without diabetes: a survey and prospective ‘mystery shopper’ study in UK primary care. BMJ Open. 2018;8(4):e020589. doi:10.1136/bmjopen-2017-020589
  • Luymes CH, de Ruijter W, Poortvliet RK, et al. Change in calculated cardiovascular risk due to guideline revision: a cross-sectional study in the Netherlands. Eur J Gen Pract. 2015;21(4):217–223. doi:10.3109/13814788.2015.106438926230039
  • Rosell-Murphy M, Rodriguez-Blanco T, Moran J, et al. Variability in screening prevention activities in primary care in Spain: a multilevel analysis. BMC Public Health. 2015;15(1):473. doi:10.1186/s12889-015-1767-525947302
  • Peng M, Chen G, Kaplan GG, et al. Methods of defining hypertension in electronic medical records: validation against national survey data. J Public Health (Oxf). 2016;38(3):e392–e399. doi:10.1093/pubmed/fdv15526547088
  • Suija K, Kivisto K, Sarria-Santamera A, et al. Challenges of audit of care on clinical quality indicators for hypertension and type 2 diabetes across four European countries. Fam Pract. 2015;32(1):69–74. doi:10.1093/fampra/cmu07825411423
  • NICE Quality and Outcomes Framework indicator. The National Institute for Health and Care Excellence (NICE). Available from: https://www.nice.org.uk/standards-and-indicators/qofindicators?categories=&page=3. Accessed 283 2019,2019.
  • Sidorenkov G, Voorham J, de Zeeuw D, Haaijer-Ruskamp FM, Denig P. Do treatment quality indicators predict cardiovascular outcomes in patients with diabetes? PLoS One. 2013;8(10):e78821. doi:10.1371/journal.pone.007882124205325
  • Wilson A. Building primary care in a changing Europe: case studies [Internet]. 2015(Observatory Studies Series, No. 40.). UK Available from: https://www.ncbi.nlm.nih.gov/books/NBK459014/. Accessed 313, 2020.
  • Dedeu T, Bolibar B, Gené J, Pareja C, Violan C. Building primary care in a changing Europe: case studies [Internet]. 2015(Observatory Studies Series, No. 40.). Spain Available from: https://www.ncbi.nlm.nih.gov/books/NBK459029/. Accessed 313, 2020.
  • Yeowell G, Smith P, Nazir J, Hakimi Z, Siddiqui E, Fatoye F. Real-world persistence and adherence to oral antimuscarinics and mirabegron in patients with overactive bladder (OAB): a systematic literature review. BMJ Open. 2018;8(11):e021889. doi:10.1136/bmjopen-2018-021889
  • Margulis AV, Linder M, Arana A, et al. Patterns of use of antimuscarinic drugs to treat overactive bladder in Denmark, Sweden, and the United Kingdom. PLoS One. 2018;13(9):e0204456. doi:10.1371/journal.pone.020445630260993