[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

The Effect of a Clinical Decision Support System on Improving Adherence to Guideline in the Treatment of Atrial Fibrillation: An Interrupted Time Series Study

  • Mobile & Wireless Health
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

To evaluate the effect of a computerized Decision Support System (CDSS) on improving adherence to an anticoagulation guideline for the treatment of atrial fibrillation (AF). This study had an interrupted time series design. The adherence to the guideline was assessed at fortnightly (two weeks) intervals from January 2016 to January 2017, 6 months before and 6 months after intervention. Newly diagnosed patients with AF were included in the offices of ten cardiologists. Stroke and major bleeding risks were calculated by the CDSS which was implemented via a mobile application. Treatment recommendations based on the guideline were shown to cardiologists. The segmented regression model was used to evaluate the effect of CDSS on level and trend of guideline adherence for the treatment of AF. In our analysis, 373 patients were included. The trend of adherence to the anticoagulation guideline for the treatment of AF was stable in the pre-intervention phase. After the CDSS intervention, mean of the adherence to the guideline significantly increased from 48% to 65.5% (P-value < 0.0001). The trend of adherence to the guideline was stable in the post-intervention phase. Our results showed that the CDSS can improve adherence to the anticoagulation guideline for the treatment of AF. Registration ID: IRCT2016052528070N1.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Chugh, S.S., et al., Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation. 129(8):837–847, 2014.

    Article  PubMed  Google Scholar 

  2. Mozaffarian, D., et al., Executive Summary: Heart Disease and Stroke Statistics--2016 Update: A Report From the American Heart Association. Circulation. 133(4):447–454, 2016.

    Article  PubMed  Google Scholar 

  3. Miller, P.S., Andersson, F.L., and Kalra, L., Are cost benefits of anticoagulation for stroke prevention in atrial fibrillation underestimated? Stroke. 36(2):360–366, 2005.

    Article  PubMed  Google Scholar 

  4. Caro, J.J., and Albers, G.W., Optimizing oral anticoagulation in managed care. Am J Manag Care. 10(14 Suppl):S474–S477, 2004.

    PubMed  Google Scholar 

  5. O'Dell, K.M., Igawa, D., and Hsin, J., New oral anticoagulants for atrial fibrillation: a review of clinical trials. Clin Ther. 34(4):894–901, 2012.

    Article  PubMed  Google Scholar 

  6. Hart, R.G., Pearce, L.A., and Aguilar, M.I., Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med. 146(12):857–867, 2007.

    Article  PubMed  Google Scholar 

  7. Potpara, T.S., et al., Decision-Making in Clinical Practice: Oral Anticoagulant Therapy in Patients with Non-valvular Atrial Fibrillation and a Single Additional Stroke Risk Factor. Adv Ther, 2016.

  8. Kirchhof, P., et al., 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. European heart journal, 2016.37(38).

  9. January, C.T., et al., 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation. Circulation. 130(23):e199–e267, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lip, G.Y., et al., Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest. 137(2):263–272, 2010.

    Article  PubMed  Google Scholar 

  11. Pisters, R., et al., A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest. 138(5):1093–1100, 2010.

    Article  PubMed  Google Scholar 

  12. Ogilvie, I.M., et al., Characterization of the proportion of untreated and antiplatelet therapy treated patients with atrial fibrillation. Am J Cardiol. 108(1):151–161, 2011.

    Article  PubMed  Google Scholar 

  13. Holt, T.A., et al., Risk of stroke and oral anticoagulant use in atrial fibrillation: a cross-sectional survey. Br J Gen Pract. 62(603):e710–e717, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Vallakati, A., and Lewis, W.R., Underuse of anticoagulation in patients with atrial fibrillation. Postgrad Med. 128(2):191–200, 2016.

    Article  PubMed  Google Scholar 

  15. Arts, D.L., et al., Effectiveness and usage of a decision support system to improve stroke prevention in general practice: A cluster randomized controlled trial. PLoS One. 12(2):e0170974, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nieuwlaat, R., et al., Guideline-adherent antithrombotic treatment is associated with improved outcomes compared with undertreatment in high-risk patients with atrial fibrillation. The Euro Heart Survey on Atrial Fibrillation. Am Heart J. 153(6):1006–1012, 2007.

    Article  CAS  PubMed  Google Scholar 

  17. Sintchenko, V., et al., Comparative impact of guidelines, clinical data, and decision support on prescribing decisions: an interactive web experiment with simulated cases. J Am Med Inform Assoc. 11(1):71–77, 2004.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ennis, J., et al., Clinical decision support improves physician guideline adherence for laboratory monitoring of chronic kidney disease: a matched cohort study. BMC Nephrol. 16:163, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Goud, R., et al., The effect of computerized decision support on barriers to guideline implementation: a qualitative study in outpatient cardiac rehabilitation. Int J Med. Inform. 79(6):430–437, 2010.

    Article  PubMed  Google Scholar 

  20. Kawamoto, K., et al., Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. Bmj. 330(7494):765, 2005.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Arts, D.L., et al., Improving stroke prevention in patients with atrial fibrillation. Trials. 14:193, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Bajorek, B., et al., A cluster-randomized controlled trial of a computerized antithrombotic risk assessment tool to optimize stroke prevention in general practice: a study protocol. BMC Health Serv Res. 14:55, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Holt, T.A., et al., Automated Risk Assessment for Stroke in Atrial Fibrillation (AURAS-AF)--an automated software system to promote anticoagulation and reduce stroke risk: study protocol for a cluster randomised controlled trial. Trials. 14:385, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Chen, R., et al., Early Experiences from a guideline-based computerized clinical decision support for stroke prevention in atrial fibrillation. Stud Health Technol Inform. 192:244–247, 2013.

    PubMed  Google Scholar 

  25. Karlsson, L.O., et al., Clinical decision support for stroke prevention in atrial fibrillation (CDS-AF): Rationale and design of a cluster randomized trial in the primary care setting. American Heart Journal. 187:45–52, 2017.

    Article  PubMed  Google Scholar 

  26. Sheibani, R., et al., Effects of Computerized Decision Support Systems on management of Atrial Fibrillation: A Scoping Review. Journal of Atrial Fibrillation (JAFIB), 2017.10(1).

  27. Wagner, A.K., et al., Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 27(4):299–309, 2002.

    Article  CAS  PubMed  Google Scholar 

  28. Rodríguez del Águila, M.M., and Benítez-Parejo, N., Simple linear and multivariate regression models. Allergologia et Immunopathologia. 39(3):159–173, 2011.

    Article  PubMed  Google Scholar 

  29. Goud, R., et al., The effect of computerized decision support on barriers to guideline implementation: a qualitative study in outpatient cardiac rehabilitation. International journal of medical informatics. 79(6):430–437, 2010.

    Article  PubMed  Google Scholar 

  30. Lobach, D., et al., Enabling health care decisionmaking through clinical decision support and knowledge management. Evid Rep Technol Assess (Full Rep). 203:1–784, 2012.

    Google Scholar 

  31. Cook, D.A., et al., An automated clinical alert system for newly-diagnosed atrial fibrillation. PLoS One. 10(4):e0122153, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Eckman, M.H., et al., Impact of an Atrial Fibrillation Decision Support Tool on thromboprophylaxis for atrial fibrillation. American Heart Journal. 176:17–27, 2016.

    Article  PubMed  Google Scholar 

  33. Hendriks, J.L., et al., Improving guideline adherence in the treatment of atrial fibrillation by implementing an integrated chronic care program. Neth Heart J. 18(10):471–477, 2010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Setia, S., et al., Hypertension and blood pressure variability management practices among physicians in Singapore. Vasc Health Risk Manag. 13:275–285, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study was funded by Mashhad University of Medical Sciences (thesis number 940843).

Author information

Authors and Affiliations

Authors

Contributions

SE, AH: conception and design of study, interpretation of data, and supervising the study. RS: software development, data collection, analysis and interpretation, and drafting manuscript. MS: data collection and interpretation, and critical revision. AA: conception and design, and critical revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Saeid Eslami.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This study was approved by the Medical Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.fm.REC.1394.524). All procedures performed in the study were in accordance with the 1964 Helsinki declaration and its later amendments.

Informed consent

Informed consent was obtained from all cardiologists included in the study. Cardiologists’ information which must be considered as sensitive was not disclosed.

Additional information

This article is part of the Topical Collection on Mobile & Wireless Health

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheibani, R., Sheibani, M., Heidari-Bakavoli, A. et al. The Effect of a Clinical Decision Support System on Improving Adherence to Guideline in the Treatment of Atrial Fibrillation: An Interrupted Time Series Study. J Med Syst 42, 26 (2018). https://doi.org/10.1007/s10916-017-0881-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-017-0881-6

Keywords

Navigation