Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Assessing cardiovascular drug safety for clinical decision-making

Abstract

Optimal therapeutic decision-making requires integration of patient-specific and therapy-specific information at the point of care, particularly when treating patients with complex cardiovascular conditions. The formidable task for the prescriber is to synthesize information about all therapeutic options and match the best treatment with the characteristics of the individual patient. Computerized decision support systems have been developed with the goal of integrating such information and presenting the acceptable therapeutic options on the basis of their effectiveness, often with limited consideration of their safety for a specific patient. Assessing the safety of therapies relative to each patient is difficult, and sometimes impossible, because the evidence required to make such an assessment is either imperfect or does not exist. In addition, many of the alerts sent to prescribers by decision-support systems are not perceived as credible, and 'alert fatigue' causes warnings to be ignored putting patients at risk of harm. The CredibleMeds.org and BrugadaDrugs.org websites are prototypes for evidence-based sources of safety information that rank drugs for their risk of a specific form of drug toxicity—in these cases, drug-induced arrhythmias. Broad incorporation of this type of information in electronic prescribing algorithms and clinical decision support could speed the evolution of safe personalized medicine.

Key Points

  • For prescribers attempting to assess the benefit and risk potential of therapies, the large and rapidly changing knowledge base for adverse drug reactions represents a major challenge

  • Most automated decision-support systems designed to promote safe use of medicines do not provide evidence-based, actionable information about a specific patient's relative risk of harm from available therapeutic options

  • Independent resources, in which the huge amount of rapidly changing information on the relative safety of medications are continuously assessed, are needed to inform modern decision-support systems

  • Excessive QT prolongation on the electrocardiogram is a preventable drug reaction associated with >50 medications and drug interactions that can result in life-threatening ventricular arrhythmias (i.e. torsades de pointes), and death

  • Websites are available that provide independent information on the relative risk of drugs that can induce QT prolongation and torsades de pointes in various clinical settings

  • These resources provide a model for causality analysis that can be incorporated into decision-support systems to inform physicians about the relative risks of drug-induced adverse events

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Schematic for the causality analysis of relative drug safety for CredibleMeds.org.62
Figure 2: Hypothetical drug safety clinical decision support system.

Similar content being viewed by others

References

  1. Spinks, A., Glasziou, P. P. & Del Mar, C. B. Antibiotics for sore throat. Cochrane Database of Systematic Reviews Issue 4. Art. No.: CD000023 http://dx.doi.org/10.1002/14651858.CD000023.pub3.

  2. The Medical Letter. Trusted drug information and drugs facts since 1959 [online], (2013).

  3. Prorok, J. C., Iserman, E. C., Wilczynski, N. L. & Haynes, R. B. The quality, breadth, and timeliness of content updating vary substantially for 10 online medical texts: an analytic survey. J. Clin. Epidemiol. 65, 1289–1295 (2012).

    Article  PubMed  Google Scholar 

  4. Phua, J., See, K. C., Khalizah, H. J., Low, S. P. & Lim, T. K. Utility of the electronic information resource UpToDate for clinical decision-making at bedside rounds. Singapore Med. J. 53, 116–120 (2012).

    CAS  PubMed  Google Scholar 

  5. Wolters Kluwer Health. UpToDate [online], (2013).

  6. Isaac, T., Zheng, J. & Jha, A. Use of UpToDate and outcomes in US hospitals. J. Hosp. Med. 7, 85–90 (2012).

    Article  PubMed  Google Scholar 

  7. Jeffery, R. et al. How current are leading evidence-based medical textbooks? An analytic survey of four online textbooks. J. Med. Internet Res. 14, e175 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Banzi, R. et al. Speed of updating online evidence based point of care summaries: prospective cohort analysis. BMJ 343, d5856 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Cao, Y. et al. AskHERMES: an online question answering system for complex clinical questions. J. Biomed. Inform. 44, 277–288 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Eslami, S., Abu-Hanna, A., de Keizer, N. F. & de Jonge, E. Errors associated with applying decision support by suggesting default doses for aminoglycosides. Drug Saf. 29, 803–809 (2006).

    Article  CAS  PubMed  Google Scholar 

  11. Strom, B. L. et al. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. Arch. Intern. Med. 170, 1578–1583 (2010).

    Article  PubMed  Google Scholar 

  12. Rogers, J. A. et al. Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a beta regression meta-analysis. J. Pharmacokinet. Pharmacodyn. 39, 479–498 (2012).

    Article  PubMed  Google Scholar 

  13. Cabana, M. D. & Kim, C. Physician adherence to preventive cardiology guidelines for women. Women's Health Issues 13, 142–149 (2003).

    Article  PubMed  Google Scholar 

  14. Cabana, M. D. et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA 282, 1458–1465 (1999).

    Article  CAS  PubMed  Google Scholar 

  15. Veatch, R. M. Reasons physicians do not follow clinical practice guidelines. JAMA 283, 1685 (2000).

    CAS  PubMed  Google Scholar 

  16. Kesselheim, A. S. et al. A randomized study of how physicians interpret research funding disclosures. N. Engl. J. Med. 367, 1119–1127 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wennberg, D. E. Variation in the delivery of health care: the stakes are high. Ann. Intern. Med. 128, 866–868 (1998).

    Article  CAS  PubMed  Google Scholar 

  18. Yu, D. T. et al. Impact of implementing alerts about medication black-box warnings in electronic health records. Pharmacoepidemiol. Drug Saf. 20, 192–202 (2011).

    Article  PubMed  Google Scholar 

  19. Payne, T. H., Nichol, W. P., Hoey, P. & Savarino, J. Characteristics and override rates of order checks in a practitioner order entry system. Proc. AMIA Symp. 2002, 602–606 (2002).

    Google Scholar 

  20. Kohn, L. T., Corrigan, J. M. & Donaldson, M. S. for The Committee on Quality of Health Care in America. To err is human: building a safer health system. Institute of Medicine of the National Academies [online], (1999).

    Google Scholar 

  21. Lucado, J., Paez, K. & Elixhauser, A. Statistical brief #109. Medication-related adverse outcomes in US hospitals and emergency departments, 2008. Healthcare Cost and Utilization Project. Agency for Healthcare Research and Quality [online], (2011).

    Google Scholar 

  22. Straus, S. M. et al. Antipsychotics and the risk of sudden cardiac death. Arch. Intern. Med. 164, 1293–1297 (2004).

    Article  PubMed  Google Scholar 

  23. Straus, S. M. et al. Non-cardiac QTc-prolonging drugs and the risk of sudden cardiac death. Eur. Heart J. 26, 2007–2012 (2005).

    Article  PubMed  Google Scholar 

  24. Zhang, K., Young, C. & Berger, J. Administrative claims analysis of the relationship between warfarin use and risk of hemorrhage including drug–drug and drug–disease interactions. J. Managed Care Pharm. 12, 640–648 (2006).

    Article  Google Scholar 

  25. McWilliam, A., Ludder, R. & Nardinelli, C. AEI-Brookings Joint Center for Regulatory Studies Health care savings from personalized medicine using genetic testing: the case of warfarin. Working paper 06–23. Genelex.com, [online] (2006).

    Google Scholar 

  26. Johnson, J. A. & Bootman, J. L. Drug-related morbidity and mortality. A cost-of-illness model. Arch. Intern. Med. 155, 1949–1956 (1995).

    Article  CAS  PubMed  Google Scholar 

  27. Ernst, F. R. & Grizzle, A. J. Drug-related morbidity and mortality: updating the cost-of-illness model. J. Am. Pharm. Assoc. (Wash.) 41, 192–199 (2001).

    Article  CAS  Google Scholar 

  28. Health Care Financing Administration DoCE. National health expenditures, 1986–2000. Health Care Financ. Rev. 8, 1–36 (1987).

  29. Cutler, D. M. & Everett, W. Thinking outside the pillbox—medication adherence as a priority for health care reform. N. Engl. J. Med. 362, 1553–1555 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Hohl, C. M. et al. Outcomes of emergency department patients presenting with adverse drug events. Ann. Emerg. Med. 58, 270–279 (2011).

    Article  PubMed  Google Scholar 

  31. Woosley, R. L. Discovering adverse reactions: why does it take so long? Clin. Pharmacol. Ther. 76, 287–289 (2004).

    Article  PubMed  Google Scholar 

  32. Kerr, D. J. et al. Rofecoxib and cardiovascular adverse events in adjuvant treatment of colorectal cancer. N. Engl. J. Med. 357, 360–369 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Leahey, E. B. Jr et al. Interactions between quinidine and digoxin. JAMA 240, 533–534 (1978).

    Article  PubMed  Google Scholar 

  34. Peck, C. C. et al. Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development. Clin. Pharmacol. Ther. 51, 465–473 (1992).

    Article  CAS  PubMed  Google Scholar 

  35. Honig, P. K., Woosley, R. L., Zamani, K., Conner, D. P. & Cantilena, L. R. Jr. Changes in the pharmacokinetics and electrocardiographic pharmacodynamics of terfenadine with concomitant administration of erythromycin. Clin. Pharmacol. Ther. 52, 231–238 (1992).

    Article  CAS  PubMed  Google Scholar 

  36. Shah, R. R. Drugs, QT interval prolongation and ICH E14: the need to get it right. Drug Saf. 28, 115–125 (2005).

    Article  PubMed  Google Scholar 

  37. Hines, L. E., Malone, D. C. & Murphy, J. E. Recommendations for generating, evaluating, and implementing drug–drug interaction evidence. Pharmacotherapy 32, 304–313 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. Leape, L. L. et al. Systems analysis of adverse drug events. ADE Prevention Study Group. JAMA 274, 35–43 (1995).

    Article  CAS  PubMed  Google Scholar 

  39. Puckett, W. H. Jr & Visconti, J. A. An epidemiological study of the clinical significance of drug–drug interactions in a private community hospital. Am. J. Hosp. Pharm. 28, 247–253 (1971).

    PubMed  Google Scholar 

  40. Ford, D. R. Jr, Rivers, N. P. & Wood, G. C. A computerized detection system for potentially significant adverse drug–drug interactions. J. Am. Pharm. Assoc. 17, 354–357 (1977).

    PubMed  Google Scholar 

  41. Hohl, C. M., Dankoff, J., Colacone, A. & Afilalo, M. Polypharmacy, adverse drug-related events, and potential adverse drug interactions in elderly patients presenting to an emergency department. Ann. Emerg. Med. 38, 666–671 (2001).

    Article  CAS  PubMed  Google Scholar 

  42. Hohl, C. M. et al. Emergency physician recognition of adverse drug-related events in elder patients presenting to an emergency department. Acad. Emerg. Med. 12, 197–205 (2005).

    Article  PubMed  Google Scholar 

  43. Hohl, C. M. et al. Clinical decision rules to improve the detection of adverse drug events in emergency department patients. Acad. Emerg. Med. 19, 640–649 (2012).

    Article  PubMed  Google Scholar 

  44. Cavuto, N. J., Woosley, R. L. & Sale, M. Pharmacies and prevention of potentially fatal drug interactions. JAMA 275, 1086–1087 (1996).

    Article  CAS  PubMed  Google Scholar 

  45. Hazlet, T. K., Lee, T. A., Hansten, P. D. & Horn, J. R. Performance of community pharmacy drug interaction software. J. Am. Pharm. Assoc. (Wash.) 41, 200–204 (2001).

    Article  CAS  Google Scholar 

  46. Grizzle, A. J. et al. Reasons provided by prescribers when overriding drug–drug interaction alerts. Am. J. Manag. Care 13, 573–578 (2007).

    PubMed  Google Scholar 

  47. Burkhart, G. A., Sevka, M. J., Temple, R. & Honig, P. K. Temporal decline in filling prescriptions for terfenadine closely in time with those for either ketoconazole or erythromycin. Clin. Pharmacol. Ther. 61, 93–96 (1997).

    Article  CAS  PubMed  Google Scholar 

  48. Shah, R. R. Drug-induced QT interval prolongation: regulatory perspectives and drug development. Ann. Med. 36 (Suppl. 1), 47–52 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Wilkinson, J. J., Force, R. W. & Cady, P. S. Impact of safety warnings on drug utilization: marketplace life span of cisapride and troglitazone. Pharmacotherapy 24, 978–986 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Abarca, J. et al. Concordance of severity ratings provided in four drug interaction compendia. J. Am. Pharm. Assoc. (Wash.) 44, 136–141 (2004).

    Article  Google Scholar 

  51. Olvey, E. L., Clauschee, S. & Malone, D. C. Comparison of critical drug–drug interaction listings: the Department of Veterans Affairs medical system and standard reference compendia. Clin. Pharmacol. Ther. 87, 48–51 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Hines, L. E. et al. Evaluation of warfarin drug interaction listings in US product information for warfarin and interacting drugs. Clin. Ther. 33, 36–45 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Bertsche, T. et al. Prevention of adverse drug reactions in intensive care patients by personal intervention based on an electronic clinical decision support system. Intensive Care Med. 36, 665–672 (2010).

    Article  PubMed  Google Scholar 

  54. Isaac, T. et al. Overrides of medication alerts in ambulatory care. Arch. Intern. Med. 169, 305–311 (2009).

    Article  PubMed  Google Scholar 

  55. Weingart, S. N. et al. Clinicians' assessments of electronic medication safety alerts in ambulatory care. Arch. Intern. Med. 169, 1627–1632 (2009).

    PubMed  Google Scholar 

  56. Strom, B. L. Randomized clinical trial of a customized electronic alert requiring an affirmative response compared to a control group receiving a commercial passive CPOE alert: NSAID–warfarin co-prescribing as a test case. J. Am. Med. Inform. Assoc. 17, 411–415 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Glassman, P. A., Simon, B., Belperio, P. & Lanto, A. Improving recognition of drug interactions: benefits and barriers to using automated drug alerts. Med. Care 40, 1161–1171 (2002).

    Article  PubMed  Google Scholar 

  58. Fulda, T. R., Valuck, R. J., Vander Zanden, J., Parker, S. & Byrns, P. J. Disagreement among drug compendia on inclusion and ratings of drug-drug interaction. Curr. Ther. Res. 61, 540–548 (2000).

    Article  Google Scholar 

  59. Anthony, M. et al. Warfarin interactions with substances listed in drug information compendia and in the FDA-approved label for warfarin sodium. Clin. Pharmacol. Ther. 86, 425–429 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Agency for Healthcare Research and Quality. Evidence Report/Technology Assessment Number 211. Making health care safer II: an updated critical analysis of the evidence for patient safety practices. Executive summary [online], (2013).

  61. Roden, D. M. Cellular basis of drug-induced torsades de pointes. Br. J. Pharmacol. 154, 1502–1507 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. AZCERT. CredibleMeds.org [online], (2013).

  63. University of Amsterdam Academic Medical Center. BrugadaDrugs.org [online], (2013).

  64. Hill, A. B. The environment and disease: association or causation? Proc. R. Soc. Med. 58, 295–300 (1965).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Shakir, S. A. & Layton, D. Causal association in pharmacovigilance and pharmacoepidemiology: thoughts on the application of the Austin Bradford-Hill criteria. Drug Saf. 25, 467–471 (2002).

    Article  PubMed  Google Scholar 

  66. Perrio, M., Voss, S. & Shakir, S. A. Application of the Bradford-Hill criteria to assess the causality of cisapride-induced arrhythmia: a model for assessing causal association in pharmacovigilance. Drug Saf. 30, 333–346 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. van der Sijs, H. et al. Clinically relevant QTc prolongation due to overridden drug–drug interaction alerts: a retrospective cohort study. Br. J. Clin. Pharmacol. 67, 347–354 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  68. New Zealand Medicine and Medical Devices Safety Authority. Medsafe. Minutes of the 150th Medicines Adverse Reactions Committee meeting 13 September 2012 [online], (2012).

  69. Indiana University School of Medicine. Division of Clinical Pharmacology. Drug Interactions. Defining genetic influences on pharmacologic responses [online], (2013).

  70. Behr, E. R. & Roden, D. Drug-induced arrhythmia: pharmacogenomic prescribing? Eur. Heart J. 34, 89–95 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Pulley, J. M. et al. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin. Pharmacol. Ther. 92, 87–95 (2012).

    Article  CAS  PubMed  Google Scholar 

  72. Schildcrout, J. S. et al. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. Clin. Pharmacol. Ther. 92, 235–242 (2012).

    Article  CAS  PubMed  Google Scholar 

  73. DuMouchel, W. et al. Antipsychotics, glycemic disorders, and life-threatening diabetic events: a Bayesian data-mining analysis of the FDA adverse event reporting system (1968–2004). Ann. Clin. Psychiatry 20, 21–31 (2008).

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

Both authors researched data for the article, contributed to the discussion of content, wrote the manuscript, and reviewed/edited the article before submission.

Corresponding author

Correspondence to Raymond L. Woosley.

Ethics declarations

Competing interests

The authors declare that they are uncompensated officers of the nonprofit organization AZCERT.org, which sponsors the website www.CredibleMeds.org that is mentioned in this Review. The authors declare no other competing interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Woosley, R., Romero, K. Assessing cardiovascular drug safety for clinical decision-making. Nat Rev Cardiol 10, 330–337 (2013). https://doi.org/10.1038/nrcardio.2013.57

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrcardio.2013.57

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research