Subscribe to RSS
Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT SyndromeFunding Data from the electronic health record was obtained through participation of the Health Data Compass data repository, in the Colorado Center for Personalized Medicine, on the University of Colorado Anschutz Medical Campus. CDS analysis was made possible through cooperation of the Epic CDS Governance Committee, directed by Dr. CT Lin. Funding for this investigation was provided by the National Institutes of Health, National Heart Lung and Blood Institute (MAR: K23 HL127296, R01 HL146824; KET: K12 HL137862).
Objective Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.
Methods We examined provider response data over a period from October 8, 2016 until November 8, 2018 for a CDS alert deployed within the EHR from a 13-hospital integrated health care system that fires when a patient with a QTc ≥ 500 ms within the past 14 days is prescribed a known QT-prolonging medication. We used multivariate generalized estimating equations to analyze the impact of therapeutic alternatives, relative risk of diLQTS for specific medications, and patient characteristics on provider response to the CDS and overall patient mortality.
Results The CDS alert fired 15,002 times for 7,510 patients for which the most common response (51.0%) was to override the alert and order the culprit medication. In multivariate models, we found that patient age, relative risk of diLQTS, and presence of alternative agents were significant predictors of adherence to the CDS alerts and that nonadherence itself was a predictor of mortality. Risk of diLQTS and presence of an alternative agent are major factors in provider adherence to a CDS to prevent diLQTS; however, provider nonadherence was associated with a decreased risk of mortality.
Conclusion Surrogate endpoints, such as provider adherence, can be useful measures of CDS value but attention to hard outcomes, such as mortality, is likely needed.
Protection of Human and Animal Subjects
The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed by the Colorado Multiple Institutional Review Board.
Received: 08 October 2020
Accepted: 03 January 2021
10 March 2021 (online)
© 2021. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
- 1 Selzer A, Wray HW. Quinidine syncope. Paroxysmal ventricular fibrillation occurring during treatment of chronic atrial arrhythmias. Circulation 1964; 30: 17-26
- 2 Dofetilide UK. Dofetilide. UK 68, UK 68798, tikosyn, xelide. Drugs R D 1999; 1 (04) 304-311
- 3 Straus SMJM, Sturkenboom MCJM, Bleumink GS. et al. Non-cardiac QTc-prolonging drugs and the risk of sudden cardiac death. Eur Heart J 2005; 26 (19) 2007-2012
- 4 Darpo B, Nebout T, Sager PT. Clinical evaluation of QT/QTc prolongation and proarrhythmic potential for nonantiarrhythmic drugs: the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use E14 guideline. J Clin Pharmacol 2006; 46 (05) 498-507
- 5 Poluzzi E, Raschi E, Godman B. et al. Pro-arrhythmic potential of oral antihistamines (H1): combining adverse event reports with drug utilization data across Europe. PLoS One 2015; 10 (03) e0119551
- 6 Milberg P, Eckardt L, Bruns HJ. et al. Divergent proarrhythmic potential of macrolide antibiotics despite similar QT prolongation: fast phase 3 repolarization prevents early afterdepolarizations and torsade de pointes. J Pharmacol Exp Ther 2002; 303 (01) 218-225
- 7 Kocher R, Emanuel EJ, DeParle NAM. The Affordable Care Act and the future of clinical medicine: the opportunities and challenges. Ann Intern Med 2010; 153 (08) 536-539
- 8 Abdolrasulnia M, Menachemi N, Shewchuk RM, Ginter PM, Duncan WJ, Brooks RG. Market effects on electronic health record adoption by physicians. Health Care Manage Rev 2008; 33 (03) 243-252
- 9 Vis C, Mol M, Kleiboer A. et al. Improving implementation of emental health for mood disorders in routine practice: systematic review of barriers and facilitating factors. JMIR Ment Health 2018; 5 (01) e20
- 10 Dexheimer JW, Talbot TR, Sanders DL, Rosenbloom ST, Aronsky D. Prompting clinicians about preventive care measures: a systematic review of randomized controlled trials. J Am Med Inform Assoc 2008; 15 (03) 311-320
- 11 Pell JM, Cheung D, Jones MA, Cumbler E. Don't fuel the fire: decreasing intravenous haloperidol use in high risk patients via a customized electronic alert. J Am Med Inform Assoc 2014; 21 (06) 1109-1112
- 12 Cheung D, Cumbler E, Hale G, Pell J. Reining in the QTc: reducing the risk of torsades de pointes across a major health system. J Am Med Inform Assoc 2018; 25 (09) 1202-1205
- 13 Drew BJ, Ackerman MJ, Funk M. et al; American Heart Association Acute Cardiac Care Committee of the Council on Clinical Cardiology, Council on Cardiovascular Nursing, American College of Cardiology Foundation. Prevention of torsade de pointes in hospital settings: a scientific statement from the American Heart Association and the American College of Cardiology Foundation. J Am Coll Cardiol 2010; 55 (09) 934-947
- 14 Prgomet M, Li L, Niazkhani Z, Georgiou A, Westbrook JI. Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis. J Am Med Inform Assoc 2017; 24 (02) 413-422
- 15 Terrell KM, Perkins AJ, Dexter PR, Hui SL, Callahan CM, Miller DK. Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patients: a randomized, controlled trial. J Am Geriatr Soc 2009; 57 (08) 1388-1394
- 16 Terrell KM, Perkins AJ, Hui SL, Callahan CM, Dexter PR, Miller DK. Computerized decision support for medication dosing in renal insufficiency: a randomized, controlled trial. Ann Emerg Med 2010; 56 (06) 623-629
- 17 Légat L, Van Laere S, Nyssen M, Steurbaut S, Dupont AG, Cornu P. Clinical decision support systems for drug allergy checking: systematic review. J Med Internet Res 2018; 20 (09) e258
- 18 Noseworthy PA, Peloso GM, Hwang SJ. et al. QT interval and long-term mortality risk in the Framingham Heart Study. Ann Noninvasive Electrocardiol 2012; 17 (04) 340-348
- 19 Poncet A, Gencer B, Blondon M. et al. Electrocardiographic screening for prolonged QT interval to reduce sudden cardiac death in psychiatric patients: a cost-effectiveness analysis. PLoS One 2015; 10 (06) e0127213
- 20 Haugaa KH, Bos JM, Tarrell RF, Morlan BW, Caraballo PJ, Ackerman MJ. Institution-wide QT alert system identifies patients with a high risk of mortality. Mayo Clin Proc 2013; 88 (04) 315-325
- 21 Sharma S, Martijn Bos J, Tarrell RF. et al. Providers' response to clinical decision support for QT prolonging drugs. J Med Syst 2017; 41 (10) 161
- 22 Tisdale JE, Jaynes HA, Kingery JR. et al. Effectiveness of a clinical decision support system for reducing the risk of QT interval prolongation in hospitalized patients. Circ Cardiovasc Qual Outcomes 2014; 7 (03) 381-390
- 23 Bertsche T, Pfaff J, Schiller P. 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 2010; 36 (04) 665-672
- 24 Sorita A, Bos JM, Morlan BW, Tarrell RF, Ackerman MJ, Caraballo PJ. Impact of clinical decision support preventing the use of QT-prolonging medications for patients at risk for torsade de pointes. J Am Med Inform Assoc 2015; 22 (e1): e21-e27
- 25 Roden DM. Acquired long QT syndromes and the risk of proarrhythmia. J Cardiovasc Electrophysiol 2000; 11 (08) 938-940
- 26 McCullagh LJ, Sofianou A, Kannry J, Mann DM, McGinn TG. User centered clinical decision support tools: adoption across clinician training level. Appl Clin Inform 2014; 5 (04) 1015-1025
- 27 Humphrey KE, Mirica M, Phansalkar S, Ozonoff A, Harper MB. Clinician perceptions of timing and presentation of drug-drug interaction alerts. Appl Clin Inform 2020; 11 (03) 487-496
- 28 Abdel-Rahman SM, Gill H, Carpenter SL. et al. Design and usability of an electronic health record-integrated, point-of-care, clinical decision support tool for modeling and simulation of antihemophilic factors. Appl Clin Inform 2020; 11 (02) 253-264
- 29 Trinkley KE, Kahn MG, Bennett TD. et al. Integrating the practical robust implementation and sustainability model with best practices in clinical decision support design: implementation science approach. J Med Internet Res 2020; 22 (10) e19676
- 30 Bhat S, Derington CG, Trinkley KE. Clinicians' values and preferences for medication adherence and cost clinical decision support in primary care: a qualitative study. Appl Clin Inform 2020; 11 (03) 405-414