Electronic Alerts for Acute Kidney Injury Amelioration (ELAIA-1): a completely electronic, multicentre, randomised controlled trial: design and rationale

Marina Mutter, Melissa Martin, Yu Yamamoto, Aditya Biswas, Boian Etropolski, Harold Feldman, Amit Garg, Noah Gourlie, Stephen Latham, Haiqun Lin, Paul M Palevsky, Chirag Parikh, Erica Moreira, Ugochukwu Ugwuowo, Francis P Wilson, Marina Mutter, Melissa Martin, Yu Yamamoto, Aditya Biswas, Boian Etropolski, Harold Feldman, Amit Garg, Noah Gourlie, Stephen Latham, Haiqun Lin, Paul M Palevsky, Chirag Parikh, Erica Moreira, Ugochukwu Ugwuowo, Francis P Wilson

Abstract

Introduction: Acute kidney injury (AKI) is common among hospitalised patients and under-recognised by providers and yet carries a significant risk of morbidity and mortality. Electronic alerts for AKI have become more common despite a lack of strong evidence of their benefits. We designed a multicentre, randomised, controlled trial to evaluate the effectiveness of AKI alerts. Our aim is to highlight several challenges faced in the design of this trial, which uses electronic screening, enrolment, randomisation, intervention and data collection.

Methods and analysis: The design and implementation of an electronic alert system for AKI was a reiterative process involving several challenges and limitations set by the confines of the electronic medical record system. The trial will electronically identify and randomise 6030 adults with AKI at six hospitals over a 1.5-2 year period to usual care versus an electronic alert containing an AKI-specific order set. Our primary outcome will be a composite of AKI progression, inpatient dialysis and inpatient death within 14 days of randomisation. During a 1-month pilot in the medical intensive care unit of Yale New Haven Hospital, we have demonstrated feasibility of automating enrolment and data collection. Feedback from providers exposed to the alerts was used to continually improve alert clarity, user friendliness and alert specificity through refined inclusion and exclusion criteria.

Ethics and dissemination: This study has been approved by the appropriate ethics committees for each of our study sites. Our study qualified for a waiver of informed consent as it presents no more than minimal risk and cannot be feasibly conducted in the absence of a waiver. We are committed to open dissemination of our data through clinicaltrials.gov and submission of results to the NIH data sharing repository. Results of our trial will be submitted for publication in a peer-reviewed journal.

Trial registration number: NCT02753751; Pre-results.

Keywords: Acute kidney injury; acute renal failure; clinical decision support; electronic health record; randomized, alert.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
The ‘pop-up’ electronic alert. The alert gives relevant information regarding recent creatinine values and provides access to an AKI order set as well as relevant trial information. AKI, acute kidney injury.
Figure 2
Figure 2
AKI order set. This order set can be opened directly from the electronic alert and contains generic options for further work-up. AKI, acute kidney injury.
Figure 3
Figure 3
Histogram demonstrating the number of alerts received by providers during the pilot phase.

References

    1. Bihorac A, Yavas S, Subbiah S, et al. . Long-term risk of mortality and acute kidney injury during hospitalization after major surgery. Ann Surg 2009;249:851–8. 10.1097/SLA.0b013e3181a40a0b
    1. KDIGO CLinical Practice Guideline for Acute Kidney Injury. Kidney Int 2012;2(Suppl):1–138.
    1. Balasubramanian G, Al-Aly Z, Moiz A, et al. . Early nephrologist involvement in hospital-acquired acute kidney injury: a pilot study. Am J Kidney Dis 2011;57:228–34. 10.1053/j.ajkd.2010.08.026
    1. Wilson FP, Bansal AD, Jasti SK, et al. . The impact of documentation of severe acute kidney injury on mortality. Clin Nephrol 2013;80:417–25. 10.5414/CN108072
    1. Chertow GM, Lee J, Kuperman GJ, et al. . Guided medication dosing for inpatients with renal insufficiency. JAMA 2001;286:2839–44. 10.1001/jama.286.22.2839
    1. Salomon L, Deray G, Jaudon MC, et al. . Medication misuse in hospitalized patients with renal impairment. Int J Qual Health Care 2003;15:331–5. 10.1093/intqhc/mzg046
    1. Strom BL, Schinnar R, Bilker W, et al. . 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 2010;17:411–5. 10.1136/jamia.2009.000695
    1. Sequist TD, Morong SM, Marston A, et al. . Electronic risk alerts to improve primary care management of chest pain: a randomized, controlled trial. J Gen Intern Med 2012;27:438–44. 10.1007/s11606-011-1911-6
    1. Galanter WL, Polikaitis A, DiDomenico RJ. A trial of automated safety alerts for inpatient digoxin use with computerized physician order entry. J Am Med Inform Assoc 2004;11:270–7. 10.1197/jamia.M1500
    1. Kucher N, Koo S, Quiroz R, et al. . Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 2005;352:969–77. 10.1056/NEJMoa041533
    1. Dexter PR, Perkins S, Overhage JM, et al. . A computerized reminder system to increase the use of preventive care for hospitalized patients. N Engl J Med 2001;345:965–70. 10.1056/NEJMsa010181
    1. Wilson FP, Shashaty M, Testani J, et al. . Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial. Lancet 2015;385:1966–74. 10.1016/S0140-6736(15)60266-5
    1. Chan AW, Tetzlaff JM, Altman DG, et al. . SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med 2013;158:200–7. 10.7326/0003-4819-158-3-201302050-00583
    1. Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly 2014;144:w14073 10.4414/smw.2014.14073
    1. Torgerson DJ. Contamination in trials: is cluster randomisation the answer? BMJ 2001;322:355–7. 10.1136/bmj.322.7282.355
    1. Chawla LS, Bellomo R, Bihorac A, et al. . Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol 2017;13:241–57. 10.1038/nrneph.2017.2
    1. PASS 13. [computer program]. Kaysville, Utah, USA, 2014.
    1. Woolson RF, Bean JA, Rojas PB. Sample size for case-control studies using Cochran’s statistic. Biometrics 1986;42:927–32. 10.2307/2530706
    1. Nam J. Sample size determination for case-control studies and the comparison of stratified and unstratified analyses. Biometrics 1992;48:389–95. 10.2307/2532298
    1. O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics 1979;35:549–56. 10.2307/2530245
    1. Strom BL, Schinnar R, Aberra F, 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 2010;170:1578–83. 10.1001/archinternmed.2010.324
    1. Baseman JG, Revere D, Painter I, et al. . Public health communications and alert fatigue. BMC Health Serv Res 2013;13:295 10.1186/1472-6963-13-295
    1. Carspecken CW, Sharek PJ, Longhurst C, et al. . A clinical case of electronic health record drug alert fatigue: consequences for patient outcome. Pediatrics 2013;131:e1970–3. 10.1542/peds.2012-3252
    1. Citing reports of alarm-related deaths, the Joint Commission issues a sentinel event alert for hospials to improve medical device alarm safety. ED Manag 2013;26:suppl 1–3.
    1. Khalifa M. Clinical Decision Support: Strategies for Success. Procedia Comput Sci 2014;37:422–7. 10.1016/j.procs.2014.08.063
    1. Scheepers-Hoeks A, Neef RJ, Ackerman C, et al. . Success Factors and Barriers for Implementation of Advaced Clinical Decision Support Systems Jao C, Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain: InTeh, 2011.
    1. Melton BL, Zillich AJ, Russell SA, et al. . Reducing prescribing errors through creatinine clearance alert redesign. Am J Med 2015;128:1117–25. 10.1016/j.amjmed.2015.05.033
    1. Shah NR, Seger AC, Seger DL, et al. . Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc 2006;13:5–11. 10.1197/jamia.M1868
    1. van der Sijs H, Aarts J, Vulto A, et al. . Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006;13:138–47. 10.1197/jamia.M1809
    1. Guidi JL, Clark K, Upton MT, et al. . Clinician Perception of the Effectiveness of an Automated Early Warning and Response System for Sepsis in an Academic Medical Center. Ann Am Thorac Soc 2015;12:1514–9. 10.1513/AnnalsATS.201503-129OC
    1. Glassman PA, Simon B, Belperio P, et al. . Improving recognition of drug interactions: benefits and barriers to using automated drug alerts. Med Care 2002;40:1161–71. 10.1097/01.MLR.0000036429.31445.E0

Source: PubMed

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