Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm.
Journal
Drugs - real world outcomes
ISSN: 2199-1154
Titre abrégé: Drugs Real World Outcomes
Pays: Switzerland
ID NLM: 101658456
Informations de publication
Date de publication:
06 Jan 2024
06 Jan 2024
Historique:
accepted:
06
11
2023
medline:
7
1
2024
pubmed:
7
1
2024
entrez:
6
1
2024
Statut:
aheadofprint
Résumé
The term triple whammy (TW) refers to the concomitant use of non-steroidal anti-inflammatory drugs, diuretics, and angiotensin system inhibitors; this combination significantly increases the risk of acute kidney injury (AKI). To prevent this serious complication, we developed an electronic algorithm that detects TW prescriptions in patients with additional risk factors such as old age and impaired kidney function. The algorithm alerts a clinical pharmacist who then evaluates and forwards the alert to the prescribing physician. We evaluated the performance of this algorithm in a retrospective observational study of clinical data from all adult patients admitted to the Cantonal Hospital of Aarau in Switzerland in 2021. We identified all patients who received a TW prescription, had a TW alert, or developed AKI during TW therapy. Algorithm performance was evaluated by calculating the sensitivity and specificity as a primary endpoint and determining the acceptance rate among clinical pharmacists and physicians as a secondary endpoint. Among 21,332 hospitalized patients, 290 patients had a TW prescription, of which 12 patients experienced AKI. Overall, 216 patients were detected by the alert algorithm, including 11 of 12 patients with AKI; the algorithm sensitivity is 88.3% with a specificity of 99.7%. Physician acceptance was high (77.7%), but clinical pharmacists were reluctant to forward the alerts to prescribers in some cases. The TW algorithm is highly sensitive and specific in identifying patients with TW therapy at risk for AKI. The algorithm may help to prevent AKI in TW patients in the future.
Sections du résumé
BACKGROUND AND OBJECTIVE
OBJECTIVE
The term triple whammy (TW) refers to the concomitant use of non-steroidal anti-inflammatory drugs, diuretics, and angiotensin system inhibitors; this combination significantly increases the risk of acute kidney injury (AKI). To prevent this serious complication, we developed an electronic algorithm that detects TW prescriptions in patients with additional risk factors such as old age and impaired kidney function. The algorithm alerts a clinical pharmacist who then evaluates and forwards the alert to the prescribing physician.
METHODS
METHODS
We evaluated the performance of this algorithm in a retrospective observational study of clinical data from all adult patients admitted to the Cantonal Hospital of Aarau in Switzerland in 2021. We identified all patients who received a TW prescription, had a TW alert, or developed AKI during TW therapy. Algorithm performance was evaluated by calculating the sensitivity and specificity as a primary endpoint and determining the acceptance rate among clinical pharmacists and physicians as a secondary endpoint.
RESULTS
RESULTS
Among 21,332 hospitalized patients, 290 patients had a TW prescription, of which 12 patients experienced AKI. Overall, 216 patients were detected by the alert algorithm, including 11 of 12 patients with AKI; the algorithm sensitivity is 88.3% with a specificity of 99.7%. Physician acceptance was high (77.7%), but clinical pharmacists were reluctant to forward the alerts to prescribers in some cases.
CONCLUSION
CONCLUSIONS
The TW algorithm is highly sensitive and specific in identifying patients with TW therapy at risk for AKI. The algorithm may help to prevent AKI in TW patients in the future.
Identifiants
pubmed: 38183571
doi: 10.1007/s40801-023-00405-y
pii: 10.1007/s40801-023-00405-y
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : GSASA Research Grant
ID : 2020
Informations de copyright
© 2024. The Author(s).
Références
Thomas MC. Diuretics, ACE inhibitors and NSAIDs—The triple whammy. Med J Aust. 2000;172(4):184–5.
doi: 10.5694/j.1326-5377.2000.tb125548.x
pubmed: 10772593
Camin RM, Cols M, Chevarria JL, Osuna RG, Carreras M, Lisbona JM, et al. Acute kidney injury secondary to a combination of renin-angiotensin system inhibitors, diuretics and NSAIDS: “The Triple Whammy.” Nefrologia. 2015;35(2):197–206.
doi: 10.1016/j.nefro.2015.05.021
pubmed: 26300514
Schetz M, Dasta J, Goldstein S, Golper T. Drug-induced acute kidney injury. Curr Opin Crit Care. 2005;11(6):555–65.
doi: 10.1097/01.ccx.0000184300.68383.95
pubmed: 16292059
Pannu N, Nadim MK. An overview of drug-induced acute kidney injury. Crit Care Med. 2008;36(4S):S216–23.
doi: 10.1097/CCM.0b013e318168e375
pubmed: 18382197
Prieto-García L, Pericacho M, Sancho-Martínez SM, Sánchez Á, Martínez-Salgado C, López-Novoa JM, et al. Mechanisms of triple whammy acute kidney injury. Pharmacol Ther. 2016;167:132–45.
doi: 10.1016/j.pharmthera.2016.07.011
pubmed: 27490717
Lapi F, Azoulay L, Yin H, Nessim SJ, Suissa S. Concurrent use of diuretics, angiotensin converting enzyme inhibitors, and angiotensin receptor blockers with non-steroidal anti-inflammatory drugs and risk of acute kidney injury: nested case-control study. BMJ. 2013;08(346): e8525.
doi: 10.1136/bmj.e8525
Dreischulte T, Morales DR, Bell S, Guthrie B. Combined use of nonsteroidal anti-inflammatory drugs with diuretics and/or renin-angiotensin system inhibitors in the community increases the risk of acute kidney injury. Kidney Int. 2015;88(2):396–403.
doi: 10.1038/ki.2015.101
pubmed: 25874600
Waikar SS, Wald R, Chertow GM, Curhan GC, Winkelmayer WC, Liangos O, et al. Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure. J Am Soc Nephrol. 2006;17(6):1688–94.
doi: 10.1681/ASN.2006010073
pubmed: 16641149
Seiberth S, Berner J, Hug MJ, Strobach D. “Double Whamm” and “Triple Whamm” combinations in hospitalized surgical patients—real life data from a tertiary teaching hospital. Pharmazie. 2022;77(1):38–43.
pubmed: 35045924
Kunitsu Y, Hira D, Morikochi A, Ueda T, Isono T, Morita SY, et al. Time until onset of acute kidney injury by combination therapy with “Triple Whammy” drugs obtained from Japanese Adverse Drug Event Report database. PLoS ONE. 2022;17:e0263682.
doi: 10.1371/journal.pone.0263682
pubmed: 35139129
pmcid: 8827454
Leete J, Wang C, López-Hernández FJ, Layton AT. Determining risk factors for triple whammy acute kidney injury. Math Biosci. 2022;347:108809. https://doi.org/10.1016/j.mbs.2022.108809 .
doi: 10.1016/j.mbs.2022.108809
pubmed: 35390421
Thakar CV, Christianson A, Himmelfarb J, Leonard AC. Acute kidney injury episodes and chronic kidney disease risk in diabetes mellitus. Clin J Am Soc Nephrol. 2011;6(11):2567–72.
doi: 10.2215/CJN.01120211
pubmed: 21903988
pmcid: 3359576
Amdur RL, Chawla LS, Amodeo S, Kimmel PL, Palant CE. Outcomes following diagnosis of acute renal failure in U.S. veterans: Focus on acute tubular necrosis. Kidney Int. 2009;76(10):1089–97.
doi: 10.1038/ki.2009.332
pubmed: 19741590
Olakotan O, Mohd Yusof M, Ezat Wan Puteh S. A Systematic Review on CDSS Alert Appropriateness. Stud Health Technol Inform 2020;270:906-10.
Krähenbühl-Melcher A, Schlienger R, Lampert M, Haschke M, Drewe J, Krähenbühl S. Drug-related problems in hospitals: a review of the recent literature. Drug Saf. 2007;30(5):379–407.
doi: 10.2165/00002018-200730050-00003
pubmed: 17472418
Wright A, Aaron S, Seger DL, Samal L, Schiff GD, Bates DW. Reduced Effectiveness of Interruptive Drug-Drug Interaction Alerts after Conversion to a Commercial Electronic Health Record. J Gen Intern Med. 2018;33(11):1868–76.
doi: 10.1007/s11606-018-4415-9
pubmed: 29766382
pmcid: 6206354
Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R, et al. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak. 2017;17(1):36.
doi: 10.1186/s12911-017-0430-8
pubmed: 28395667
pmcid: 5387195
Cavuto NJ, Woosley RL, Sale M. Pharmacies and prevention of potentially fatal drug interactions. JAMA. 1996;275(14):1086–7.
pubmed: 8601923
Poly TN, Islam MM, Yang HC, Li YJ. Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review. JMIR Med Inform. 2020;8(7): e15653.
doi: 10.2196/15653
pubmed: 32706721
pmcid: 7400042
Chou E, Boyce RD, Balkan B, Subbian V, Romero A, Hansten PD, et al. Designing and evaluating contextualized drug-drug interaction algorithms. JAMIA Open. 2021;4(1):ooab023.
doi: 10.1093/jamiaopen/ooab023
pubmed: 33763631
pmcid: 7976224
Chien SC, Chen YL, Chien CH, Chin YP, Yoon CH, Chen CY, et al. Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis. Healthcare (Basel). 2022;10(4):601.
doi: 10.3390/healthcare10040601
pubmed: 35455779
pmcid: 9028311
Vonbach P, Dubied A, Krahenbuhl S, Beer JH. Evaluation of frequently used drug interaction screening programs. Pharm World Sci. 2008;30(4):367–74.
doi: 10.1007/s11096-008-9191-x
pubmed: 18415695
Guthrie B, Makubate B, Hernandez-Santiago V, Dreischulte T. The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995–2010. BMC Med. 2015;7(13):74.
doi: 10.1186/s12916-015-0322-7
Fishman L, Brühwiler L, Schwappach D. Medikationssicherheit: Wo steht die Schweiz? Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 2018 2018/09/01;61(9):1152-8.
Davies LE, Spiers G, Kingston A, Todd A, Adamson J, Hanratty B. Adverse Outcomes of Polypharmacy in Older People: Systematic Review of Reviews. J Am Med Dir Assoc. 2020;21(2):181–7.
doi: 10.1016/j.jamda.2019.10.022
pubmed: 31926797
Alzueta N, Celaya MC, Acin MT, Echeverría A, Fontela C, Sanz L, et al. Triple whammy interaction: Improving patients’ safety. Eur J Hosp Pharm. 2019;26:A246.
Guthrie B, Treweek S, Petrie D, Barnett K, Ritchie LD, Robertson C, et al. Protocol for the Effective Feedback to Improve Primary Care Prescribing Safety (EFIPPS) study: a cluster randomised controlled trial using ePrescribing data. BMJ Open. 2012;2(6):e002359.
doi: 10.1136/bmjopen-2012-002359
pubmed: 23242239
pmcid: 3533102
Pons-Mesquida MÀ, Oms-Arias M, Diogène-Fadini E, Figueras A. Safer prescription of drugs: impact of the PREFASEG system to aid clinical decision-making in primary care in Catalonia. BMC Med Inform Decis Mak. 2021;21(1):349.
doi: 10.1186/s12911-021-01710-8
pubmed: 34911534
pmcid: 8675496
Pons-Mesquida MÀ, Oms-Arias M, Figueras A, Diogène-Fadini E. Impact of a system to assist in clinical decision-making in primary healthcare in Catalonia: prescription Self Audit. BMC Med Inform Decis Mak. 2022;22(1):70.
doi: 10.1186/s12911-022-01809-6
pubmed: 35305620
pmcid: 8934479
Rogero-Blanco E, Del-Cura-González I, Aza-Pascual-Salcedo M, García de Blas González F, Terrón-Rodas C, Chimeno-Sánchez S, et al. Drug interactions detected by a computer-assisted prescription system in primary care patients in Spain: MULTIPAP study. Eur J General Practice. 2021;27(1):90-6.
Dahmke H, Fiumefreddo R, Schuetz P, De Iaco R, Zaugg C. Tackling alert fatigue with a semi-automated clinical decision support system: quantitative evaluation and end-user survey. Swiss Med Wkly. 2023;7(153):40082.
doi: 10.57187/smw.2023.40082
Kellum JA, Lameire N, Aspelin P, Barsoum RS, Burdmann EA, Goldstein SL, et al. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney Int. 2012;2:1–138.
World Health Organization. The use of the WHO-UMC system for standardised case causality assessment. Internet; 2013.
Chen S. Retooling the creatinine clearance equation to estimate kinetic GFR when the plasma creatinine is changing acutely. J Am Soc Nephrol. 2013;24(6):877–88.
doi: 10.1681/ASN.2012070653
pubmed: 23704286
Berger FA, van der Sijs H, Becker ML, van Gelder T, van den Bemt PMLA. Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice. BMC Med Inform Decis Mak. 2020;20(1):171.
doi: 10.1186/s12911-020-01181-3
pubmed: 32703198
pmcid: 7376881
Damoiseaux-Volman BA, Medlock S, van der Meulen DM, et al. Clinical validation of clinical decision support systems for medication review: A scoping review. Br J Clin Pharmacol. 2022;88(5):2035–51. https://doi.org/10.1111/bcp.15160 .
doi: 10.1111/bcp.15160
pubmed: 34837238
Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003;10(6):523–30.
doi: 10.1197/jamia.M1370
pubmed: 12925543
pmcid: 264429
Bittmann JA, Haefeli WE, Seidling HM. Modulators influencing medication alert acceptance: an explorative review. Appl Clin Inform. 2022;13(2):468–85.
doi: 10.1055/s-0042-1748146
pubmed: 35981555
pmcid: 9388223