Adverse drug events among hospitalized COVID-19 patients. Experience with the Global Trigger Tool

The Global Trigger Tool is a reliable method for detecting adverse events, demonstrating positive predictive value and significant sensitivity among patients with COVID-19.

The objective of this study was to develop and apply an adapted Global Trigger Tool to identify potential adverse events among hospitalized patients with moderate to severe COVID-19.

Materials and methods. The study included a literature review, analysis of spontaneous reports n=873 in the national database of the Russian Federation for the COVID-19 indication for the period 2020–2022, and application of the trigger tool in a single-center retrospective study n=329. Statistical processing was performed by the method of disproportionality with the determination of the odds ratio of reporting. The symptom-syndrome method based on parameterization of interaction effects using Zhegalkin polynomials was used to identify significant effects of drug associations; Fisher’s exact test was used to select the most significant associations for occurrence.

Results. Among the triggers with a frequency of 10 or higher in terms of mortality rate (≥80%), the leading ones were: pulmonary edema, hypotension, and respiratory failure, which is consistent with the COVID-19 clinical course of the disease. A disproportionately high incidence of respiratory failure was associated with favipiravir use. Leukocytosis associated with tofacitinib use and drug-induced liver damage associated with tocilizumab use.

Conclusions. The identified potential adverse events and their associated mortality risks among patients with moderate to severe COVID-19 allow for compliance with current clinical guidelines for the management of patients with COVID-19.

1. Griffin FA, Resar RK. IHI Global Trigger Tool for Measuring Adverse Events (Second Edition). IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2009. (Available on www.IHI.org).

2. Sharek PJ. The Emergence of the Trigger Tool as the Premier Measurement Strategy for Patient Safety. AHRQ WebM&M. 2012;2012(5):120.

3. Naessens JM, Campbell CR, Huddleston JM, et al. A comparison of hospital adverse events identified by three widely used detection methods. Int J Qual Health Care. 2009 Aug;21(4):301-7. doi: 10.1093/intqhc/mzp027.

4. Dillner P, Eggenschwiler LC, Rutjes AWS, et al. Incidence and characteristics of adverse events in paediatric inpatient care: a systematic review and meta-analysis. BMJ Qual Saf. 2023 Mar;32(3):133-149. doi: 10.1136/bmjqs-2022-015298.

5. Kryukov AV, Zhiryakova AS, Shevchuk YuV, et al. Safety of pharmacotherapy in COVID-19 patients: a literature review. Bezopasnost’ i risk farmakoterapii = Safety and Risk of Pharmacotherapy. 2022;10(4):326-344. (In Russ.). doi: 10.30895/2312-7821-2022-10-4-326-344.

6. Alshehail B, Al Jamea Z, Chacko R, et al. Incidence and risk factors of adverse drug reactions in patients with coronavirus disease 2019: A pharmacovigilance experience utilizing an ADR trigger tool. Saudi Pharm J. 2022 Apr;30(4):407-413. doi: 10.1016/j.jsps.2022.01.021.

7. Syraeva GI, Mishinova SA, Kolbin AS, Eremenko EO. Safety profile assessment of drug products used for the pathogenetic treatment of COVID-19. Clinical Microbiology and Antimicrobial Chemotherapy. 2021;23(3):314-329. (In Russ.). doi: 10.36488/cmac.2021.3.314-329.

8. Mishinova SA, Syraeva GI, Kolbin AS, et al. Report of the Russian database on adverse drug reactions for COVID-19-related drugs with a focus on favipiravir. Clinical Microbiology and Antimicrobial Chemotherapy. 2023;25(1):26-33. (In Russ.).\ doi: 10.36488/cmac.2023.1.26-33

9. Mishinova SA, Gomon YuM, Kolbin AS, et al. Factors associated with adverse outcome among hospitalized patients with moderate to severe COVID-19. Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice. 2023;(1):34-46. (In Russ.) doi: 10.37489/2588-0519-2023-1-34-46

10. Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998;54(4):315-21. doi: 10.1007/s002280050466.

11. Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483-486. doi: 10.1002/pds.677.

12. Alekseeva NP. The symptomsyndrome analysis of multivariate categorical data based on Zhegalkin polynomials. Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy. 2021;8(66)(3):394-405. (In Russ.). doi: 10.21638/spbu01.2021.302.