Ortiz GX, Ulbrich AHDPS, Lenhart G, dos Santos HDP, Schwambach KH, Becker MW, Blatt CR. Drug-induced liver injury and COVID-19: Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice. Artif Intell Gastroenterol 2023; 4(2): 36-47 [DOI: 10.35712/aig.v4.i2.36]
Corresponding Author of This Article
Gabriela Xavier Ortiz, MSc, Academic Research, Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Sarmento Leite, 245, Porto Alegre 90050-170, Brazil. gabrielax@ufcspa.edu.br
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Drug-induced liver injury and COVID-19: Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice
Gabriela Xavier Ortiz, Ana Helena Dias Pereira dos Santos Ulbrich, Gabriele Lenhart, Henrique Dias Pereira dos Santos, Karin Hepp Schwambach, Matheus William Becker, Carine Raquel Blatt
Gabriela Xavier Ortiz, Karin Hepp Schwambach, Matheus William Becker, Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
Ana Helena Dias Pereira dos Santos Ulbrich, Henrique Dias Pereira dos Santos, Institute of Artificial Intelligence in Healthcare, Porto Alegre 90.620-200, Brazil
Gabriele Lenhart, Multiprofessional Residency Integrated in Health, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
Carine Raquel Blatt, Department of Pharmacoscience, Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
Author contributions: Ortiz GX conceptualization, data curation, formal analysis, and writing the original draft; Ulbrich AHDPS and dos Santos HDP resources, software and reviewing; Becker MW, Lenhart G, and Schwambach KH writing, reviewing, and editing; Blatt CR project administration and reviewing; All authors contributed to the article and approved the submitted version.
Institutional review board statement: The study was reviewed and approved by the Federal University of Health Sciences of Porto Alegre and the Institute of Artificial Intelligence in Healthcare.
Informed consent statement: This is not applicable to the study. The ethical advice is described in the document “Institutional Review Board Approval Form or Document".
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Gabriela Xavier Ortiz, MSc, Academic Research, Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Sarmento Leite, 245, Porto Alegre 90050-170, Brazil. gabrielax@ufcspa.edu.br
Received: June 4, 2023 Peer-review started: June 4, 2023 First decision: July 28, 2023 Revised: August 18, 2023 Accepted: September 5, 2023 Article in press: September 5, 2023 Published online: September 8, 2023 Processing time: 94 Days and 12.1 Hours
ARTICLE HIGHLIGHTS
Research background
Liver injury is a relevant condition in coronavirus disease 2019 (COVID-19) inpatients. Drug-induced liver injury (DILI) may be present in COVID-19 patients due to wide exposure to multiple treatments. Artificial intelligence (AI) applications are interesting tools for early detection of DILI cases in hospitals using electronic medical records.
Research motivation
DILI detection and monitoring is clinically relevant, as DILI may contribute to poor prognosis, prolonged hospitalization and increase indirect healthcare costs.
Research objectives
To demonstrate the use of AI and the updated Roussel Uclaf Causality Assessment Method (RUCAM) to detect DILI cases from data mining in electronic medical records (EMR) of COVID-19 inpatients.
In total, 17 COVID-19 inpatients were evaluated and there were 31 suspected drugs with the following RUCAM score: possible (n = 24), probable (n = 5), and unlikely (n = 2). DILI agents were ivermectin, bicalutamide, linezolid, azithromycin, ceftriaxone, amoxicillin-clavulanate, tocilizumab, piperacillin-tazobactam, and albendazole. Lack of essential clinical information excluded most patients.
Research conclusions
These results are included in a project of clinical pharmacy using AI tools. Future research must focus on the prospective applicability of the updated RUCAM to improve DILI quality data. The use of AI in clinical pharmacy decision support in conjunction with RUCAM can contribute to patient safety and pharmacovigilance practices, improving clinical outcomes.
Research perspectives
These results are included in a project of clinical pharmacy using AI tools. Future research must focus on the prospective applicability of the updated RUCAM to improve DILI quality data. The use of AI in clinical pharmacy decision support in conjunction with RUCAM can contribute to patient safety and pharmacovigilance practices, improving clinical outcomes.
