Observational Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Sep 8, 2023; 4(2): 36-47
Published online Sep 8, 2023. doi: 10.35712/aig.v4.i2.36
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
Abstract
BACKGROUND

Liver injury is a relevant condition in coronavirus disease 2019 (COVID-19) inpatients. Pathophysiology varies from direct infection by virus, systemic inflammation or drug-induced adverse reaction (DILI). DILI detection and monitoring is clinically relevant, as it may contribute to poor prognosis, prolonged hospitalization and increase indirect healthcare costs. Artificial Intelligence (AI) applied in data mining of electronic medical records combining abnormal liver tests, keyword searching tools, and risk factors analysis is a relevant opportunity for early DILI detection by automated algorithms.

AIM

To describe DILI cases in COVID-19 inpatients detected from data mining in electronic medical records (EMR) using AI and the updated Roussel Uclaf Causality Assessment Method (RUCAM).

METHODS

The study was conducted in March 2021 in a hospital in southern Brazil. The NoHarm© system uses AI to support decision making in clinical pharmacy. Hospital admissions were 100523 during this period, of which 478 met the inclusion criteria. From these, 290 inpatients were excluded due to alternative causes of liver injury and/or due to not having COVID-19. We manually reviewed the EMR of 188 patients for DILI investigation. Absence of clinical information excluded most eligible patients. The DILI assessment causality was possible via the updated RUCAM in 17 patients.

RESULTS

Mean patient age was 53 years (SD ± 18.37; range 22-83), most were male (70%), and admitted to the non-intensive care unit sector (65%). Liver injury pattern was mainly mixed, mean time to normalization of liver markers was 10 d, and mean length of hospitalization was 20.5 d (SD ± 16; range 7-70). Almost all patients recovered from DILI and one patient died of multiple organ failure. There were 31 suspected drugs with the following RUCAM score: Possible (n = 24), probable (n = 5), and unlikely (n = 2). DILI agents in our study were ivermectin, bicalutamide, linezolid, azithromycin, ceftriaxone, amoxicillin-clavulanate, tocilizumab, piperacillin-tazobactam, and albendazole. Lack of essential clinical information excluded most patients. Although rare, DILI is a relevant clinical condition in COVID-19 patients and may contribute to poor prognostics.

CONCLUSION

The incidence of DILI in COVID-19 inpatients is rare and the absence of relevant clinical information on EMR may underestimate DILI rates. Prospects involve creation and validation of alerts for risk factors in all DILI patients based on RUCAM assessment causality, alterations of liver biomarkers and AI and machine learning.

Keywords: Chemical and drug induced liver injury, RUCAM, Artificial intelligence, COVID-19, Liver injury

Core Tip: This is a real-life study that correlated hospital clinical pharmacy data with artificial intelligence (AI) and pharmacovigilance in coronavirus disease 2019 (COVID-19) inpatients. Inpatient screening for liver injury was made with AI and drug-induced liver injury was evaluated with the Roussel Uclaf Causality Assessment Method (RUCAM) algorithm. A total of 17 COVID-19 inpatients were evaluated, there were 31 suspected drugs, RUCAM score: possible (n = 24), probable (n = 5), and unlikely (n = 2). This study contributed to the patient safety and pharmacovigilance database. These results are included in a project of clinical pharmacy using AI tools.