Published online Sep 8, 2023. doi: 10.35712/aig.v4.i2.36
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
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.
DILI detection and monitoring is clinically relevant, as DILI may contribute to poor prognosis, prolonged hospitalization and increase indirect healthcare costs.
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.
The study was conducted in March 2021 in a hospital in southern Brazil. Hospital admissions were 100523 during this period. The NoHarm© system uses AI to support decision making in clinical pharmacy. 478 cases met the inclusion criteria and 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 updated RUCAM was applied to all suspected cases of DILI.
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.
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.
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.