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
Processing time: 94 Days and 12.1 Hours
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.
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).
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.
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.
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.
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.