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Home Articles Vol 33,2024 No.9 Detail

Signal mining and analysis of adverse events of esketamine based on proportional imbalance method and machine learning algorithms

Published on Oct. 01, 2024Total Views: 222 times Total Downloads: 144 times Download Mobile

Author: CHEN Xi 1 LIU Chang 1 LING Yi 1 ZHANG Hewei 2 GUO Xiaojing 2

Affiliation: 1. College of Basic Medical Sciences, Naval Medical University, Shanghai 200433, China 2. Department of Army Medical Statistics, Faculty of Medical Services, Naval Medical University, Shanghai 200433, China

Keywords: Esketamine Treatment-resistant depression Adverse drug event Signal detection Disproportional assay Machine learning algorithm FAERS database Pharmacovigilance

DOI: 10.12173/j.issn.1005-0698.202408074

Reference: CHEN Xi, LIU Chang, LING Yi, ZHANG Hewei, GUO XiaojingSignal mining and analysis of adverse events of esketamine based on proportional imbalance method and machine learning algorithms[J].Yaowu Liuxingbingxue Zazhi,2024, 33(9):961-970.DOI: 10.12173/j.issn.1005-0698.202408074.[Article in Chinese]

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Abstract

Objective  To explore and analyse the signals of adverse events of esketamine, and to provide references for rational clinical use of the drug.

Methods The adverse event reports of esketamine from the first quarter of 2019 to the fourth quarter of 2023 in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database were collected. The reporting odds ratio (ROR) method and information component (IC) method in the disproportionality analysis and random forest (RF) algorithm, K-nearest neighbor algorithm and extreme gradient boosting (XGBoost) algorithm in machine learning algorithms were used for signal mining of target medicines respectively. The accuracy of machine learning signal detection results was assessed by the area under the curve (AUC).

Results  A total of 5  247 adverse event records with esketamine as the primary suspect drug were obtained. Using the traditional detection measures of dis-proportionality, 138 positive signal results were detected, 6 new adverse events including anticholinergic syndrome, urinary incontinence, double vision, pyelonephritis, spontaneous pneumothorax, biliary obstruction, were not included in the FDA drug inserts, and it was found that the drug may be more likely to cause cardiovascular problems. The results of the machine learning training showed that XGBoost algorithm and RF algorithm performed moderately well, with AUC means of 0.928 and 0.921, respectively. A total of 4 new potential adverse drug event signals, diplopia, deterioration of general physical health, suicidal ideation and withdrawal syndrome were detected by XGBoost algorithm and RF algorithm.

Conclusion  Esketamine is accompanied by some unknown risks while obtaining significant efficacy and adverse events not mentioned in the specification may occur in clinical practice. Healthcare professionals should be fully alert to the relevant adverse events when applying them in clinical treatment and take timely measures to ensure the safety of the treatment.

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References

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