Hypoglycemia-Induced Basal Ganglia Infarct: A Rare Case of Metformin Toxicity in a Hemodialysis Patient

Metformin adverse event profile: a pharmacovigilance study based on the FDA Adverse Event Reporting System (FAERS) from 2004 to 2022

BACKGROUND

Metformin has the potential for treating numerous diseases, but there are still many unrecognized and unreported adverse events (AEs).

METHODS

We selected data from the United States FDA Adverse Event Reporting System (FAERS) database from the first quarter (Q1) of 2004 to the fourth quarter (Q4) of 2022 for disproportionality analysis to assess the association between metformin and related adverse events.

RESULTS

In this study 10,500,295 case reports were collected from the FAERS database, of which 56,674 adverse events related to metformin were reported. A total of 643 preferred terms (PTs) and 27 system organ classes (SOCs) that were significant disproportionality conforming to the four algorithms simultaneously were included. The SOCs included metabolic and nutritional disorders (p = 0.00E + 00), gastrointestinal disorders (p = 0.00E + 00) and others. PT levels were screened for adverse drug reaction (ADR) signals such as acute pancreatitis (p = 0.00E + 00), melas syndrome, pemphigoid (p = 0.00E + 00), skin eruption (p = 0.00E + 00) and drug exposure during pregnancy (p = 0.00E + 00).

CONCLUSION

Most of our results were consistent with the specification, but some new signals of adverse reactions such as acute pancreatitis were not included. Therefore, further studies are needed to validate unlabeled adverse reactions and provide important support for clinical monitoring and risk identification of metformin.

Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning

Background

Prognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients.

Methods

Two independent datasets, namely, the Korean Atrial Fibrillation Evaluation Registry in Ischemic Stroke Patients (K-ATTENTION) and the Korea University Stroke Registry (KUSR), were used for internal and external validation, respectively. These datasets include common variables such as demographic, laboratory, and imaging findings during early hospitalization. Outcomes were unfavorable functional status with modified Rankin scores of 3 or higher and mortality at 3 months. We developed two machine learning models, namely, a tree-based model and a multi-layer perceptron (MLP), along with a baseline logistic regression model. The area under the receiver operating characteristic curve (AUROC) was used as the outcome metric. The Shapley additive explanation (SHAP) method was used to evaluate the contributions of variables.

Results

Machine learning models outperformed logistic regression in predicting both outcomes. For 3-month unfavorable outcomes, MLP exhibited significantly higher AUROC values of 0.890 and 0.859 in internal and external validation sets, respectively, than those of logistic regression. For 3-month mortality, both machine learning models exhibited significantly higher AUROC values than the logistic regression for internal validation but not for external validation. The most significant predictor for both outcomes was the initial National Institute of Health and Stroke Scale.

Conclusion

The explainable machine learning model can reliably predict short-term outcomes and identify high-risk patients with AF-related strokes.