Republished: Metformin-associated lactic acidosis: reinforcing learning points

Goonoo, Mohummad Shaan; Morris, Rebecca A.; Raithatha, Ajay; Creagh, F. M.

doi:10.1136/dtb.2021.235608rep

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…As we expected, acidosis and hypoglycemia are the most important factors in determining the outcome. Metformin-associated lactic acidosis (MALA) is a fatal condition following metformin poisoning and is accompanied by other gastrointestinal symptoms [ 24 ]. MALA has been reported to occur at up to 138 per 100,000 patients per year [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

Mehrpour

Saeedi

Hoyte

et al. 2022

BMC Pharmacol Toxicol

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Background With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin’s toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT). Methods This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation. Results Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively. Conclusions In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases.

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Section: Discussionmentioning

confidence: 99%

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

Mehrpour

Saeedi

Hoyte

et al. 2022

BMC Pharmacol Toxicol

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Early initiation of continuous renal replacement therapy for metformin-associated lactic acidosis

et al. 2023

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Introduction. Rapid diagnosis of metformin-associated lactic acidosis (MALA) and initiation of continuous renal replacement therapy (CRRT) in diabetic patient successfully corrects a severe metabolic disorder of a patient with acute renal failure. Case outline. A 58-year-old male patient with a medical history of type 2 diabetes and alcohol abuse was admitted to Emergency department because of the vomiting, diarrhea and altered mental status. Initial arterial blood gas (ABG) analysis revealed severe metabolic acidosis (pH: 6.8, PaCO2: 12 mmHg, HCO3: 3.2 mmol/l), but the lactate level was too high to measure. MALA was suspected based on progressive lactic acidosis and past intake of metformin. The renal replacement therapy (RRT) was initiated- continuous veno-venous hemodiafiltration (CVVHDF), and as a result a significant improvement of the clinical status, with both blood pH and lactate level showing normalization was achieved after finishing CRRT. Conclusion. Acute metformin-associated lactic acidosis carries an ominous prognosis. This case suggests the application of early initiation of CRRT in hemodynamically unstable diabetic patients with MALA.

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Republished: Metformin-associated lactic acidosis: reinforcing learning points

Cited by 2 publications

References 36 publications

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

Early initiation of continuous renal replacement therapy for metformin-associated lactic acidosis

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