Hypoglycemia and severe lactic acidosis in a dog following metformin exposure

Barrella, Nicole; Eisenberg, Beth; Simpson, Stephanie Nicole

doi:10.1002/ccr3.1255

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…Bron et al conducted a study on over 200,000 type-2 diabetes mellitus patients who were on anti-diabetic medications to evaluate the risk of hypoglycemia and found that sulfonylureas were more likely to cause hypoglycemia than biguanides, such as metformin [49]; unlike sulfonylurea, hypoglycemia rarely occurs in biguanides poisoning [50,51]. Moreover, it has been noted that metformin-induced hypoglycemia has an incidence ranging from 0.6 to 12.2% [52]. Hypoglycemia in biguanides exposure is not common because they increase insulin sensitivity and does not increase insulin release.…”

Section: Discussionmentioning

confidence: 99%

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System

Mehrpour

Saeedi

Nakhaee

et al. 2023

BMC Med Inform Decis Mak

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Background Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases. Methods The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model’s performance. Results Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%. Conclusion Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.

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