Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach

Abstract

Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The objective of this study is to apply machine learning techniques to predict the appropriateness of initial digoxin dosage. A total of 307 inpatients who had their conditions treated with digoxin between 2004 and 2013 at a medical center in Taiwan were collected in the study. Ten independent variables, including demographic information, laboratory data, and whether the patients had CHF were also noted. A patient with serum digoxin concentration being controlled at 0.5–0.9 ng/mL after his/her initial digoxin dosage was defined as having an appropriate use of digoxin; otherwise, a patient was defined as having an inappropriate use of digoxin. Weka 3.7.3, an open source machine learning software, was adopted to develop prediction models. Six machine learning techniques were considered, including decision tree (C4.5), k-nearest neighbors (kNN), classification and regression tree (CART), randomForest (RF), multilayer perceptron (MLP), and logistic regression (LGR). In the non-DDI group, the area under ROC curve (AUC) of RF (0.912) was excellent, followed by that of MLP (0.813), CART (0.791), and C4.5 (0.784); the remaining classifiers performed poorly. For the DDI group, the AUC of RF (0.892) was the best, followed by CART (0.795), MLP (0.777), and C4.5 (0.774); the other classifiers' performances were less than ideal. The decision tree-based approaches and MLP exhibited markedly superior accuracy performance, regardless of DDI status. Although digoxin is a high-alert medication, its initial dose can be accurately determined by using data mining techniques such as decision tree-based and MLP approaches. Developing a dosage decision support system may serve as a supplementary tool for clinicians and also increase drug safety in clinical practice.

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

“…Some studies used AI to predict the dosage of medicines such as insulin, digoxin, and warfarin [ 85 , 89 , 91 , 95 ]. AI in drug safety was also used to scan through the hospital’s EHR data and identify medication errors (ie, wrong medication prescriptions) [ 96 ].…”

Personalized drug concentration predictions with machine learning: an exploratory study

“…12 Hu et al, in a study on prediction of digoxin dosage found that machine learning models can accurately predict initial digoxin dose and help clinicians in safe use of digoxin. 14 Yao et al, found that machine learning algorithms are the best alternatives to predict serum digoxin concentrations whenever blood samples from newborn infants for therapeutic drug monitoring are not available. 15 Goicoechea et al, has found that machine learning models can be helpful in interpretation of plasma concentrations of antiretrovirals in therapeutic drug monitoring.…”

Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables

“…For example, a dose of digoxin that is too high for a patient can cause severe toxicity and even death. Only two and a half times a normal dosage of digoxin can be fatal in 50% of patients (Burns, 1999;Hu et al, 2018). NTI drugs are especially dangerous in patients that are elderly, have multiple illnesses, or are receiving multiple drugs (Burns, 1999).…”