Construction of a Risk Prediction Model of Extended Release Oxycodone Tablet-Induced Nausea and Clarification of Predictive Factors

Kumai, Masayoshi; Imai, Susumu; Kato, Shintaro; Koyanagi, Ryo; Tsuruga, Kenkichi; Yamada, Takehiro; Takekuma, Yoh; Sugawara, Mitsuru

doi:10.1248/bpb.b20-01028

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…13) Decision tree (DT) analysis, a type of ML, has a flowchart-like model where users can easily predict the risk of adverse drug reactions by considering the mutual relationship between multiple factors. [14][15][16] Therefore, DT analysis may be a useful new predictive tool for digoxin toxicity in clinical settings, but this has not yet been explored.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of Digoxin Toxicity in Heart Failure: A Multicenter Retrospective Study

Asai

Tashiro

Kondo

et al. 2023

Biological & Pharmaceutical Bulletin

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Digoxin toxicity (plasma digoxin concentration ≥0.9 ng/mL) is associated with worsening heart failure (HF). Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of adverse drug reactions. The present study aimed to construct a flowchart using DT analysis that can be used by medical staff to predict digoxin toxicity. We conducted a multicenter retrospective study involving 333 adult patients with HF who received oral digoxin treatment. In this study, we employed a chi-squared automatic interaction detection algorithm to construct DT models. The dependent variable was set as the plasma digoxin concentration (≥ 0.9 ng/mL) in the trough during the steady state, and factors with p < 0.2 in the univariate analysis were set as the explanatory variables. Multivariate logistic regression analysis was conducted to validate the DT model. The accuracy and misclassification rates of the model were evaluated. In the DT analysis, patients with creatinine clearance <32 mL/min, daily digoxin dose ≥1.6 µg/kg, and left ventricular ejection fraction ≥50% showed a high incidence of digoxin toxicity (91.8%; 45/49). Multivariate logistic regression analysis revealed that creatinine clearance <32 mL/min and daily digoxin dose ≥1.6 µg/kg were independent risk factors. The accuracy and misclassification rates of the DT model were 88.2 and 46.2 2.7%, respectively. Although the flowchart created in this study needs further validation, it is straightforward and potentially useful for medical staff in determining the initial dose of digoxin in patients with HF.

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