RESUMO
OBJECTIVE: High plasma levels of mono-N-desethylamiodarone (MDEA), an active amiodarone metabolite, may be associated with tissue toxicity in heart failure (patients with heart rhythm disturbances); therefore, a tool that can identify patients for whom therapeutic drug monitoring (TDM) of MDEA is required. This multicenter study aimed to develop a decision tree (DT) model that can identify patients with heart rhythm disturbances at high MDEA concentrations. MATERIALS AND METHODS: A multicenter retrospective cohort study was conducted, including 157 adult patients with heart failure who received oral amiodarone treatment. A χ2 automatic interaction-detection algorithm was used to construct a DT model. In the DT analysis, the dependent variable was set as an MDEA trough plasma concentration of ≥ 0.6 µg/mL during the steady-state period. Explanatory variables were selected as factors with p < 0.05 in multivariate logistic regression analysis. RESULTS: The adjusted odds ratios for the daily dose of amiodarone and body mass index were 1.01 (95% coefficient interval: 1.008 - 1.021, p < 0.001) and 0.91 (95% confidence interval: 0.834 - 0.988, p = 0.025), respectively. For DT analysis, the risk of reaching plasma MDEA concentrations ≥ 0.6 µg/mL was relatively high, combined with a daily dose of amiodarone > 100 mg and body mass index ≤ 22.3 kg/m2 at 69.0% (20/29), and its trend was also detected in the sensitivity analysis. CONCLUSION: Patients taking a daily amiodarone dose > 100 mg and with a body mass index ≤ 22.3 kg/m2 warrant TDM implementation for MDEA to minimize the risk of MDEA-induced tissue toxicity.
RESUMO
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.
Assuntos
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Insuficiência Cardíaca , Adulto , Humanos , Estudos Retrospectivos , Volume Sistólico , Creatinina , Função Ventricular Esquerda , Digoxina/efeitos adversos , Insuficiência Cardíaca/induzido quimicamente , Aprendizado de Máquina , Cardiotônicos/efeitos adversosRESUMO
BACKGROUND: Polypharmacy, defined as the concurrent use of over six drugs, is common in the treatment of heart failure (HF); however, unpredictable drug interactions with bepridil may occur. In this study, we have elucidated the influence of polypharmacy on plasma bepridil concentrations in patients with HF. METHODS: We conducted a multicenter retrospective study involving 359 adult patients with HF who received oral bepridil. Because QT prolongation is an adverse effect following plasma bepridil concentrations ≥800 ng/mL, the risk factors for patients achieving these concentrations at steady state were elucidated via multivariate logistic regression. The correlation between bepridil dose and plasma concentration was examined. The effect of polypharmacy on the value of the concentration-to-dose (C/D) ratio was investigated. RESULTS: A significant relationship was observed between bepridil dose and plasma concentration (p < 0.001), and the intensity of the correlation was moderate (r = 0.503). Based on multivariate logistic regression, the adjusted odds ratios for a daily dose of bepridil ≥1.6 mg/kg, polypharmacy, and concomitant of aprindine, a cytochrome P450 2D6 inhibitor, were 6.82 (95% coefficient interval: 2.104-22.132, p = 0.001), 2.96 (95% coefficient interval: 1.014-8.643, p = 0.047), and 8.63 (95% coefficient interval: 1.684-44.215, p = 0.010), respectively. Despite the moderate correlation in non-polypharmacy, the correlation was not observed in polypharmacy. Therefore, inhibiting metabolism, along with other mechanisms, may contribute to the polypharmacy-induced increase in plasma bepridil concentrations. Moreover, the C/D ratios in the groups receiving 6-9 and 10≤ concomitant drugs were 1.28- and 1.70-fold higher than in those receiving <6 drugs, respectively. CONCLUSIONS: Plasma bepridil concentrations may be influenced by polypharmacy. Moreover, the plasma bepridil concentration increased in correlation with the number of concomitant drugs used. Although the mechanism of this increase could not be determined, plasma bepridil concentrations should be periodically monitored for safe use in patients with HF. TRIAL REGISTRATION: Retrospectively registered.