Predictive performance of serum digoxin concentration in patients with congestive heart failure by a hyperbolic model based on creatinine clearance
Predictive performance of serum digoxin concentration in patients with congestive heart failure by a hyperbolic model based on creatinine clearance
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DOI：
10.1046/j.1365-2710.2002.00418.x
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发表时间：
2002-08-01
影响因子：
2
通讯作者：
Yamaji, A
中科院分区：
文献类型：
--
作者：
Konishi, H;Shimizu, S;Yamaji, A
目的：建立以肌酸酐清除率（Ccr）为解释变量确定地高辛日需要量的简单公式。方法：我们收集了235例接受地高辛治疗的充血性心力衰竭住院患者的常规监测和临床实验室检测数据（稳态血清地高辛浓度和Ccr值）。将107组数据拟合为双曲线模型，以解释血清地高辛水平与日剂量之比（L/D）与6种方法测定的Ccr值之间的关系。通过非线性回归分析计算相关系数(r)。为了评估最佳拟合模型的有效性，使用另外128个数据集，将L/D比率的预测性能与先前发表的7个参考模型的预测性能进行了比较。结果：利用Cockcroft和Gault方程估算的Ccr值所建立的双曲模型显示实际Ccr值与估算Ccr值的相关性最密切（r = 0.81）。当将其他数据拟合到所提出的模型时，L/D比率（0.018 ng/mL）的平均预测误差（ME）（一种偏差度量）几乎可以忽略不计，并且该ME值被证明比以前发表的预测模型计算的值要小得多。该模型的平均绝对预测误差（一种精度度量）也令人满意。结论：该模型对血清地高辛水平有较好的预测效果。考虑到实际使用的简单性，该模型的临床应用将允许根据个体Ccr值准确、快速地确定地高辛的初始维持给药方案，而无需实际测量其血清浓度。
Objectives: To formulate a simple equation for determining the daily dose requirements of digoxin by inclusion of creatinine clearance (Ccr) values as an explanatory variable.Methods: We included 235 routine monitoring and clinical laboratory test data (steady-state serum digoxin concentration and Ccr values), obtained from hospitalized patients receiving digoxin for treatment of congestive heart failure. The 107 data sets were fitted to a hyperbolic model to account for the relation between the ratio of serum digoxin level to the daily dose (L/D) and the Ccr values determined by six methods. Their correlation coefficients (r) were computed by non-linear regression analysis. To evaluate the validity of the best-fitting model, the predictive performance of the L/D ratios was compared with those given by seven reference models previously published, using another 128 data sets.Results: The hyperbolic model involving the Ccr values estimated by Cockcroft and Gault's equation showed the closest correlation (r = 0.81) between the actual and estimated Ccr values. Mean prediction error (ME), a measure of bias, of the L/D ratio (0.018 ng/mL) was almost negligible when other data were fitted to the proposed model, and this ME value proved to be much smaller than those calculated from the previously published prediction models. Mean absolute prediction error, a measure of precision, by the proposed model was also satisfactory for prediction.Conclusions: The newly developed model provided good predictive performance of serum digoxin level. Taking simplicity in practical use into account, the clinical application of the proposed model will allow for accurate and rapid determination of the initial maintenance dosing regimen of digoxin based on the individual Ccr value, without actual measurement of its serum concentration.