Williams P J, Hull J H, Sarubbi F A, Rogers J F, Wargin W A
J Clin Pharmacol. 1986 Feb;26(2):79-86. doi: 10.1002/j.1552-4604.1986.tb02910.x.
Data from 60 patients treated with amikacin were analyzed for factors associated with nephrotoxicity. In 42 of these patients, data were examined for factors associated with clinical outcome. Variables evaluated included patient weight, age, sex, serum creatinine level, creatinine clearance, duration of therapy, total dose, mean daily dose, organism minimum inhibitory concentration (MIC), mean peak levels, mean trough levels, mean area under the serum concentration-time curve (AUC), total AUC, mean AUC greater than MIC, total AUC greater than MIC, mean Schumacher's intensity factor (IF), total IF, In (mean maximum concentration [Cmax]/MIC). Model-dependent pharmacokinetic parameters were calculated by computer based on a one-compartment model. When the parameters were examined individually, duration of therapy and total AUC correlated significantly (P less than .05) with nephrotoxicity. In contrast, a stepwise discriminant function analysis identified only duration of therapy (P less than .001) as an important factor. Based on this model and on Bayes' theorem, the predictive accuracy of identifying "nephrotoxic" patients increased from 0.17 to 0.39. When examined individually, mean IF, MIC, total dose, mean daily dose, and ln (mean Cmax/MIC) correlated significantly (P less than .05) with cure. In contrast, a simultaneous multivariable analysis identified IF, MIC, and total dose according to one model and ln (mean Cmax/MIC) according to a second statistical model of parameters selected to have the greatest prospective value. Based on Bayes' theorem and the first model, the predictive accuracy of identifying patients not cured increased from 0.19 to 0.83. For the second model, the predictive accuracy increased from 0.19 to 0.50.(ABSTRACT TRUNCATED AT 250 WORDS)
对60例接受阿米卡星治疗的患者数据进行分析,以确定与肾毒性相关的因素。其中42例患者的数据用于检查与临床结局相关的因素。评估的变量包括患者体重、年龄、性别、血清肌酐水平、肌酐清除率、治疗持续时间、总剂量、平均每日剂量、病原体最低抑菌浓度(MIC)、平均峰浓度、平均谷浓度、血清浓度-时间曲线下平均面积(AUC)、总AUC、平均AUC大于MIC的值、总AUC大于MIC的值、平均舒马赫强度因子(IF)、总IF、ln(平均最大浓度[Cmax]/MIC)。基于一室模型通过计算机计算模型依赖的药代动力学参数。当单独检查这些参数时,治疗持续时间和总AUC与肾毒性显著相关(P小于0.05)。相比之下,逐步判别函数分析仅确定治疗持续时间(P小于0.001)是一个重要因素。基于该模型和贝叶斯定理,识别“肾毒性”患者的预测准确性从0.17提高到0.39。当单独检查时,平均IF、MIC、总剂量、平均每日剂量和ln(平均Cmax/MIC)与治愈显著相关(P小于0.05)。相比之下,同时多变量分析根据一个模型确定IF、MIC和总剂量,并根据第二个具有最大前瞻性价值的参数统计模型确定ln(平均Cmax/MIC)。基于贝叶斯定理和第一个模型,识别未治愈患者的预测准确性从0.19提高到0.83。对于第二个模型,预测准确性从0.19提高到0.50。(摘要截短至250字)