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Factors Associated With Nephrotoxicity and Clinical Outcome in Patients Receiving Amikacin
Author(s) -
Williams Paul J.,
Hull J. Heyward,
Sarubbi Felix A.,
Rogers John F.,
Wargin William A.
Publication year - 1986
Publication title -
the journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 116
eISSN - 1552-4604
pISSN - 0091-2700
DOI - 10.1002/j.1552-4604.1986.tb02910.x
Subject(s) - nephrotoxicity , medicine , renal function , creatinine , pharmacokinetics , area under the curve , amikacin , urology , antibiotics , toxicity , chemistry , biochemistry
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 > MIC, total AUC > MIC, mean Schumacher's intensity factor (IF), total IF, and ln (mean maximum concentration [C max ]/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 <.05) with nephrotoxicity. In contrast, a stepwise discriminant function analysis identified only duration of therapy ( P < .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 C max /MIC) correlated significantly ( P < .05 ) with cure. In contrast, a simultaneous multivariable analysis identified IF, MIC, and total dose according to one model and ln (mean C max /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. These findings may assist clinicians in improving cure rates while minimizing the occurrence of nephrotoxicity.