A case of recovery from aphasia following dose reduction of cefepime by bayesian prediction-based therapeutic drug monitoring. Issue 5 (May 2020) - Oda, Kazutaka; Miyakawa, Toshikazu; Katanoda, Tomomi; Hashiguchi, Yumi; Iwamura, Koji; Nosaka, Kisato; Yamaguchi, Ayami; Jono, Hirofumi; Saito, Hideyuki
Abstract: Cefepime is known to exert bactericidal activity against Pseudomonas aeruginosa . Cefepime-induced neurotoxicity, most likely caused by increased exposure, has recently become a major concern in clinical practice; therefore, appropriate dose reduction of cefepime should be applied with respect to patients with low cefepime clearance (mostly eliminated by the kidneys). Here, we report a case in which Bayesian prediction-based therapeutic drug monitoring (Bayes-TDM) was effectively used to reduce the dose of cefepime in a patient with pneumonia to prevent neurotoxic complications. A woman (age: 59 years, body weight: 32.5 kg, serum creatinine concentration: 1.02 mg/dL) developed pneumonia caused by P. aeruginosa while receiving treatment for scleroderma and systemic lupus erythematosus. She started treatment with a dosing regimen of 1.0 g of cefepime every 8 h (day X). On day X+5, aphasia developed, and the serum cefepime concentration was 71.3 mg/L at trough. This concentration was twice or thrice higher than the reported safe concentration of cefepime (22 or 35 mg/L at trough). Therefore, we reduced the dose of cefepime to 0.5 g every 12 h using Bayes-TDM from day X+7. As a result, the severity of aphasia decreased by day X+10, and this dose was successfully continued up to day X+13 without further adjustment. In conclusion, individualizing doses by Bayes-TDM may be useful in preventing adverse effects associated with cefepime treatment.