Phenytoin-rifampin drug interaction in a hypoalbuminemic, renal failure patient: a complex clinical case

Evaluation of Goal Phenytoin Levels After an Initial Intravenous Loading Dose at an Academic Medical Center

BACKGROUND

Phenytoin intravenous loading doses are administered in status epilepticus to rapidly achieve therapeutic levels. Accurately assessing phenytoin levels after the initial load can be challenging because of its complex pharmacokinetic profile and nonstandardized weight-based loading doses.

OBJECTIVES

The objectives of this analysis were to determine the incidence of patients achieving goal phenytoin levels after the initial loading dose and characterize factors that contribute to achieving the goal level.

METHODS

This single-center, retrospective cohort analysis was approved by our institutional review board and included adult patients who received a phenytoin load from May 2016 to March 2021. Patients were excluded if no total phenytoin level was drawn within 24 hours of the load, if the maintenance dose was given before the first level was drawn, or if the patient was on phenytoin before the load. The major endpoint was the percentage of patients achieving a corrected goal phenytoin level of ≥10 mcg/mL after the initial load. Multivariate regression was used to determine predictors of achieving the goal phenytoin level.

RESULTS

Of the 152 patients included, 139 patients (91.4%) achieved a corrected goal level after the first load. Patients at goal received a significantly higher median weight-based loading dose (19.1 mg/kg [15.0-20.0] vs 12.6 mg/kg [10.1-15.0], P < 0.01). The multivariate analysis identified weight-based dosing as a statistically significant predictor of achieving the corrected goal level (odds ratio, 1.30; 95% CI, 1.12-1.53; P < 0.01).

CONCLUSION AND RELEVANCE

Most patients achieved a corrected goal phenytoin level after the initial load. A higher median weight-based loading dose was shown to be a predictor of achieving the goal level and should be encouraged for rapid seizure termination. Future studies are warranted to confirm patient-specific factors that affect rapid achievement of the goal phenytoin level.

Development and Validation of a Risk Prediction Model for Acute Kidney Injury After the First Course of Cisplatin

Purpose Cisplatin-associated acute kidney injury (C-AKI) is common. We sought to develop and validate a predictive model for C-AKI after the first course of cisplatin. Methods Clinical and demographic data were collected on patients who received cisplatin between 2000 and 2016 at two cancer centers. C-AKI was defined as a 0.3 mg/dL rise in serum creatinine within 14 days of receiving cisplatin. Using multivariable logistic regression models with C-AKI as the primary outcome, we created a scoring model from the development cohort (DC) and tested it in the validation cohort (VC). Results C-AKI occurred in 13.6% of 2,118 patients in the DC and in 11.6% of 2,363 patients in the VC. Factors significantly associated with C-AKI included age 61 to 70 years (odds ratio [OR], 1.64 [95% CI, 1.21 to 2.23]; P = .001) and 71 to 90 years (OR, 2.97 [95% CI, 2.06 to 4.28]; P < .001) compared with ≤ 60 years; cisplatin dose 101 to 150 mg (OR, 1.58 [95% CI, 1.14 to 2.19]; P = .007) and > 150 mg (OR, 3.73 [95% CI, 2.68 to 5.20]; P < .001) compared with ≤ 100 mg; a history of hypertension (OR, 2.10 [95% CI, 1.54 to 2.72]; P < .001) compared with no hypertension; and serum albumin 2.0 to 3.5 g/dL (OR, 2.21 [95% CI, 1.62 to 3.03]; P < .001) compared with > 3.5 g/dL. The baseline estimated glomerular filtration rate was not significantly associated with the risk of C-AKI. The c-statistics of the score-based model in the DC and the VC were 0.72 (95% CI, 0.69 to 0.75) and 0.70 (95% CI, 0.67 to 0.73), respectively. Scores of 0, 3.5, and 8.5 were associated with a probability of C-AKI of 0.03 (95% CI, 0.03 to 0.05), 0.12 (95% CI, 0.11 to 0.14), and 0.51 (95% CI, 0.43 to 0.60), respectively. Conclusion A score-based model created by using the patient's age, cisplatin dose, hypertension, and serum albumin is predictive of C-AKI.

Pharmacokinetic behavior presents drug therapy challenges

There are conditions that cause a substantial change in drug clearance to such a degree that how a specific drug is managed to optimize drug response and minimize drug toxicity presents a challenge. This review will focus on recent literature (within the past 5 years) that evaluates pathophysiologic and genetic conditions and drug interactions which can change drug clearance to the magnitude that response is affected. Situations discussed that cause an increase in drug clearance will include: augmented renal clearance in critically ill patients; ultrafast drug metabolism caused by gene duplication; and enzyme induction interactions caused by rifampin. Situations discussed that result in a reduction in clearance will include: multiple organ failure in critically ill, patients with non-functioning CYP2D6 and CYP2C8/9 alleles, and CYP3A4 drug interactions with erythromycin and clarithromycin. In each case evaluated clearance is changed to the magnitude such that managing drug therapy can be difficult.