Isavuconazole Exposure in Critically Ill Patients Treated with Extracorporeal Membrane Oxygenation: Two Case Reports and a Narrative Literature Review.
Beatrijs MertensOmar ElkayalErwin DreesenJoost WautersPhilippe MeerssemanYves DebaveyeKarlien DegezellePieter VermeerschMatthias GijsenIsabel SprietPublished in: Antibiotics (Basel, Switzerland) (2023)
Effective dosing of isavuconazole in patients supported by extracorporeal membrane oxygenation (ECMO) is important due to the role of isavuconazole as a first-line treatment in patients with influenza- and COVID-19-associated pulmonary aspergillosis. To date, robust pharmacokinetic data in patients supported by ECMO are limited. Therefore, it is unknown whether ECMO independently impacts isavuconazole exposure. We measured isavuconazole plasma concentrations in two patients supported by ECMO and estimated individual pharmacokinetic parameters using non-compartmental analysis and two previously published population pharmacokinetic models. Furthermore, a narrative literature review on isavuconazole exposure in adult patients receiving ECMO was performed. The 24 h areas under the concentration-time curve and trough concentrations of isavuconazole were lower in both patients compared with exposure values published before. In the literature, highly variable isavuconazole concentrations have been documented in patients with ECMO support. The independent effect of ECMO versus critical illness itself on isavuconazole exposure cannot be deduced from our and previously published (case) reports. Pending additional data, therapeutic drug monitoring is recommended in critically ill patients, regardless of ECMO support.
Keyphrases
- extracorporeal membrane oxygenation
- acute respiratory distress syndrome
- end stage renal disease
- respiratory failure
- ejection fraction
- newly diagnosed
- chronic kidney disease
- prognostic factors
- case report
- systematic review
- mechanical ventilation
- sars cov
- pulmonary hypertension
- patient reported outcomes
- intensive care unit
- electronic health record
- deep learning
- childhood cancer