Predictors of pulmonary failure following severe trauma: a trauma registry-based analysis

Background: The incidence of pulmonary failure in trauma patients is considered to be influenced by several factors such as liver injury. We intended to assess the association of various potential predictors of pulmonary failure following thoracic trauma and liver injury. Methods: Records of 12,585 trauma patients documented in the TraumaRegister DGU® of the German Trauma Society were analyzed regarding the potential impact of concomitant liver injury on the incidence of pulmonary failure using uni- and multivariate analyses. Pulmonary failure was defined as pulmonary failure of ≥ 3 SOFA-score points for at least two days. Patients were subdivided according to their injury pattern into four groups: group 1: AIS thorax < 3; AIS liver < 3; group 2: AIS thorax ≥ 3; AIS liver < 3; group 3: AIS thorax < 3; AIS liver ≥ 3 and group 4: AIS thorax ≥ 3; AIS liver ≥ 3. Results: Overall, 2643 (21%) developed pulmonary failure, 12% (n= 642) in group 1, 26% (n= 697) in group 2, 16% (n= 30) in group 3, and 36% (n= 188) in group 4. Factors independently associated with pulmonary failure included relevant lung injury, pre-existing medical conditions (PMC), sex, transfusion of more than 10 units of packed red blood cells (PRBC), Glasgow Coma Scale (GCS) ≤ 8, and the ISS. However, liver injury was not associated with an increased risk of pulmonary failure following severe trauma in our setting. Conclusions: Specific factors, but not liver injury, were associated with an increased risk of pulmonary failure following trauma. Trauma surgeons should be aware of these factors for optimized intensive care treatment

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Dosage Individualization of Linezolid: Precision Dosing of Linezolid To Optimize Efficacy and Minimize Toxicity

The high interindividual variability in the pharmacokinetics (PK) of linezolid has been described, which results in an unacceptably high proportion of patients with either suboptimal or potentially toxic concentrations following the administration of a fixed regimen. The aim of this study was to develop a population pharmacokinetic model of linezolid and use this to build and validate alogorithms for individualized dosing. A retrospective pharmacokinetic analysis was performed using data from 338 hospitalized patients (65.4% male, 65.5 [±14.6] years) who underwent routine therapeutic drug monitoring for linezolid. Linezolid concentrations were analyzed by using high-performance liquid chromatography. Population pharmacokinetic modeling was performed using a nonparametric methodology with Pmetrics, and Monte Carlo simulations were employed to calculate the 100% time >MIC after the administration of a fixed regimen of 600 mg administered every 12 h (q12h) intravenously (i.v.). The dose of linezolid needed to achieve a PTA ≥ 90% for all susceptible isolates classified according to EUCAST was estimated to be as high as 2,400 mg q12h, which is 4 times higher than the maximum licensed linezolid dose. The final PK model was then used to construct software for dosage individualization, and the performance of the software was assessed using 10 new patients not used to construct the original population PK model. A three-compartment model with an absorptive compartment with zero-order i.v. input and first-order clearance from the central compartment best described the data. The dose optimization software tracked patients with a high degree of accuracy. The software may be a clinically useful tool to adjust linezolid dosages in real time to achieve prespecified drug exposure targets. A further prospective study is needed to examine the potential clinical utility of individualized therapy