Potential value of a rapid syndromic multiplex PCR for the diagnosis of native and prosthetic joint infections: a real-world evidence study

Introduction: The BIOFIRE Joint Infection (JI) Panel is a diagnostic tool that uses multiplex-PCR testing to detect microorganisms in synovial fluid specimens from patients suspected of having septic arthritis (SA) on native joints or prosthetic joint infections (PJIs). Methods: A study was conducted across 34 clinical sites in 19 European and Middle Eastern countries from March 2021 to June 2022 to assess the effectiveness of the BIOFIRE JI Panel. Results: A total of 1527 samples were collected from patients suspected of SA or PJI, with an overall agreement of 88.4 % and 85 % respectively between the JI Panel and synovial fluid cultures (SFCs). The JI Panel detected more positive samples and microorganisms than SFC, with a notable difference on Staphylococcus aureus, Streptococcus species, Enterococcus faecalis, Kingella kingae, Neisseria gonorrhoeae, and anaerobic bacteria. The study found that the BIOFIRE JI Panel has a high utility in the real-world clinical setting for suspected SA and PJI, providing diagnostic results in approximately 1 h. The user experience was positive, implying a potential benefit of rapidity of results' turnover in optimising patient management strategies. Conclusion: The study suggests that the BIOFIRE JI Panel could potentially optimise patient management and antimicrobial therapy, thus highlighting its importance in the clinical setting

A model-based analysis of the predictive performance of different renal function markers for cefepime clearance in the ICU

Several population pharmacokinetic models for cefepime in critically ill patients have been described, which all indicate that variability in renal clearance is the main determinant of the observed variability in exposure. The main objective of this study was to determine which renal marker best predicts cefepime clearance. A pharmacokinetic model was developed using NONMEM based on 208 plasma and 51 urine samples from 20 ICU patients during a median follow-up of 3 days. Four serum-based kidney markers (creatinine, cystatin C, urea and uromodulin) and two urinary markers [measured creatinine clearance (CLCR) and kidney injury molecule-1] were evaluated as covariates in the model. A two-compartment model incorporating a renal and non-renal clearance component along with an additional term describing haemodialysis clearance provided an adequate description of the data. The Cockcroft-Gault formula was the best predictor for renal cefepime clearance. Compared with the base model without covariates, the objective function value decreased from 1971.7 to 1948.1, the median absolute prediction error from 42.4% to 29.9% and the between-subject variability in renal cefepime clearance from 135% to 50%. Other creatinine- and cystatin C-based formulae and measured CLCR performed similarly. Monte Carlo simulations using the Sanford guide dose recommendations indicated an insufficient dose reduction in patients with a decreased kidney function, leading to potentially toxic levels. The Cockcroft-Gault formula was the best predictor for cefepime clearance in critically ill patients, although other creatinine- and cystatin C-based formulae and measured CLCR performed similarly

Short communication: Sensitivity of estrus alerts and relationship with timing of the luteinizing hormone surge

Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone-based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which model-based indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied.status: publishe