A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
Methods
Meropenem may be an important drug in the treatment of open tibial fractures and chronic osteomyelitis. Therefore, the objective of this study was to describe meropenem pharmacokinetics in plasma, subcutaneous adipose tissue (SCT), and cancellous bone using microdialysis in a porcine model. Six female pigs were assigned to receive 1000 mg of meropenem intravenously over five minutes. Measurements of meropenem were obtained from plasma, SCT, and cancellous bone for eight hours thereafter. Microdialysis was applied for sampling in solid tissues. The meropenem concentrations were determined using ultra-high-performance liquid chromatography.Objectives
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