Purpose Flexion and rotation of the knee joint are supposed to alter the measurement of the mechanical leg axis on long leg radiographs. However, in patients with varus or valgus alignment it has not been systematically analyzed so far. The hypothesis is that measurement of the mechanical leg axis is more influenced by flexion and rotation in presence of varus or valgus alignment compared to patients with a straight coronal alignment. Methods 3D surface models of the lower extremities of seven individuals with varying degrees of coronal alignment were created based on CT data. The coronal alignment of the seven individuals captured the range between 9° varus and 9° valgus with equal steps of 3°. Combinations of internal and external rotations of 10°, 20°, and 30° with flexion of 5°, 10°, 15°, 20°, and 30° were simulated. The mechanical leg axis was measured for each combination as the antero-posterior (ap)-projected hip-knee-ankle (HKA-) angle. Results 294 simulations with all combinations of rotation and flexion were performed. Ranges of deviation of HKA never showed a critical deviation of more than 3° from median values. Deviations from baseline appeared normally distributed for all flexion and rotation combinations (p < 0.05) and the probability for a deviation from the mean mechanical leg axis of more than 3° was less than 0.03 for all combinations. Comparability of the models, therefore, could be assumed. Conclusion Deviations in HKA-angle measurements, caused by rotation or flexion, does not vary relevantly through the range of coronal alignment of 9° varus to 9° valgus. As a clinical relevance, deviations in HKA-angle measurements can be considered as comparable in patients with different coronal alignment. Level of evidence III.

Introduction The most frequently prescribed empirical antibiotic agents for mild and moderate diabetic foot infections (DFIs) are amino‐penicillins and second‐generation cephalosporins that do not cover Pseudomonas spp. Many clinicians believe they can predict the involvement of Pseudomonas in a DFI by visual and/or olfactory clues, but no data support this assertion. Methods In this prospective observational study, we separately asked 13 experienced (median 11 years) healthcare workers whether they thought the Pseudomonas spp. would be implicated in the DFI. Their predictions were compared with the results of cultures of deep/intraoperative specimens and/or the clinical remission of DFI achieved with antibiotic agents that did not cover Pseudomonas. Results Among 221 DFI episodes in 88 individual patients, intraoperative tissue cultures grew Pseudomonas in 22 cases (10%, including six bone samples). The presence of Pseudomonas was correctly predicted with a sensitivity of 0.32, specificity of 0.84, positive predictive value of 0.18 and negative predictive value 0.92. Despite two feedbacks of the interim results and a 2‐year period, the clinicians' predictive performance did not improve. Conclusion The combined visual and olfactory performance of experienced clinicians in predicting the presence of Pseudomonas in a DFI was moderate, with better specificity than sensitivity, and did not improve over time. Further investigations are needed to determine whether clinicians should use a negative prediction of the presence of Pseudomonas in a DFI, especially in settings with a high prevalence of pseudomonal DFIs.

Comparison of 3D and 2D speckle tracking performed on standard 2D and triplane 2D datasets of normal and pathological left ventricular (LV) wall-motion patterns with a focus on the effect that 3D volume rate (3DVR), image quality and tracking artifacts have on the agreement between 2D and 3D speckle tracking. 37 patients with normal LV function and 18 patients with ischaemic wall-motion abnormalities underwent 2D and 3D echocardiography, followed by offline speckle tracking measurements. The values of 3D global, regional and segmental strain were compared with the standard 2D and triplane 2D strain values. Correlation analysis with the LV ejection fraction (LVEF) was also performed. The 3D and 2D global strain values correlated good in both normally and abnormally contracting hearts, though systematic differences between the two methods were observed. Of the 3D strain parameters, the area strain showed the best correlation with the LVEF. The numerical agreement of 3D and 2D analyses varied significantly with the volume rate and image quality of the 3D datasets. The highest correlation between 2D and 3D peak systolic strain values was found between 3D area and standard 2D longitudinal strain. Regional wall-motion abnormalities were similarly detected by 2D and 3D speckle tracking. 2DST of triplane datasets showed similar results to those of conventional 2D datasets. 2D and 3D speckle tracking similarly detect normal and pathological wall-motion patterns. Limited image quality has a significant impact on the agreement between 3D and 2D numerical strain values.

Musculoskeletal modeling is a well-established method in spine biomechanics and generally employed for investigations concerning both the healthy and the pathological spine. It commonly involves inverse kinematics and optimization of muscle activity and provides detailed insight into joint loading. The aim of the present work was to develop and validate a procedure for the automatized generation of semi-subject-specific multi-rigid body models with an articulated lumbar spine. Individualization of the models was achieved with a novel approach incorporating information from annotated EOS images. The size and alignment of bony structures, as well as specific body weight distribution along the spine segments, were accurately reproduced in the 3D models. To ensure the pipeline’s robustness, models based on 145 EOS images of subjects with various weight distributions and spinopelvic parameters were generated. For validation, we performed kinematics-dependent and segment-dependent comparisons of the average joint loads obtained for our cohort with the outcome of various published in vivo and in situ studies. Overall, our results agreed well with literature data. The here described method is a promising tool for studying a variety of clinical questions, ranging from the evaluation of the effects of alignment variation on joint loading to the assessment of possible pathomechanisms involved in adjacent segment disease.