Eslam Ayman Mohamed

Background Sacroiliac joint is one of the most common sources of low back pain, however it is difficult to place the needle accurately inside the joint space without image guidance, improvement of ultrasound technology may lead to high success rate of intra‐articular drug deposition. Objective Assessment of the success rate of ultrasound guided intra‐articular sacroiliac joint injection. Design prospective observational study. Methodology Ultrasound‐guided injections were performed for 34 patients suffering from sacroiliitis. After injection of the drug solution and withdrawal of the needle, an anteroposterior fluoroscopy image was obtained and recorded for the injected joint to detect whether it was pre‐dominantly intraarticular or periarticular, Clinical outcome using a numerical pain rating scale as well as limitation of physical functioning measured by the Oswestry Disability Index(ODI) were determined. Results 33 injections (84.6%) were intra‐articular, while 6 injections (15.4%) were periarticular as confirmed by fluoroscopy with no statistical difference regarding clinical outcome between them. The baseline mean pain score decreased from 7.21 to 1.92 one month after injection and the ODI results improved from a mean of 61.41 to 17.13. Intervention was well tolerated and 91.2% of patients were satisfied or most satisfied. Conclusion ultrasonography provides a high success rate of intraarticular sacroiliac joint injection as confirmed by fluoroscopy, no significant difference in clinical outcome between intraarticular and periarticular injection was found.

Objectives: Accurate estimation of a critically ill patient's caloric requirements is essential for a proper nutritional plan. This study aimed to evaluate the use of point‐of‐care ultrasound (US) to predict the resting energy expenditure (REE) in critically ill patients.Methods: In 69 critically ill patients, we measured the REE using indirect calorimetry (REE_IC), muscle layer thicknesses (MLTs), and cardiac output (CO). Muscle thickness was measured at the biceps and the quadriceps muscles. Patients were randomly split into a model development group (n = 46) and a cross‐validation group (n = 23). In the model development group, a multiple regression analysis was applied to generate REE using US (REE_US) values. In the cross‐validation group, REE was calculated by the REE_US and the resting energy expenditure using the Harris‐Benedict equation (REE_HB), and both were compared to the REE_IC.Results: In the model development group, the REE_US was predicted by the following formula: predicted REE_US (kcal/d) = 206 + 173.5 × CO (L/min) + 137 × MLT (cm) – 230 × (women = 1; men = 0) (R² = 0.8; P < .0001). In the cross‐validated group, the REE_IC and REE_US values were comparable (mean difference, −66 [−3.3%] kcal/d; P = .14). However, the difference between the mean REE_IC and the mean REE_HB was 455.8 (26%) kcal/d (P < .001). According to a Bland–Altman analysis, the REE_US agreed well with the REE_IC, whereas the REE_HB did not.Conclusions: Resting energy expenditure could be estimated from US measurements of MLTs and CO. Our point‐of‐care US model explains 80% of the change in the REE in critically ill patients.