Haytham Mohamed Nasser

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease with diverse manifestations, which resembles a clinical challenge to be managed. Lupus nephritis is a life-threatening condition as about 10% of patients develop chronic kidney disease. Aim of the work: To assess the role of resistive index (RI) as a noninvasive parameter in detecting renal affection in SLE patients. Results: A case–control study included 3 matched groups: 30 patients, 15 SLE with no renal affection, and 15 SLE lupus nephritis patients, who were selected, diagnosed according to ACR criteria 2019 for SLE, beside 15 age- and gender-matched healthy controls without any risk factors of chronic diseases. Written informed consent was obtained from all the three groups, and the study was approved by ethical committee. There was a statistically significant increase in both SLEDAI and renal SLEDAI scores, serum BUN, creatinine, urinary pus cells, RBCs, casts and proteins, 24-h urinary proteins, and protein/creatinine ratio beside a statistically significant increase in both right and left resistive indices in the group of lupus nephritis than the other group. There was highly statistically significant difference between SLE without nephritis and SLE with nephritis regarding renal echogenicity. There was statistically significant positive correlation between average RI and SLEDAI, rSLEDAI, serum creatinine, BUN, 24-h urinary proteins, protein/creatinine ratio, and renal echogenicity. Relation between renal echogenicity and demographic, laboratory, and clinical data was highly statistically significant with rSLEDAI, serum creatinine, BUN, 24-h urinary proteins, and P/C ratio. Our study highlighted that the best cutoff point of Rt average RI to detect SLE with nephritis group was found > 0.68 with sensitivity of 86.7% and specificity of 100.0%, while the best cutoff point of left average RI to detect SLE with nephritis group was found > 0.7 with sensitivity of 80.0% and specificity of 100.0%. Conclusion: Renal RI is a noninvasive technique that can be used for detection renal disease activity in SLE patients, together with renal parenchymal echogenicity by grayscale US.

Purpose of this study is: To evaluate the role of diffusion weighted magnetic resonance imaging in differentiation between malignant & benign hepatic focal lesions. Methods: The study included 38 patients all of them were with hepatic focal lesions detected by ultrasound (between 20 and 85 years of age) referred from GIT and oncology departments. Each patient included in the study was subjected to full history taking, ultrasonography and conventional MRI sequences, post Gd- DTPA dynamic and Diffusion Weighted imaging as well as ADC value measurement. Technique was performed using a standard 1.5 Tesla unit (Ingenia, Philips). Detailed MRI and laboratory investigations were done. Results: The study showed significant results were obtained between ADC values of benign and malignant hepatic focal lesions (p < 0.001). Conclusion: We concluded in this study according to the obtained results that DWI sequence together with quantitative ADC values should be used as an essential sequence to supplement the conventional MRI sequences for proper detection and characterization of hepatic focal lesions.

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.