ObjectivesTo compare image quality on computed tomographic (CT) images acquired with filtered back-projection (FBP), adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) techniques in CT kidney/ureter/bladder (KUB) examination.MethodsEighteen patients underwent standard protocol CT KUB at our institution. The same raw data were reconstructed using FBP, ASIR and MBIR. Objective [mean image noise, contrast-to-noise ratio (CNR) for kidney and mean attenuation values of subcutaneous fat] and subjective image parameters (image noise, image contrast, overall visibility of kidneys/ureters/bladder, visibility of small structures, and overall diagnostic confidence) were assessed using a scoring system from 1 (best) to 5 (worst).ResultsObjective image measurements revealed significantly less image noise and higher CNR and the same fat attenuation values for the MBIR technique (P < 0.05). MBIR scored best in all the subjective image parameters (P < 0.001) with averages ranging between 2.05–2.73 for MBIR, 2.95–3.10 for ASIR and 3.08–3.31 for FBP. No significant difference was observed between FBP and ASIR (P > 0.05), while there was a significant difference between ASIR vs. MBIR (P < 0.05). The mean effective dose was 3 mSv.ConclusionMBIR shows superior reduction in noise and improved image quality (both objective and subjective analysis) compared with ASIR and FBP CT KUB examinations.Main Messages• There are many reconstruction options in CT.• Novel model-based iterative reconstruction (MBIR) showed the least noise and optimal image quality.• For CT of the kidneys/ureters/bladder, MBIR should be utilised, if available.• Further studies to reduce the dose while maintaining image quality should be pursued.

Objective: To determine the association of body mass index with the severity of COVID-19 pneumonia in hospitalized patients. Study Design: Cross-sectional study. Place and Duration of Study: Pak Emirates Military Hospital, Rawalpindi Pakistan, form May to Jun 2021. Methodology: Patients diagnosed with COVID-19 pneumonia on PCR and chest imaging and admitted to our hospital were included in the study. Body mass index was calculated on the first day of hospital admission, and they were followed up for two weeks during the disease. Increased oxygen demand, duration of admission, CT severity score and use of non-invasive ventilation were compared in patients with normal and increased body mass index. Results: A total of 800 COVID-19 patients admitted to the hospital were included in the final analysis. The mean age of the study participants was 41.36±4.55 years. Out of 800 patients, 337(42.1%) had normal BMI, 420(52.5%) were classed in the category of overweight and 43(5.4%) were obese. Furthermore, it was seen that increased demand for oxygen, high CT severity score and longer duration of hospital admission had a statistically significant relationship (p-value<0.05) with high body mass index. Conclusion: More than half of the patients admitted after diagnosis of COVID-19 had higher than normal body mass index. A significant association was found between increased demand for oxygen, high CT severity score, longer hospital admission duration, and high body mass index.Keywords: Body mass index, COVID-19, Outcome.

Objective: To compare NEDOCS with ICMED in predicting clinicians' concerns regarding crowding in the Emergency Department of a tertiary care hospital in Pakistan. Study Design: Prospective comparative study. Place and Duration of Study: Accident & Emergency Department, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Dec 2021 to Jan 2022. Methodology: This study was conducted at the Accident and Emergency Department over 14 different days, 30 data sets of 3-hour intervals each were collected using the NEDOCS and ICMED proforma. NEDOCS Scores and ICMED scores were calculated. In addition, perceptions of the staff regarding crowding and danger to the patient's status were recorded on Visual Analogue scales. Results: The mean recorded NEDOCS score was 577.94±251.57, with 29 'extremely overcrowded' and 1 'overcrowded' data set.The mean ICMED score was 2.86±0.83. Twenty-four (80%) sets did not have crowding, with only six (20%) sets being categorized as 'crowded'. The NEDOCS score had a moderately positive correlation with the crowding perception of the staff (Correlation coefficient (r)=0.593). Conclusion: NEDOCS was a more suitable measure for recording ED crowding in Pakistan, as it recorded the quantitative component of waiting time. ICMED, on the other hand, only recorded waiting time on a binary scale, with the waiting time impact not translated fully on the total score.

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