Diagnosing Sarcopenia with AI-Aided Ultrasound (DINOSAUR)—A Pilot Study

Background: Sarcopenia has been recognized as a determining factor in surgical outcomes and is associated with an increased risk of postoperative complications and readmission. Diagnosis is currently based on clinical guidelines, which includes assessment of skeletal muscle mass but not quality. Ultrasound has been proposed as a useful point-of-care diagnostic tool to assess muscle quality, but no validated cut-offs for sarcopenia have been reported. Using novel automated artificial intelligence (AI) software to interpret ultrasound images may assist in mitigating the operator-dependent nature of the modality. Our study aims to evaluate the fidelity of AI-aided ultrasound as a reliable and reproducible modality to assess muscle quality and diagnose sarcopenia in surgical patients. Methods: Thirty-six adult participants from an outpatient clinic were recruited for this prospective cohort study. Sarcopenia was diagnosed according to Asian Working Group for Sarcopenia (AWGS) 2019 guidelines. Ultrasonography of the rectus femoris muscle was performed, and images were analyzed by an AI software (MuscleSound® (Version 5.69.0)) to derive muscle parameters including intramuscular adipose tissue (IMAT) as a proxy of muscle quality. A receiver operative characteristic (ROC) curve was used to assess the predictive capability of IMAT and its derivatives, with area under the curve (AUC) as a measure of overall diagnostic accuracy. To evaluate consistency between ultrasound users of different experience, intra- and inter-rater reliability of muscle ultrasound parameters was analyzed in a separate cohort using intraclass correlation coefficients (ICC) and Bland–Altman plots. Results:The median age was 69.5 years (range: 26–87), and the prevalence of sarcopenia in the cohort was 30.6%. The ROC curve plotted with IMAT index (IMAT% divided by muscle area) yielded an AUC of 0.727 (95% CI: 0.551–0.904). An optimal cut-off point of 4.827%/cm2 for IMAT index was determined with a Youden’s Index of 0.498. We also demonstrated that IMAT index has excellent intra-rater reliability (ICC = 0.938, CI: 0.905–0.961) and good inter-rater reliability (ICC = 0.776, CI: 0.627–0.866). In Bland–Altman plots, the limits of agreement were from −1.489 to 1.566 and −2.107 to 4.562, respectively. Discussion: IMAT index obtained via ultrasound has the potential to act as a point-of-care evaluation for sarcopenia screening and diagnosis, with good intra- and inter-rater reliability. The proposed IMAT index cut-off maximizes sensitivity for case finding, supporting its use as an easily implementable point-of-care test in the community for sarcopenia screening. Further research incorporating other ultrasound parameters of muscle quality may provide the basis for a more robust diagnostic tool to help predict surgical risk and outcomes.</p

A unique presentation of acute tophaceous gout in the lumbar spine causing cauda equina syndrome

Gout is a common metabolic disease characterized by the deposition of monosodium urate (MSU) crystals and typically affects the peripheral joint, rarely involving the axial skeleton. We present a rare case of acute tophaceous gout in the lumbar spine causing cauda equina syndrome. A 60-year-old man with a history of gout and prior admissions for polyarticular gout flare presented with acute onset of bilateral lower limb numbness and weakness. He underwent surgical decompression with drainage of the epidural collection, with histology consistent with tophaceous gout. The patient made a full recovery postoperatively and was discharged uneventfully. Due to the high initial suspicion for gout, early spinal decompression surgery was performed, and the patient was started on medical therapy. Spinal tophaceous should be considered in the list of different diagnoses of spinal epidural masses especially in the context of a history of gouty arthritis

Variable computed tomography appearances of COVID-19

The coronavirus disease 2019 (COVID-19) is typically diagnosed by specific assays that detect viral nucleic acid from the upper respiratory tract; however, this may miss infections involving only the lower airways. Computed tomography (CT) has been described as a diagnostic modality in the COVID-19 diagnosis and treatment plan. We present a case series with virologically confirmed COVID-19 pneumonia. Variable CT features were observed: consolidation with ground-glass opacities, ground-glass opacities with subpleural reticular bands, and an anterior-posterior gradient of lung abnormalities resembling that of acute respiratory distress syndrome. Evolution of CT findings was observed in one patient, where there was interval resolution of bilateral lung consolidation with development of bronchiolectasis and subpleural fibrotic bands. While sensitive for detecting lung parenchymal abnormalities in COVID-19 pneumonia, the use of CT for initial diagnosis is discouraged and should be reserved for specific clinical indications. Interpretation of chest CT findings should be correlated with duration of symptoms to better determine the disease stage and aid in patient management.Published versio