We investigated the feasibility of the clinical application of novice-practitioner-performed/offsite-mentor-guided ultrasonography for identifying the appendix. A randomized crossover study was conducted using a telesonography system that can transmit the ultrasound images displayed on the ultrasound monitor (ultrasound sequence video) and images showing the practitioner's operations (background video) to a smartphone without any interruption in motion over a Long-Term Evolution (LTE) network. Thirty novice practitioners were randomly assigned to two groups. The subjects in group A (n = 15) performed ultrasonography for the identification of the appendix under mentoring by an onsite expert, whereas those in group B (n = 15) performed the same procedure under mentoring by an offsite expert. Each subject performed the procedure on three simulated patients. After a 4-week interval, they performed the procedure again under the other type of mentoring. A total of 90 ultrasound examinations were performed in each scenario. The primary outcomes were the success rate for identifying the appendix and the time required to identify the appendix. The success rates for identifying the appendix were 91.1 % (82/90) in onsite-mentored ultrasonography and 87.8 % (79/90) in offsite-mentored ultrasonography; both rates were high, and there was no significant difference (p = 0.468) between them. The time required in the case of offsite mentoring (median, 242.9 s; interquartile range (IQR), 238.2) was longer than that for onsite mentoring (median, 291.4 s; IQR, 200.9); however, the difference was not significant (p = 0.051). It appears that offsite mentoring can allow novice onsite practitioners to perform ultrasonography as effectively as they can under onsite mentoring, even for examinations that require proficiency in rather complex practices, such as identifying the appendix.

Background/Aims: Muscle mass depletion has been suggested to predict morbidity and mortality in various diseases. However, it is not well known whether muscle mass depletion is associated with poor outcome in sepsis. We hypothesized that muscle mass depletion is associated with poor outcome in sepsis. Methods: Retrospective observational study was conducted in an emergency department during a 9-year period. Medical records of 627 patients with sepsis were reviewed. We divided the patients into 2 groups according to 28-day mortality and compared the presence of muscle mass depletion assessed by the cross-sectional area of the psoas muscle at the level of the third lumbar vertebra on abdomen CT scans. Univariate and multivariate logistic regression analyses were conducted to examine the association of scarcopenia on the outcome of sepsis. Results: A total of 274 patients with sepsis were finally included in the study: 45 (16.4%) did not survive on 28 days and 77 patients (28.1%) were identified as having muscle mass depletion. The presence of muscle mass depletion was independently associated with 28-day mortality on multivariate logistic analysis (OR 2.79; 95% CI 1.35–5.74, p = 0.01). Conclusions: Muscle mass depletion evaluated by CT scan was associated with poor outcome of sepsis patients. Further studies on the appropriateness of specific treatment for muscle mass depletion with sepsis are needed.

No abstract

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.