A Median Arcuate Ligament Syndrome Could Be Re-termed as a Nutcracker Celiac Ganglion Abdominal Pain Syndrome.

Background/Aims: Median arcuate ligament syndrome (MALS) is known as chronic recurrent abdominal pain related to compression of the celiac artery by the median arcuate ligament. We aim to seek the specific mechanism of the pain by evaluating symptoms and radiological characteristics on abdominal CT scans. Methods: We analyzed 35 patients who visited the emergency room for recurrent abdominal pain after cholecystectomy. We classified the characteristics of patients as 4 clinical components and 2 radiological components. We defined the sum of weighted clinical scores and weighted radiological scores as nutcracker ganglion abdominal pain syndrome (NCGAPS) scores. We categorized the patients into 3 groups classified by the degree of NCGAPS scores. The 3 patients with top-3 NCGAPS scores were recommended for CT angiography. Results: When the suspicion was graded by NCGAPS scores, post stenotic dilatation was significantly different among all groups (P < 0.001). The clinical components of pain varied positional or respirational change and continuous pain were significantly different among all the groups (P < 0.01). NCGAPS scores can remarkably differentiate highly suspicious patients in comparison to simply combined scores. Only 1 patient in the highly suspicious group by NCGAPS scores took the CT angiography and was confirmed with NCGAPS. Conclusions: We suggest renaming MALS as NCGAPS, nutcracker celiac ganglion abdominal pain syndrome, to better explain the mechanism of the recurrent abdominal pain. Further studies on the diagnostic cutoff of clinical and radiological scores of NCGAPS are needed not to miss the diagnosis of NCGAPS.

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation.

BACKGROUND: Pulse transit time and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to use these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP. OBJECTIVE: This study aims to predict systolic blood pressure and diastolic blood pressure based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models. METHODS: This study was conducted on 1362 healthy men older than 18 years who visited the Samsung Medical Center. The systolic blood pressure and diastolic blood pressure were estimated using the multiple linear regression method. Models were divided into two groups based on age: younger than 60 years and 60 years or older; 200 seeds were repeated in consideration of partition bias. Mean of error, absolute error, and root mean square error were used as performance metrics. RESULTS: The model divided into two age groups (younger than 60 years and 60 years and older) performed better than the model without division. The performance difference between the model using only three variables (PWV, BMI, age) and the model using 17 variables was not significant. Our final model using PWV, BMI, and age met the criteria presented by the American Association for the Advancement of Medical Instrumentation. The prediction errors were within the range of about 9 to 12 mmHg that can occur with a gold standard mercury sphygmomanometer. CONCLUSIONS: Dividing age based on the age of 60 years showed better BP prediction performance, and it could show good performance even if only PWV, BMI, and age variables were included. Our final model with the minimal number of variables (PWB, BMI, age) would be efficient and feasible for predicting BP.

The Relationship Between Breast Density Change During Menopause and the Risk of Breast Cancer in Korean Women.

BACKGROUND: The aim of this study was to investigate the relationship between changes in breast density during menopause and breast cancer risk. METHODS: This study was a retrospective, longitudinal cohort study for women over 30 years of age who had undergone breast mammography serially at baseline and postmenopause during regular health checkups at Samsung Medical Center. None of the participants had been diagnosed with breast cancer at baseline. Mammographic breast density was measured using the American College of Radiology Breast Imaging Reporting and Data System. RESULTS: During 18,615 person-years of follow-up (median follow-up 4.8 years; interquartile range 2.8-7.5 years), 45 participants were diagnosed with breast cancer. The prevalence of dense breasts was higher in those who were younger, underweight, had low parity or using contraceptives. The cumulative incidence of breast cancer increased 4 years after menopause in participants, and the consistently extremely dense group had a significantly higher cumulative incidence (CI) of breast cancer compared with other groups [CI of extremely dense vs. others (incidence rate per 100,000 person-years): 375 vs. 203, P < 0.01]. CONCLUSION: Korean women whose breast density was extremely dense before menopause and who maintained this density after menopause were at two-fold greater risk of breast cancer. PREVENTION RELEVANCE: Extremely dense breast density that is maintained persistently from premenopause to postmenopause increases risk of breast cancer two fold in Korean women. Therefore, women having risk factors should receive mammography frequently and if persistently extremely dense breast had been detected, additional modalities of BC screening could be considered.

Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department.

OBJECTIVES: The aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome. METHODS: The retrospective study included ED visits between January 2016 and December 2017 that resulted in either intensive care unit admission or emergency room death. We trained four classifiers using logistic regression and a deep learning model on INA and low dimensional (LD) INA, logistic regression on the Korea Triage and acuity scale (KTAS) and Sequential Related Organ Failure Assessment (SOFA). We varied the outcome ratio for external validation. Finally, variables of importance were identified using the random forest model's information gain. The four most influential variables were used for LD modeling for efficiency. RESULTS: A total of 86,304 patient visits were included, with an overall outcome rate of 3.5%. The area under the curve (AUC) values for the KTAS model were 76.8 (74.9-78.6) with logistic regression and 74.0 (72.1-75.9) for the SOFA model, while the AUC values of the INA model were 87.2 (85.9-88.6) and 87.6 (86.3-88.9) with logistic regression and deep learning, suggesting that the ML and INA-based triage system result more accurately predicted the outcomes. The AUC values for the LD model were 81.2 (79.4-82.9) and 80.7 (78.9-82.5) for logistic regression and deep learning, respectively. CONCLUSIONS: We developed an ML and INA-based triage system for EDs. The novel system was able to predict clinical outcomes more accurately than existing triage systems, KTAS and SOFA.