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.

Clinical scoring system for the prediction of survival of patients with advanced gastric cancer.

OBJECTIVE: In this study, we established a risk scoring system using easily obtained clinical characteristics at the time of initiating palliative chemotherapy to predict accurate overall survival of patients with advanced gastric cancer after first-line treatment with fluoropyrimidine-platinum combination chemotherapy. METHODS: A total of 1733 patients treated at the Samsung Medical Center, Korea were included in the study, and clinicopathological and laboratory data were retrospectively analysed. The dataset was split into a training set (n=1156, 67%) and a validation set (n=577, 33%). Top-ranked variables were identified using the random forest survival algorithm and integrated into a Cox regression model, thereby constructing the scoring system for predicting the overall survival of patients with advanced gastric cancer. RESULTS: The following five variables were finally included in the scoring system: serum neutrophil-lymphocyte ratio, alkaline phosphatase level, albumin level, performance status and histologic differentiation. The scoring system determined four distinct risk groups in the validation dataset with median overall survival of 17.1 months (95% CI=14.9 to 20.5 months), 12.9 months (95% CI=11.4 to 14.6 months), 8.1 months (95% CI=5.3 to 12.3 months) and 3.9 months (95% CI=1.5 to 8.2 months), respectively. The area under the curve to estimate the discrimination performance of the scoring system was 66.1 considering 1 year overall survival. CONCLUSIONS: We developed a simple and clinically useful predictive scoring model in a homogeneous population with advanced gastric cancer treated with fluoropyrimidine-containing and platinum-containing chemotherapy. However, additional independent validation will be required before the scoring model can be used commonly.