The relationship between metabolic syndrome and the incidence of colorectal cancer

OBJECTIVES@#This study evaluated the incidence of colorectal cancer (CRC) according to the number of metabolic syndrome (MetS) components.@*METHODS@#Using health checkup and insurance claims data of 6,365,409 subjects, the occurrence of CRC according to stage of MetS by sex was determined from the date of the health checkup in 2009 until December 31, 2018.@*RESULTS@#Cumulative incidence rates (CIR) of CRC in men and women was 3.9 and 2.8 per 1000 (p < 0.001), respectively. CIR of CRC for the normal, pre-MetS, and MetS groups in men was 2.6, 3.9, and 5.5 per 1000 (p < 0.001) and CIR in women was 2.1, 2.9, and 4.5 per 1000 (p < 0.001), respectively. Compared with the normal group, the hazard ratio (HR) of CRC for the pre-MetS group was 1.25 (95% CI 1.17-1.33) in men and 1.09 (95% CI 1.02-1.17) in women, and the HR of CRC for the MetS group was 1.54 (95% CI 1.43-1.65) in men and 1.39 (95% CI 1.26-1.53) in women after adjustment.@*CONCLUSIONS@#We found that MetS is a risk factor for CRC in this study. Therefore, the prevention and active management of MetS would contribute to the prevention of CRC.

An Automated Measurement of Ciliary Beating Frequency using a Combined Optical Flow and Peak Detection / 대한의료정보학회지

OBJECTIVES: The mucociliary transport system is a major defense mechanism of the respiratory tract. The performance of mucous transportation in the nasal cavity can be represented by a ciliary beating frequency (CBF). This study proposes a novel method to measure CBF by using optical flow. METHODS: To obtain objective estimates of CBF from video images, an automated computer-based image processing technique is developed. This study proposes a new method based on optical flow for image processing and peak detection for signal processing. We compare the measuring accuracy of the method in various combinations of image processing (optical flow versus difference image) and signal processing (fast Fourier transform [FFT] vs. peak detection [PD]). The digital high-speed video method with a manual count of CBF in slow motion video play, is the gold-standard in CBF measurement. We obtained a total of fifty recorded ciliated sinonasal epithelium images to measure CBF from the Department of Otolaryngology. The ciliated sinonasal epithelium images were recorded at 50-100 frames per second using a charge coupled device camera with an inverted microscope at a magnification of x1,000. RESULTS: The mean square errors and variance for each method were 1.24, 0.84 Hz; 11.8, 2.63 Hz; 3.22, 1.46 Hz; and 3.82, 1.53 Hz for optical flow (OF) + PD, OF + FFT, difference image [DI] + PD, and DI + FFT, respectively. Of the four methods, PD using optical flow showed the best performance for measuring the CBF of nasal mucosa. CONCLUSIONS: The proposed method was able to measure CBF more objectively and efficiently than what is currently possible.

A Data Warehouse Based Retrospective Post-marketing Surveillance Method: A Feasibility Test with Fluoxetine / 대한의료정보학회지

OBJECTIVE: Post-marketing surveillance (PMS) is an adverse events monitoring practice of pharmaceutical drugs on the market. Traditional PMS methods are labor intensive and expensive to perform, because they are largely based on manual work including phone-calling, mailing, or direct visits to relevant subjects. The objective of this study was to develop and validate a PMS methodology based on the clinical data warehouse (CDW). METHODS: We constructed a archival DB using a hospital information system and a refined CDW from three different hospitals. Fluoxetine hydrochloride, an antidepressant, was selected as the target monitoring drug. Corrected QT prolongation on ECG was selected as the target adverse outcome. The Wilcoxon signed rank test was performed to analyze the difference in the corrected QT interval before and after the target drug administration. RESULTS: A refined CDW was successfully constructed from three different hospitals. Table specifications and an entity-relation diagram were developed and are presented. A total of 13 subjects were selected for monitoring. There was no statistically significant difference in the QT interval before and after target drug administration (p=0.727). CONCLUSION: The PMS method based on CDW was successfully performed on the target drug. This IT-based alternative surveillance method might be beneficial in the PMS environment of the future.

A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis / 대한의료정보학회지

OBJECTIVE: Breast cancer is one of the most common cancers affecting women. Both physicians and patients have concerned about breast cancer survivability. Many researchers have studied the breast cancer survivability applying artificial nerural network model (ANN). Usually ANN model outperformed in classification of breast cancer survivability than other models such as logistic regression, Bayesian network (BN), or decision tree models. However, physicians in the fields hesitate to use ANN model, because ANN is a black-box model, and hard to explain the classification result to patients. In this study, we proposed a hybrid model with a degree of the accuracy and interpretation by combining the ANN for accuracy and BN for interpretation. METHODS: We developed an artificial neural network, a Bayesian network, and a hybrid Bayesian network model to predict breast cancer prognosis. The hybrid model combined the artificial neural network and the Bayesian network to obtain a good estimation of prognosis as well as a good explanation of the results. The National Cancer Institute's SEER program public-use data (1973-2003) were used to construct and evaluate the proposed models. Nine variables, which are clinically acceptable, were selected for input to the proposed models' nodes. A confidence value of the neural network served as an additional input node to the hybrid Bayesian network model. Ten iterations of random subsampling were performed to evaluate performance of the models. RESULTS: The hybrid BN model achieved the highest area under the curve value of 0.935, whereas the corresponding values of the neural network and Bayesian network were 0.930 and 0.813, respectively. The neural network model achieved the highest prediction accuracy of 88.8% with a sensitivity of 93.7% and a specificity of 85.4%. The hybrid Bayesian network model achieved a prediction accuracy of 87.2% with a sensitivity of 93.3% and a specificity of 83.1%. The results of the hybrid Bayesian network model were very similar to the neural network model. CONCLUSION: In the experiments, the hybrid model and the ANN model outperformed the Bayesian network model. The proposed hybrid BN model for breast cancer prognosis predictin may be useful for clinicians in the medical fields, as the model provides both high degree of performance inherited from ANN and good explanation power from BN.