Association between Helicobacter pylori infection and overweight/obesity among adult health checkup population in China / 中华健康管理学杂志

Objective@#To explore the association between Helicobacter pylori (H. pylori) infection and overweight/obesity in a Chinese population.@*Methods@#This was a cross-sectional study that included all adult participants who underwent a 13C-urea breath test at the physical examination center in Tongji Hospital (Wuhan, China) in 2016. Data on demographic characteristics, anthropometric index, biochemical variables, and medical history were collected. Multivariate analyses were performed to assess the relationship between H. pylori infection and overweight/obesity, as well as body mass index (BMI).@*Results@#Of the 27 883 participants included, 17 585 were males and 10 298 were females. They were aged (43.94±11.31) years. The prevalence rate of H. pylori infection was 33.1%. The BMIs of subjects with and without H. pylori infection were (24.30±3.28) kg/m2 and (23.99±3.35) kg/m2, respectively (t=-7.28, P<0.001). After adjusting for age, sex, blood lipid levels, diabetes, and hypertension, the BMI of subjects with H. pylori infection was 0.120 kg/m2 (95% CI: 0.050-0.191, P=0.001), which was higher than that of subjects without H. pylori infection. Moreover, H. pylori infection was independently associated with a higher risk of prevalent overweight/obesity, with an odds ratio (OR) of 1.09 (95%CI: 1.03-1.16, P=0.004). The positive association between H. pylori infection and overweight/obesity was more evident among women, with an OR of 1.19 (95%CI: 1.07-1.31, P=0.001).@*Conclusion@#H. pylori infection was closely correlated with overweight/obesity. Control of H. pylori infection may be useful in reducing the heavy disease burden caused by overweight/obesity.

Relationship of age-related macular degeneration with blood-lipid levels in a population of middle-aged and elderly people undergoing health checkup / 中华健康管理学杂志

Objective To investigate the relationship of age-related macular degeneration (AMD) with blood-lipid levels. Methods Individuals 40 years old or older who had undergone a physical-health examination in our hospital between January and December 2017 were enrolled in this study. Information regarding medical history and the results of essential ophthalmological and physical-health examinations were examined to exclude individuals with serious chronic diseases such as malignant tumors, stroke, myocardial infarction, pulmonary heart disease, hypertension, diabetes, and kidney disease. One thousand nine individuals with AMD (all at the early stage) were included in the AMD group, and 3489 individuals without AMD were included in the non-AMD group. Data of all participants, including total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels, were collected and analyzed. Results The average age in the AMD group was higher than that in the non-AMD group, and the male to female ratio was significantly higher in the AMD group (P<0.05). After adjusting for age, gender, and BMI confounders, multiple linear stepwise regression analysis revealed that HDL-C was associated with AMD (β=-0.026, 95% CI: 0.045-0.006, P=0.011); there was no correlation between TC, TG, LDL-C, and AMD (all P>0.05). Conclusion Early stage AMD was related to a decrease in HDL-C, which may be a protective factor against AMD. Further study is warranted to validate this finding.

A De-identification Method for Bilingual Clinical Texts of Various Note Types

De-identification of personal health information is essential in order not to require written patient informed consent. Previous de-identification methods were proposed using natural language processing technology in order to remove the identifiers in clinical narrative text, although these methods only focused on narrative text written in English. In this study, we propose a regular expression-based de-identification method used to address bilingual clinical records written in Korean and English. To develop and validate regular expression rules, we obtained training and validation datasets composed of 6,039 clinical notes of 20 types and 5,000 notes of 33 types, respectively. Fifteen regular expression rules were constructed using the development dataset and those rules achieved 99.87% precision and 96.25% recall for the validation dataset. Our de-identification method successfully removed the identifiers in diverse types of bilingual clinical narrative texts. This method will thus assist physicians to more easily perform retrospective research.

Lessons Learned from Development of De-identification System for Biomedical Research in a Korean Tertiary Hospital / 대한의료정보학회지

OBJECTIVES: The Korean government has enacted two laws, namely, the Personal Information Protection Act and the Bioethics and Safety Act to prevent the unauthorized use of medical information. To protect patients' privacy by complying with governmental regulations and improve the convenience of research, Asan Medical Center has been developing a de-identification system for biomedical research. METHODS: We reviewed Korean regulations to define the scope of the de-identification methods and well-known previous biomedical research platforms to extract the functionalities of the systems. Based on these review results, we implemented necessary programs based on the Asan Medical Center Information System framework which was built using the Microsoft. NET Framework and C#. RESULTS: The developed de-identification system comprises three main components: a de-identification tool, a search tool, and a chart review tool. The de-identification tool can substitute a randomly assigned research ID for a hospital patient ID, remove the identifiers in the structured format, and mask them in the unstructured format, i.e., texts. This tool achieved 98.14% precision and 97.39% recall for 6,520 clinical notes. The search tool can find the number of patients which satisfies given search criteria. The chart review tool can provide de-identified patient's clinical data for review purposes. CONCLUSIONS: We found that a clinical data warehouse was essential for successful implementation of the de-identification system, and this system should be tightly linked to an electronic Institutional Review Board system for easy operation of honest brokers. Additionally, we found that a secure cloud environment could be adopted to protect patients' privacy more thoroughly.

Correction: Lessons Learned from Development of De-identification System for Biomedical Research in a Korean Tertiary Hospital / 대한의료정보학회지

We have noticed an inadvertent error in our article. In Figure 1, an abbreviation is misspelled.

Construction and Validation of Hospital-Based Cancer Registry Using Various Health Records to Detect Patients with Newly Diagnosed Cancer: Experience at Asan Medical Center / 예방의학회지

OBJECTIVES: An accurate estimation of cancer patients is the basis of epidemiological studies and health services. However in Korea, cancer patients visiting out-patient clinics are usually ruled out of such studies and so these studies are suspected of underestimating the cancer patient population. The purpose of this study is to construct a more complete, hospital-based cancer patient registry using multiple sources of medical information. METHODS: We constructed a cancer patient detection algorithm using records from various sources that were obtained from both the in-patients and out-patients seen at Asan Medical Center (AMC) for any reason. The medical data from the potentially incident cancer patients was reviewed four months after first being detected by the algorithm to determine whether these patients actually did or did not have cancer. RESULTS: Besides the traditional practice of reviewing the charts of in-patients upon their discharge, five more sources of information were added for this algorithm, i.e., pathology reports, the national severe disease registry, the reason for treatment, prescriptions of chemotherapeutic agents and radiation therapy reports. The constructed algorithm was observed to have a PPV of 87.04%. Compared to the results of traditional practice, 36.8% of registry failures were avoided using the AMC algorithm. CONCLUSIONS: To minimize loss in the cancer registry, various data sources should be utilized, and the AMC algorithm can be a successful model for this. Further research will be required in order to apply novel and innovative technology to the electronic medical records system in order to generate new signals from data that has not been previously used.