Objectives:To describe the use of antihypertensive, antidiabetic and lipid-lowering drugs, and evaluate the effects on blood pressure, blood glucose and blood lipids controls required by Chinese Guideline on the Primary Prevention of Cardiovascular Diseases (the guideline) in a community-based cohort of individuals at high risk for cardiovascular disease. To analyze the association of the uses of antihypertensive, antidiabetic and lipid-lowering drugs, and the comprehensive control of blood pressure, blood glucose and blood lipids with cardiovascular disease. Methods:From the CHinese Electronic health Records Research in Yinzhou (CHERRY), those who were at high risk for cardiovascular disease and aged 40-75 years as of January 1, 2013 in in Yinzhou District of Ningbo, Zhejiang Province were selected as study subjects. The information about their antihypertensive, antidiabetic, and lipid-lowering drug uses between 2013 and 2015 was collected, and blood pressure, blood glucose, and blood lipid measurements were conducted during the follow-up. The study constructed two kinds of comprehensive scores: the comprehensive medication score based on the guideline requirement for the treatment of hypertension, diabetes and hyperlipidemia, dividing the study participants into the compliancy group and non-compliancy group; and the comprehensive control score based on the guideline requirement for blood pressure, blood glucose, and blood lipids control, dividing the study participants into better control group, moderate control group, and poor control group. Cox proportional hazards regression model was used to analyze the association of the comprehensive medication score and comprehensive control score with cardiovascular disease. The incidence data of cardiovascular disease were collected from January 1, 2015 (baseline time) to August 31, 2020 (follow up end time).Results:A total of 79 734 participants at high risk for cardiovascular disease were included in the study, in whom 68.4%, 27.4%, and 4.2% had 1, 2, or 3 cardiometabolic conditions (hypertension, diabetes, or hyperlipidemia), respectively. In the participants with hypertension, diabetes, and hyperlipidemia from 2013 to 2015, the proportions of those who had two years of medication compliancy records were 66.0%, 67.4%, and 13.9%, respectively. In the hypertension patients, 59.2% had better blood pressure control, in the diabetes patients, 28.7% had better blood glucose control, and in the patients with hyperlipidemia, 27.4% had better blood lipid control. After a median follow-up of 5.66 years, 4 088 cardiovascular disease cases or deaths occurred. After multivariate adjustment, compared with the non-compliancy group, the compliancy group had lower risk for cardiovascular disease ( HR=0.91, 95% CI: 0.85-0.96). Compared with the better control group, the poor control group had an increased risk for cardiovascular disease ( HR=1.67, 95% CI: 1.53-1.81). In the moderate control group, the risk increased significantly in the diabetes patients ( HR=1.29, 95% CI: 1.07-1.56), while no additional risk for cardiovascular disease was observed in non-diabetes patients ( HR=1.06, 95% CI: 0.97-1.16). Conclusions:Compliancy to the medication required by the guideline is associated with lower risk for cardiovascular disease. However, it is still necessary to improve the medication compliancy in people at high risk in primary prevention, especially in the patients with hyperlipidemia, due to their low taking rate of lipid-lowering drugs. Additionally, as the requirement of the guideline becomes more stringent, the management of disease has met more challenges. Notably, diabetes patients who have not met the guideline requirement are at high risk for cardiovascular disease, to whom the disease management should be strengthened.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Objectives:To describe the use of antihypertensive, antidiabetic and lipid-lowering drugs, and evaluate the effects on blood pressure, blood glucose and blood lipids controls required by Chinese Guideline on the Primary Prevention of Cardiovascular Diseases (the guideline) in a community-based cohort of individuals at high risk for cardiovascular disease. To analyze the association of the uses of antihypertensive, antidiabetic and lipid-lowering drugs, and the comprehensive control of blood pressure, blood glucose and blood lipids with cardiovascular disease. Methods:From the CHinese Electronic health Records Research in Yinzhou (CHERRY), those who were at high risk for cardiovascular disease and aged 40-75 years as of January 1, 2013 in in Yinzhou District of Ningbo, Zhejiang Province were selected as study subjects. The information about their antihypertensive, antidiabetic, and lipid-lowering drug uses between 2013 and 2015 was collected, and blood pressure, blood glucose, and blood lipid measurements were conducted during the follow-up. The study constructed two kinds of comprehensive scores: the comprehensive medication score based on the guideline requirement for the treatment of hypertension, diabetes and hyperlipidemia, dividing the study participants into the compliancy group and non-compliancy group; and the comprehensive control score based on the guideline requirement for blood pressure, blood glucose, and blood lipids control, dividing the study participants into better control group, moderate control group, and poor control group. Cox proportional hazards regression model was used to analyze the association of the comprehensive medication score and comprehensive control score with cardiovascular disease. The incidence data of cardiovascular disease were collected from January 1, 2015 (baseline time) to August 31, 2020 (follow up end time).Results:A total of 79 734 participants at high risk for cardiovascular disease were included in the study, in whom 68.4%, 27.4%, and 4.2% had 1, 2, or 3 cardiometabolic conditions (hypertension, diabetes, or hyperlipidemia), respectively. In the participants with hypertension, diabetes, and hyperlipidemia from 2013 to 2015, the proportions of those who had two years of medication compliancy records were 66.0%, 67.4%, and 13.9%, respectively. In the hypertension patients, 59.2% had better blood pressure control, in the diabetes patients, 28.7% had better blood glucose control, and in the patients with hyperlipidemia, 27.4% had better blood lipid control. After a median follow-up of 5.66 years, 4 088 cardiovascular disease cases or deaths occurred. After multivariate adjustment, compared with the non-compliancy group, the compliancy group had lower risk for cardiovascular disease ( HR=0.91, 95% CI: 0.85-0.96). Compared with the better control group, the poor control group had an increased risk for cardiovascular disease ( HR=1.67, 95% CI: 1.53-1.81). In the moderate control group, the risk increased significantly in the diabetes patients ( HR=1.29, 95% CI: 1.07-1.56), while no additional risk for cardiovascular disease was observed in non-diabetes patients ( HR=1.06, 95% CI: 0.97-1.16). Conclusions:Compliancy to the medication required by the guideline is associated with lower risk for cardiovascular disease. However, it is still necessary to improve the medication compliancy in people at high risk in primary prevention, especially in the patients with hyperlipidemia, due to their low taking rate of lipid-lowering drugs. Additionally, as the requirement of the guideline becomes more stringent, the management of disease has met more challenges. Notably, diabetes patients who have not met the guideline requirement are at high risk for cardiovascular disease, to whom the disease management should be strengthened.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Objective:To study the quality control management method of magnetic resonance equipment based on multi-constraint optimal programming model,and to improve the utilization efficiency and imaging effect of magnetic resonance imaging(MRI)equipment.Methods:By gridding the current carrying area of the superconducting magnet of magnetic resonance equipment,the total length of the superconducting material was calculated,and the optimal programming model of the magnetic resonance superconducting magnet was established with constraints set.The optimal programming results of the superconducting magnet was obtained by solving the model to achieve quality control of the magnetic resonance equipment.Taking the minimum scanning time of magnetic resonance equipment as the objective function,a refined management model of magnetic resonance equipment was established to realize the time planning for the use of magnetic resonance equipment.Using fuzzy set theory,the six magnetic resonance equipment in clinical use in Lishui Branch of Zhongda Hospital Southeast University from 2022 to 2023 was divided into five grades according to the imaging quality of magnetic resonance equipment,including excellent,good,average,below average and poor.The quality control management effect was evaluated from the uniformity of the superconducting magnet imaging magnetic field,the uniformity of 10 magnetic resonance images,the geometric distortion rate,and the total number of monthly scans.Results:After the implementation of the quality control management method for magnetic resonance equipment based on the multi-constraint optimal programming model,the maximum value of the magnetic field uniformity contour of the tested equipment was reduced from 15 ppm to 5 ppm;the uniformity test results of the 10 collected magnetic resonance images showed that 2 were good and 8 were excellent,all of which met the technical standards of magnetic resonance imaging;the mean value of geometric distortion rate of magnetic resonance imaging was at-0.5%～0,and the corresponding test grade was excellent;the number of monthly scans of the human body by the tested magnetic resonance equipment was higher than that before the quality control management,especially the number of lower limb scans increased from 607 times before the quality control management to 821 times,with an increase of 214 times.Conclusion:The quality control management method for magnetic resonance equipment based on multi-constraint optimal programming model can effectively improve the quality and control management level of magnetic resonance equipment,optimize the maintenance and management process of magnetic resonance equipment,improve equipment operation efficiency,and reduce operating costs.

Objective:To investigate the epidemiological characteristics of herpangina (HA) and its correlation with the incidence of hand, foot and mouth disease (HFMD) in children aged ≤6 years in Yinzhou District of Ningbo from 2017 to 2022.Methods:Epidemiological characteristics of HA in children aged ≤6 years were analyzed based on the electronic medical record data and public health management data from 2017 to 2022 collected from the Health Information Platform of Yinzhou. The incidence of HFMD was calculated using the infectious disease reporting data from the public health management data. Autoregressive integrated moving average model and cross-correlation function were used to evaluate the correlation between the incidence of HA and HFMD.Results:From 2017 to 2022, a total of 25 385 cases of HA were detected in children aged ≤6 years in Yinzhou, the male-to-female ratio of the cases was 1.12∶1. The average annual incidence of HA was 4 986.67/100 000, with the highest incidence in 2018 (10 477.09/100 000) and the lowest incidence in 2020 (870.88/100 000). The incidence peak of HA was during June to July. The incidence of HA was higher in age group 1 year (7 950.45/100 000) than in other age groups. The incidences of HA in Yunlong, Jiangshan and Xiaying were higher, with the incidence of 8 764.31/100 000, 8 377.58/100 000 and 7 965.31/100 000, respectively. The correlation coefficients between the incidence of HA and HFMD at lag day 0, 7, 12 and 18 were 0.199, 0.139, 0.090 and 0.086, respectively (all P<0.05). Conclusions:From 2017 to 2022, the incidence of HA was high in children aged ≤6 years in Yinzhou with obvious seasonality and area difference. The incidence of HA was correlated with the incidence of HFMD and the incidence of HFMD had certain lags. The comprehensive prevention and control of HA and HFMD should be further strengthened by prioritizing HA surveillance and implementing integrated surveillance and management of HA and HFMD.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To understand the current status of gastroscopy in diagnosing gastric lesions in general population, and to recommend the optimal age for the first gastroscopy and intervals for repeated gastroscopy.Methods:The gastroscopy records of residents aged 18-80 years in Yinzhou District of Ningbo, Zhejiang Province, between April 2010 and December 2021 were analyzed retrospectively. The detections of gastric lesions across different years, age and genders were described. Goodness of fit tests were applied to compare the differences in detection rates of different lesions in first-time endoscopy in different age groups and different populations. Generalized additive models were used to fit the trend of age specific gastric lesion detection rate explore the optimal age for gastroscopy. The appropriate gastroscopy intervals were determined according to the progress of the gastric lesions detected in repeated gastroscopy.Results:A total of 237 751 participants with 344 398 gastroscopy records were included in analyses. A total of 5 597 cases of chronic atrophic gastritis (CAG), 9 796 cases of intestinal metaplasia (IM), 165 cases of low-grade intraepithelial neoplasia (LGIN), 52 cases of high-grade intraepithelial neoplasia (HGIN) and 435 cases of gastric cancer were detected by the first gastroscopy. The overall detection rate of gastric lesions increased significantly in age group 45-70 years, and remained stable after 70 years old, with LGIN and HGIN showing notable increases at 50 and 55 years old, respectively. Repeated gastroscopy detected CAG, IM, LGIN, and HGIN at a higher rate compared with the first gastroscopy. Normal/superficial gastritis progressed in 3-5 years, whereas CAG or more severe lesions progressed in 1-6 years.Conclusion:Gastroscopy is recommended for general population aged 45 years and above. Furthermore, gastroscopy can be performed every 3-5 years for individuals with normal endoscopy results and once a year for patients with CAG or more severe gastric lesions.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.