Objective:To understand the prevalence of frailty and the importance of its influencing factors in adult population in Shaanxi Province.Methods:The data were from Shaanxi baseline survey of natural population cohort study in northwest China during 2018-2019. The frailty index (FI) was constructed to evaluate the frailty status of the population, and XGboost model combined with Shapley method was used to analyze the importance of the sociodemographic and life behavior factors affecting the prevalence of frailty by gender and age.Results:A total of 25 079 subjects were included, in whom 964 (3.8%) had frailty, and there was no significant difference in the overall prevalence of frailty between women (3.9%) and men (3.8%) ( P=0.629), but there was a gender specific difference in the distribution of FI ( P<0.001), and the proportion of the pre-frailty in men was higher than that in women. The prevalence of frailty increased with age ( P<0.001), the prevalence of frailty were 1.3%, 2.5% and 7.8% in young, middle-aged and elderly women, respectively, and 1.9%, 2.7% and 5.5% in young, middle-aged and elderly men, respectively. Sociodemographic characteristics and lifestyle patterns were both influencing factors for the prevalence of frailty, but their importance varied with gender and age. The top five contributing factors were education level, staying up late, annual family income level, sedentary time and marital status in young women, and staying up late, smoking, annual family income level, sedentary time and drinking in young men. The top five contributing factors were education level, annual family income level, passive exposure to smoking, staying up late, and sedentary time in middle-aged women, and annual family income level, education level, sedentary time, staying up late and drinking in middle-aged men. The top five contributing factors were annual family income level, passive exposure to smoking, sedentary time, marital status, and smartphone use in elderly women, and education level, annual family income level, smoking, smartphone use and sedentary time in elderly men. Conclusions:There are gender specific differences in the distribution of FI in Shaanxi. The prevalence of frailty increased with age, but young and middle-aged people also have frailty risk. The prevalence of frailty in young men was mainly related to unhealthy life behaviors, such as staying up late, smoking, sedentary behavior and drinking, while the prevalence of frailty in middle-aged and elderly men and women were more affected by sociodemographic factors, such as education level, economic status and marital status.

Objective:To understand the prevalence of frailty and the importance of its influencing factors in adult population in Shaanxi Province.Methods:The data were from Shaanxi baseline survey of natural population cohort study in northwest China during 2018-2019. The frailty index (FI) was constructed to evaluate the frailty status of the population, and XGboost model combined with Shapley method was used to analyze the importance of the sociodemographic and life behavior factors affecting the prevalence of frailty by gender and age.Results:A total of 25 079 subjects were included, in whom 964 (3.8%) had frailty, and there was no significant difference in the overall prevalence of frailty between women (3.9%) and men (3.8%) ( P=0.629), but there was a gender specific difference in the distribution of FI ( P<0.001), and the proportion of the pre-frailty in men was higher than that in women. The prevalence of frailty increased with age ( P<0.001), the prevalence of frailty were 1.3%, 2.5% and 7.8% in young, middle-aged and elderly women, respectively, and 1.9%, 2.7% and 5.5% in young, middle-aged and elderly men, respectively. Sociodemographic characteristics and lifestyle patterns were both influencing factors for the prevalence of frailty, but their importance varied with gender and age. The top five contributing factors were education level, staying up late, annual family income level, sedentary time and marital status in young women, and staying up late, smoking, annual family income level, sedentary time and drinking in young men. The top five contributing factors were education level, annual family income level, passive exposure to smoking, staying up late, and sedentary time in middle-aged women, and annual family income level, education level, sedentary time, staying up late and drinking in middle-aged men. The top five contributing factors were annual family income level, passive exposure to smoking, sedentary time, marital status, and smartphone use in elderly women, and education level, annual family income level, smoking, smartphone use and sedentary time in elderly men. Conclusions:There are gender specific differences in the distribution of FI in Shaanxi. The prevalence of frailty increased with age, but young and middle-aged people also have frailty risk. The prevalence of frailty in young men was mainly related to unhealthy life behaviors, such as staying up late, smoking, sedentary behavior and drinking, while the prevalence of frailty in middle-aged and elderly men and women were more affected by sociodemographic factors, such as education level, economic status and marital status.

Objective:To determine the best scoring system for assessing the severity of perioperative aortic dissection.Methods:All data were obtained from the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ) database in the United States. The predictive value of the Acute Physiology Score Ⅲ(APS Ⅲ), Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score Ⅱ(SAPS Ⅱ), and Charlson Comorbidity Index (CCI) scoring systems were evaluated using the receiver operating characteristic ( ROC) curve. The area under the curve ( AUC) was used to determine the best predictive score, and the ideal cutoff value of the score was calculated based on the Youden index. Patients were divided into high and low groups according to the cutoff value. The Kaplan- Meier curve was used to show the impact on the survival rate of patients with aortic dissection. Results:ROC curve analysis showed that APS Ⅲ( AUC: 0.803, 95% CI: 0.721-0.885) was superior to SAPS Ⅱ( AUC: 0.767, 95% CI: 0.654-0.880), OASIS( AUC: 0.760, 95% CI: 0.635-0.885), SOFA( AUC: 0.753, 95% CI: 0.649-0.857), and CCI( AUC: 0.670, 95% CI: 0.524-0.817) in assessing in-hospital mortality. Based on the ROC curve and the Youden index calculation, the ideal cutoff value of the APS Ⅲ score was 57.5. Kaplan- Meier survival analysis showed that patients in the high group of APS Ⅲ had a shorter 28-day survival time. Patients in the high group of APS Ⅲ had a higher incidence of postoperative complications, and correlation analysis showed that patients in the high group of APS Ⅲ had a longer hospital stay. Conclusion:The APS Ⅲ scoring system is more valuable in predicting the 28-day mortality and prognosis of patients with aortic dissection.

Objective:To determine the best scoring system for assessing the severity of perioperative aortic dissection.Methods:All data were obtained from the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ) database in the United States. The predictive value of the Acute Physiology Score Ⅲ(APS Ⅲ), Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score Ⅱ(SAPS Ⅱ), and Charlson Comorbidity Index (CCI) scoring systems were evaluated using the receiver operating characteristic ( ROC) curve. The area under the curve ( AUC) was used to determine the best predictive score, and the ideal cutoff value of the score was calculated based on the Youden index. Patients were divided into high and low groups according to the cutoff value. The Kaplan- Meier curve was used to show the impact on the survival rate of patients with aortic dissection. Results:ROC curve analysis showed that APS Ⅲ( AUC: 0.803, 95% CI: 0.721-0.885) was superior to SAPS Ⅱ( AUC: 0.767, 95% CI: 0.654-0.880), OASIS( AUC: 0.760, 95% CI: 0.635-0.885), SOFA( AUC: 0.753, 95% CI: 0.649-0.857), and CCI( AUC: 0.670, 95% CI: 0.524-0.817) in assessing in-hospital mortality. Based on the ROC curve and the Youden index calculation, the ideal cutoff value of the APS Ⅲ score was 57.5. Kaplan- Meier survival analysis showed that patients in the high group of APS Ⅲ had a shorter 28-day survival time. Patients in the high group of APS Ⅲ had a higher incidence of postoperative complications, and correlation analysis showed that patients in the high group of APS Ⅲ had a longer hospital stay. Conclusion:The APS Ⅲ scoring system is more valuable in predicting the 28-day mortality and prognosis of patients with aortic dissection.

Objective:To investigate the relationship between triglyceride glucose index (TyG) and body inflammation.Methods:The data were obtained from a baseline survey in population in Xi'an in natural population cohort study in northwest China established in 2018-2019. Based on TG and FPG, TyG/TyG-BMI was constructed to reflect insulin resistance (IR) in the body, and systemic immune-inflammation index (SII) reflecting inflammation in the body was constructed using neutrophil, lymphocyte, and platelet counts. A logistic regression model was used to explore the relationship between the TyG and the SII.Results:A total of 11 491 subjects were included in the analysis. After adjusting for covariates, each unit increase in the TyG increased the risk of high SII by 21% ( OR=1.21, 95% CI:1.12-1.30). The risk of high SII in the group with the TyG in Q4 was 1.34 times higher than that in the group Q1 ( OR=1.34, 95% CI:1.18-1.52). Both sensitivity analysis and subgroup analysis further confirmed the stability of the association between the TyG and the SII. In the population with a BMI ranging from 18.5 to 23.9 kg/m 2, for every unit increase in the TyG as a continuous variable, the risk for high SII increased by 31% ( OR=1.31, 95% CI:1.18-1.45). As a categorical variable, the risk for high SII in the Q4 group was 1.52 times higher than that in the Q1 group ( OR=1.52, 95% CI:1.27-1.83). In a population with BMIs ranging from 24.0 to 27.9 kg/m 2, for every unit increase in the TyG as a continuous variable, the risk for high SII increased by 20% ( OR=1.20, 95% CI:1.07-1.35), and there was no significant difference when it was a categorical variable. Conclusions:The increase in IR is closely related to the development of inflammation in the body, and BMI may regulate their relationship. Early prevention of elevated IR levels before overweight or obesity may have a positive effect on the control of inflammation in the body.

Objective:By analyzed the transmission patterns of 4 out of the 51 COVID-19 cluster cases in Shaanxi province to provide evidences for the COVID-19 control and prevention.Methods:The epidemiological data of RT-PCR test-confirmed COVID-19 cases were collected. Transmission chain was drawn and the transmission process was analyzed.Results:Cluster case 1 contained 13 cases and was caused by a family of 5 who traveled by car to Wuhan and returned to Shaanxi. Cluster case 2 had 5cases and caused by initial patient who participated family get-together right after back from Wuhan while under incubation period. Cluster case 3 contained 10 cases and could be defined as nosocomial infection. Cluster case 4 contained 4 cases and occurred in work place.Conclusion:Higher contact frequency and smaller places were more likely to cause a small-scale COVID-19 cluster outbreak, with potential longer incubation period. COVID-19 control strategies should turn the attention to infection prevention and control in crowded places, management of enterprise resumption and prevention of nosocomial infection.

Objective:To understand the incidence trend and epidemiological characteristics of COVID-19 in Shaanxi province.Methods:The incidence data of COVID-19 reported in Shaanxi as of 22 February, 2020 were collected for an epidemiological descriptive analysis.Results:A total of 245 confirmed cases of COVID-19 were reported in Shaanxi. Most cases were mild (87.76%). As time passed, the areas where confirmed cases were reported continued to increase. The case number in Xi’an was highest, accounting for nearly half of the total reported cases in the province. The epidemic pattern in Shaanxi had gradually shifted from imported case pattern to local case pattern, and the transmission of local cases was mainly based on family cluster transmission. The confirmed cases from different sources had caused the secondary transmission in Shaanxi. After February 7, the number of reported cases began to fluctuate and decrease stably, indicating a decrease-to-zero period.Conclusions:At present, the overall epidemic of COVID-19 in Shaanxi has gradually been mitigated. However, considering the approaching of return to work and study and the increasing of imported cases from other countries, the prevention and control of COVIS-19 in Shaanxi will face new challenges.

Cohort study is one of the basic methods used in epidemiological research. With the development of the etiological analysis of complex diseases such as cardiovascular diseases, large natural population-based cohort study has become a popular topic in medical research. In the process of cohort development, one of the important issues is to ensure the efficiency and safety on data collection. As a database management system, with open source, free clinical research data collection and high quality, REDCap can widely be applied in large population-based cohort studies. This article summarizes the baseline survey and follow-up procedures on cohort studies and introduces a REDCap-system-based solution for data collection and management. Contents on the establishment of data working groups, data collection, cohort follow-up methods and field application are also discussed in this paper, in order to improve the efficiency of data collection and management in cohort study to help the development of cohort study in China.