RESUMO
ObjectiveTo develop and validate a predictive risk model for vision-threatening diabetic retinopathy in patients with type 2 diabetes using readily accessible clinical data， which may provide a convenient and effective prediction tool for early identification and referral of at-risk populations. MethodsA nomogram model was developed using a dataset obtained from patients with T2DM who participated in the Guangzhou Diabetic Eye Study from November 2017 to December 2020. Logistic regression was used to construct the model， and model performance was evaluated using receiver operating characteristic curve， Hosmer-Lemeshow test， calibration curve and decision curve analysis. The model underwent internal validation through the mean AUC of k-fold cross-validation method， and further external validation was conducted in the Dongguan Eye Study. ResultsA total of 2 161 individuals were included in the model development dataset， of whom 135 （6.25%） people were diagnosed with VTDR. Age （P＜0.001，OR=0.927，95%CI：0.898~0.957） and body mass index （P＜0.001，OR =0.845，95%CI：0.821~0.932） were found to be negatively correlated with VTDR， whereas diabetes duration （P＜0.001，OR=1.064，95%CI：1.035~1.094）， insulin use （P =0.045，OR =1.534，95%CI：1.010~2.332）， systolic blood pressure （P＜0.001，OR =1.019，95%CI：1.008~1.029）， glycated hemoglobin （P＜0.001，OR =1.484，95%CI：1.341~1.643）， and serum creatinine （P＜0.001，OR =1.017，95%CI：1.010~1.023） were positively correlated with VTDR. All these variables were included in the model as predictors. The model showed strong discrimination in the development dataset with an area under the receiver operating characteristic curve （AUC） of 0.797 and in the external validation dataset （AUC 0.762）. The Hosmer-Lemeshow test（P＞0.05）and the calibration curve displayed good agreement. Decision curve analysis showed that the nomogram produced net benefit in the two datasets. ConclusionsIndependent factors influencing VTDR include age， duration of diabetes mellitus， insulin use， body mass index， systolic blood pressure， glycosylated hemoglobin， and serum creatinine. The nomogram constructed using these variables demonstrates a high degree of predictive validity. The model can serve as a valuable tool for early detection and referral of VTDR in primary care clinics. Therefore， its application and promotion are highly recommended.
RESUMO
Objective To research the prevalence model of hepatitis B virus in the minority areas of Guizhou and to provide reference for the prevention and control virus of hepatitis B virus. Methods Using multi-stage cluster simple random sampling, four villages were selected from Leishan and Libo counties in minority areas of Guizhou. Questionnaires were investigated by trained investigators and serum hepatitis B virus five-item test results were collected from the subjects. Results A total of 1 629 participants were surveyed, the outcome showed that migrant workers’ infection rate of hepatitis B was 44.8%, and the carrying rate of HBsAg was 8.4%. The positive rates of anti-HBs and anti-HBc were 28.0% and 25.6% respectively. The infection model rate of migrant workers was 19.8%, which was lower than that of non-migrant workers (23.2%) (P>0.05).The detection rate of susceptible model in migrant workers (52.2%) was higher than that in non-migrant workers (43.4%), while the detection rate of immune mode migrant workers (28.0%) was lower than that in non-migrant workers (33.4%),which the difference was statistically significant (all P<0.05). After adjusted related factors by multivariate Logistic regression analysis model, migrant workers were still the influencing factors of vulnerability model (OR=1.568, 95% CI:1.206-2.039) compared with non-migrant workers. Conclusion There was a high susceptibility to hepatitis B virus among migrant workers in minority areas of Guizhou, In order to reduce the infection and prevalence of hepatitis B virus, we should strengthen the immunization of hepatitis B vaccine to migrant workers and to improve their specific immunity.