Objective: To investigate the delay in identification, healthcare-seeking, and definitive diagnosis of tuberculosis among students in Urumqi City from 2010 to 2019, and to identify the influencing factors, so as to provide insights into tuberculosis control among students. Methods: The demographic and diagnosis data of tuberculosis patients in Urumqi City from 2010 to 2019 were captured from the Tuberculosis Information Management System of Chinese Disease Control and Prevention Information System. The delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis was analyzed among students, and the factors affecting the delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis were identified using a multivariable logistic regression model. Results: A total of 996 tuberculosis cases were identified among students in Urumqi City from 2010 to 2019. There were 702 students with delay in identification of tuberculosis (70.48%), 500 students with delay in healthcare-seeking (55.22%) and 534 students with delay in definitive diagnosis (53.61%). Multivariable logistic regression analysis identified active identification (OR=0.116, 95%CI: 0.032-0.420) as a factor affecting delay in identification of tuberculosis, women (OR=1.424, 95%CI: 1.104-1.836), non-local household registration (OR=1.311, 95%CI: 1.016-1.694) and active identification (OR=0.232, 95%CI: 0.064-0.848) as factors affecting delay in healthcare-seeking, and active identification (OR=0.143, 95%CI: 0.032-0.644) as a factor affecting delay in definitive diagnosis of tuberculosis among students. Conclusions There is a high proportion of delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis among students in Urumqi City from 2010 to 2019, and female and non-locally household-registered students were at a high risk of delay in healthcare-seeking for tuberculosis. Active detection and screening of tuberculosis should be reinforced.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.