Objective To investigate the characteristics of heart rate variability(HRV)in obese patients with severe obstructive sleep apnea hypopnea syndrome(OSAHS).Methods We retrospectively analyzed 78 patients with severe OSAHS diagnosed by polysomnography(PSG)in The Second Affiliated Hospital of Xi'an Jiaotong University from April 2018 to May 2022.According to body mass index(BMI),the patients were divided into obese with severe OSAHS group(43 cases)and non-obese with severe OSAHS group(35 cases).All patients received 24-hour Holter monitoring while on polysomnography monitoring.The differences in HRV indexes between the groups and the correlation between HRV and clinical indicators were analyzed.Results In terms of basic data and PSG indexes,the analysis results showed that compared with those in the non-obese OSAHS group,weight,BMI,neck circumference,waist circumference,and AHI in obese with severe OSAHS group were significantly higher,while the standard deviation of the 24-hour normal R-R interval(SDNN),the standard deviation of the 5-minute mean(SDANN),the triangle index(TI),the heart rate deceleration force(DC),the standard deviation of the normal R-R interval(awake SDNN),and high frequency during sleep in the obese with severe OSAHS group were significantly lower(P＜0.05).The correlation results showed that among obese with severe OSAHS patients,root mean square of the difference of adjacent R-R interval(rMSSD)was negatively correlated with the course of hypertension;TI and DC were negatively correlated with AHI.After adjusting for neck circumference and waist circumference,the linear regression analysis showed that SDNN,SDANN,and rMSSD were correlated with systolic blood pressure(P＜0.05).Conclusion There is significant decrease in HRV index in obese patients with severe OSAHS,suggesting that deterioration of cardiac autonomic nervous regulation function may increase the risk of cardiovascular disease.

To know the current status of family health and negative psychology of primary and secondary school students, and to explore the correlation between them during the prevention and control of COVID-19. From January 15 to 30, in 2022, a total of 19 343 urban and rural primary and secondary school students in X city were selected. The short form of the family health scale, center for epidemiologica survey-depression scale and student burnout inventory for junior middle school students were used to conduct a cross-sectional survey. The Pearson correlation was used to analyze the relationship between the family health and negative psychology. The family health of primary and secondary school students is at the medium level or above, and more than half of students may/must be depressed. There are significant differences in study burnout in different learning stages and epidemic management in different places of residence. It is recommended that family members and schools staff should give more psychological and social support to primary and secondary school students to reduce the negative impact of COVID-19 on them.

【Objective】 To construct a prediction model of severe obstructive sleep apnea (OSA) risk in the general population by using nomogram in order to explore the independent risk factors of severe OSA and guide the early diagnosis and treatment. 【Methods】 We retrospectively enrolled patients who had been diagnosed by polysomnography and divided them into training and validation sets at the ratio of 7∶3. Patients were divided into severe OSA group and non-severe OSA group according to apnea hypopnea index (AHI)>30. Variables entering the model were identified by least absolute shrinkage and selection operator regression model (Lasso), and logistic regression (LR) method. Then, multivariable logistic regression analysis was used to establish the nomogram, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative properties of the nomogram model. Finally, we conducted decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire and Berlin questionnaire to assess clinical utility. 【Results】 Through single factor and multiple factor logistic regression analyses, the independent risk factors for severe OSA were screened out, including moderate and severe sleepiness, family history of hypertension, history of smoking, drinking, snoring, history of suffocation, sedentary lifestyle, male, age, body mass index (BMI), waist and neck circumference. Lasso logistic regression identified smoke, suffocation time, snoring time, waistline, Epworth sleepiness scale (ESS) and BMI as predictive factors for inclusion in the nomogram. The AUC of the model was 0.795 [95% confidence interval (CI): 0.769-0.820] . Hosmer-Lemeshow test indicated that the model was well calibrated (χ²=3.942, P=0.862). The DCA results on the visual basis confirmed that the nomogram had superior overall net benefits within a wide, practical threshold probability range which displayed the nomogram was higher than that of STOP-Bang questionnaire and Berlin questionnaire, which is clinically useful. The Clinical Impact Curve (CIC) analysis showed the clinical effectiveness of the prediction model when the threshold probability was greater than 82% of the predicted score probability value. The prediction model determined that the high-risk population with severe OSA was highly matched with the actual population with severe OSA, which confirmed the high clinical effectiveness of the prediction model. 【Conclusion】 The model performed better than STOP-Bang questionnaire and Berlin questionnaire in predicting severe OSA and can be applied to screening. And it can be helpful to the early diagnosis and treatment of OSA in order to reduce social burden.