Objective:This study aimed to construct a risk prediction model for obstructive sleep apnea（OSA） related hypertension based on the nomogram, and to explore the independent risk factors for OSA-related hypertension, so as to provide reference for clinical treatment decision-making. Methods:The clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 were collected retrospectively and randomly divided into training sets and validation sets. A total of 1 493 OSA patients with 27 variables were included. The least absolute shrinkage and selection operator（Lasso） logistic regression model was used to select potentially relevant features and establish a nomogram for OSA-related hypertension.The performance and clinical benefits of this nomogram were verified in terms of discrimination, calibration ability and clinical net benefit. Results:Multivariate logistic regression showed that body mass index（BMI）, family history of hypertension, lowest oxygen saturation（LSaO2）, age and cumulative percentage of total sleep time with oxygen saturation below 90% were independent risk factors for OSA-related hypertension. Lasso logistic regression identified BMI, family history of hypertension, LSaO2 and age as predictive factors for inclusion in the nomogram. The nomogram provided a favorable discrimination, with a C-indexes of 0.835（95% confidence interval[CI ]0.806-0.863） 0.865（95%CI 0.829-0.900） for the training and validation cohort, respectively, and well calibrated. The clinical decision curve analysis displayed that the nomogram was clinically useful. Conclusion:Compared with cumulative percentage of total sleep time with blood oxygen saturation below 90%, LSaO2 may have a greater impact on the incidence of OSA-related hypertension, and the effects of different times and degrees of hypoxia on OSA-related hypertension should be further explored in the future. Apnea hypopnea index involvement is weak in predicting OSA-related hypertension, and the blood oxygen index may be a better predictor variable. Furthermore, we established a risk prediction model for OSA-related hypertension patients using nomogram, and demonstrated that this prediction model was helpful to identify high-risk OSA-related hypertension patients. This model can provide early and individualized diagnosis and treatment plans, protect patients from the serious.
Humans ; Sleep Apnea, Obstructive/complications* ; Nomograms ; Hypertension/epidemiology* ; Male ; Female ; Risk Factors ; Middle Aged ; Retrospective Studies ; Polysomnography ; Logistic Models ; Body Mass Index ; Adult

【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.