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