Optimizing Obstructive Sleep Apnea Risk Assessment in Hypertension: Development of a Predictive Nomogram in China

Obstructive sleep apnea (OSA) is common in patients with hypertension. Our study aims to construct and validate an objective nomogram that can accurately predict the risk of OSA in patients with hypertension.

Retrospective data were collected from patients with hypertension who underwent polysomnography (PSG) at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University, China. All participants were assigned to the training group (used to develop the predictive model). Similarly, data from patients with hypertension who underwent PSG at the Sleep Medicine Center of the Second Affiliated Hospital of Guangdong Medical University, China, were collected, and these participants were assigned to the validation group (used to test the model's performance). Logistic and LASSO regression analyses were used to identify factors and construct the nomogram. C-index, calibration curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to assess the model. Finally, nomogram validation was performed in the validation group.

This study included a training group of 303 patients and a validation group of 217 patients. Based on LASSO and Logistic regression analyses and clinical practicality, we identified gender, age, BMI (body mass index), NC (neck circumference) and ESS (Epworth Sleepiness Scale) as predictors for the nomogram. The C-index is 0.840 in the training group and 0.808 in the validation group. The area under the curve (AUC) of the predictive model and STOP-Bang at the three diagnostic cut-off points of the Apnea-Hypopnea Index (AHI) ≥ 5, AHI ≥ 15 and AHI ≥ 30 were 0.840 vs 0.778, 0.754 vs 0.740, and 0.765 vs 0.751 respectively. The AUC at each intercept point was higher than that of STOP-Bang. DCA and CICA showed that the nomogram is clinically useful.

The nomogram predictive model consisting of the five indicators (gender, age, BMI, NC and ESS) can be useful in determining OSA risk in patients with hypertension.