Abstract
BACKGROUND: It is crucial to accurately predict the disease progression of systemic arterial hypertension in order to determine the most effective therapeutic strategy. To achieve this, we have employed a multimodal data-integration approach to predict the longitudinal progression of new-onset systemic arterial hypertension patients with suspected obstructive sleep apnea (OSA) at the individual level. METHODS: We developed and validated a predictive nomogram model that utilizes multimodal data, consisting of clinical features, laboratory tests, and sleep monitoring data. We assessed the probabilities of major adverse cardiac and cerebrovascular events (MACCEs) as scores for participants in longitudinal cohorts who have systemic arterial hypertension and suspected OSA. In this cohort study, MACCEs were considered as a composite of cardiac mortality, acute coronary syndrome and nonfatal stroke. The least absolute shrinkage and selection operator (LASSO) regression and multiple Cox regression analyses were performed to identify independent risk factors for MACCEs among these patients. RESULTS: 448 patients were randomly assigned to the training cohort while 189 were assigned to the verification cohort. Four clinical variables were enrolled in the constructed nomogram: age, diabetes mellitus, triglyceride, and apnea-hypopnea index (AHI). This model accurately predicted 2-year and 3-year MACCEs, achieving an impressive area under the receiver operating characteristic (ROC) curve of 0.885 and 0.784 in the training cohort, respectively. In the verification cohort, the performance of the nomogram model had good discriminatory power, with an area under the ROC curve of 0.847 and 0.729 for 2-year and 3-year MACCEs, respectively. The correlation between predicted and actual observed MACCEs was high, provided by a calibration plot, for training and verification cohorts. CONCLUSIONS: Our study yielded risk stratification for systemic arterial hypertension patients with suspected OSA, which can be quantified through the integration of multimodal data, thus highlighting OSA as a spectrum of disease. This prediction nomogram could be instrumental in defining the disease state and long-term clinical outcomes.