Multimodal Data Integration Enhance Longitudinal Prediction of New-Onset Systemic Arterial Hypertension Patients with Suspected Obstructive Sleep Apnea

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