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

TL;DR

This study uses combined data to predict heart and brain risks in patients with high blood pressure and sleep apnea, helping guide better treatment.

Contribution

A novel predictive nomogram model using multimodal data for longitudinal risk stratification in hypertension patients with suspected OSA.

Findings

The nomogram model achieved high accuracy in predicting MACCEs with ROC AUCs of 0.885 and 0.847 for 2-year events in training and verification cohorts.

Key risk factors identified include age, diabetes mellitus, triglycerides, and apnea-hypopnea index (AHI).

The model showed strong calibration between predicted and observed MACCEs in both cohorts.

Abstract

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.

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.

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.

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.

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