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

Overview

Journal Rev Cardiovasc Med

Specialty Cardiology & Vascular Diseases

Date 2024 Aug 14

PMID 39139418

Authors

Yi Yang

Haibing Jiang

Haitao Yang

Xiangeng Hou

Tingting Wu

Ying Pan

Xiang Xie

Affiliations

Soon will be listed here.

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.

References

Berglund L, Brunzell J, Goldberg A, Goldberg I, Sacks F, Murad M . Evaluation and treatment of hypertriglyceridemia: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2012; 97(9):2969-89. PMC: 3431581. DOI: 10.1210/jc.2011-3213. View

Balachandran V, Gonen M, Smith J, DeMatteo R . Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015; 16(4):e173-80. PMC: 4465353. DOI: 10.1016/S1470-2045(14)71116-7. View

Wang J, Wu X, Sun J, Xu T, Zhu T, Yu F . Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment. Front Cardiovasc Med. 2022; 9:1053470. PMC: 9670131. DOI: 10.3389/fcvm.2022.1053470. View

Zhang Z, Zhao L, Lu Y, Meng X, Zhou X . Association between non-insulin-based insulin resistance indices and cardiovascular events in patients undergoing percutaneous coronary intervention: a retrospective study. Cardiovasc Diabetol. 2023; 22(1):161. PMC: 10311786. DOI: 10.1186/s12933-023-01898-1. View

Nguyen T, Chow C . Global and national high blood pressure burden and control. Lancet. 2021; 398(10304):932-933. DOI: 10.1016/S0140-6736(21)01688-3. View

Petrie J, Guzik T, Touyz R . Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol. 2018; 34(5):575-584. PMC: 5953551. DOI: 10.1016/j.cjca.2017.12.005. View

Xu Y, Ye Z, Wang B, Tang L, Sun J, Chen X . Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model. Oxid Med Cell Longev. 2022; 2022:5497134. PMC: 9252843. DOI: 10.1155/2022/5497134. View

Gyldenkerne C, Mortensen M, Kahlert J, Thrane P, Olesen K, Sorensen H . 10-Year Cardiovascular Risk in Patients With Newly Diagnosed Type 2 Diabetes Mellitus. J Am Coll Cardiol. 2023; 82(16):1583-1594. DOI: 10.1016/j.jacc.2023.08.015. View

Javaheri S, Barbe F, Campos-Rodriguez F, Dempsey J, Khayat R, Javaheri S . Sleep Apnea: Types, Mechanisms, and Clinical Cardiovascular Consequences. J Am Coll Cardiol. 2017; 69(7):841-858. PMC: 5393905. DOI: 10.1016/j.jacc.2016.11.069. View

10.

Zapater A, Sanchez-de-la-Torre M, Benitez I, Targa A, Bertran S, Torres G . The Effect of Sleep Apnea on Cardiovascular Events in Different Acute Coronary Syndrome Phenotypes. Am J Respir Crit Care Med. 2020; 202(12):1698-1706. DOI: 10.1164/rccm.202004-1127OC. View

11.

Ye Z, Xu Y, Tang L, Wu M, Wu B, Zhu T . Predicting long-term prognosis after percutaneous coronary intervention in patients with new onset ST-elevation myocardial infarction: development and external validation of a nomogram model. Cardiovasc Diabetol. 2023; 22(1):87. PMC: 10103457. DOI: 10.1186/s12933-023-01820-9. View

12.

Zeng X, Ma D, Wu K, Yang Q, Zhang S, Luo Y . Development and validation of a clinical model to predict hypertension in consecutive patients with obstructive sleep apnea hypopnea syndrome: a hospital-based study and nomogram analysis. Am J Transl Res. 2022; 14(2):819-830. PMC: 8902532. View

13.

Hartaigh B, Gransar H, Callister T, Shaw L, Schulman-Marcus J, Stuijfzand W . Development and Validation of a Simple-to-Use Nomogram for Predicting 5-, 10-, and 15-Year Survival in Asymptomatic Adults Undergoing Coronary Artery Calcium Scoring. JACC Cardiovasc Imaging. 2017; 11(3):450-458. PMC: 5723248. DOI: 10.1016/j.jcmg.2017.03.018. View

14.

Blaha M, Khorsandi M . Type 2 Diabetes Mellitus: Now in the Cardiologist's Wheelhouse. J Am Coll Cardiol. 2023; 82(16):1595-1597. DOI: 10.1016/j.jacc.2023.08.033. View

15.

Wang M, Lloyd-Jones D . Cardiovascular Risk Assessment in Hypertensive Patients. Am J Hypertens. 2021; 34(6):569-577. DOI: 10.1093/ajh/hpab021. View

16.

Randerath W, Bonsignore M, Herkenrath S . Obstructive sleep apnoea in acute coronary syndrome. Eur Respir Rev. 2019; 28(153). PMC: 9488646. DOI: 10.1183/16000617.0114-2018. View

17.

Shivalkar B, Van De Heyning C, Kerremans M, Rinkevich D, Verbraecken J, De Backer W . Obstructive sleep apnea syndrome: more insights on structural and functional cardiac alterations, and the effects of treatment with continuous positive airway pressure. J Am Coll Cardiol. 2006; 47(7):1433-9. DOI: 10.1016/j.jacc.2005.11.054. View

18.

Tocci G, Citoni B, Nardoianni G, Figliuzzi I, Volpe M . Current applications and limitations of European guidelines on blood pressure measurement: implications for clinical practice. Intern Emerg Med. 2022; 17(3):645-654. PMC: 8967564. DOI: 10.1007/s11739-022-02961-7. View

19.

Deng X, Hou H, Wang X, Li Q, Li X, Yang Z . Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China. Elife. 2021; 10. PMC: 8163499. DOI: 10.7554/eLife.66419. View

20.

Lou Y, Ma W, Wang Z, Yang N, Sun Y, Liu Y . [Writing protocols for the Chinese clinical practice guidelines of hypertension]. Zhonghua Xin Xue Guan Bing Za Zhi. 2022; 50(7):671-675. DOI: 10.3760/cma.j.cn112148-20211126-01021. View