Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea in Patients with Pulmonary Arterial Hypertension

Hu, Meixi; Duan, Anqi; Huang, Zhihua; Zhao, Zhihui; Zhao, Qing; Liu, Yan; Zhang, Yi; Li, Xin; Jin, Qi; An, Chansik; Luo, Qin; Liu, Zhihong

doi:10.2147/nss.s372447

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Several models have been developed to assess the risk of OSA in the general population [ 32 ] or in patients with specific diseases such as spinal cord injury [ 33 ], pulmonary arterial hypertension [ 34 ], and diabetes [ 35 ]. However, these models may not be suitable for stroke patients.…”

Section: Discussionmentioning

confidence: 99%

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

Yang,

Lu,

Yang

2024

Syst Rev

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Background Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive cessation or reduction in airflow during sleep. Stroke patients have a higher risk of OSA, which can worsen their cognitive and functional disabilities, prolong their hospitalization, and increase their mortality rates. Methods We conducted a comprehensive literature search in the databases of PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, and CNKI, using a combination of keywords and MeSH words in both English and Chinese. Studies published up to March 1, 2022, which reported the development and/or validation of clinical prediction models for OSA diagnosis in stroke patients. Results We identified 11 studies that met our inclusion criteria. Most of the studies used logistic regression models and machine learning approaches to predict the incidence of OSA in stroke patients. The most frequently selected predictors included body mass index, sex, neck circumference, snoring, and blood pressure. However, the predictive performance of these models ranged from poor to moderate, with the area under the receiver operating characteristic curve varying from 0.55 to 0.82. All the studies have a high overall risk of bias, mainly due to the small sample size and lack of external validation. Conclusion Although clinical prediction models have shown the potential for diagnosing OSA in stroke patients, their limited accuracy and high risk of bias restrict their implications. Future studies should focus on developing advanced algorithms that incorporate more predictors from larger and representative samples and externally validating their performance to enhance their clinical applicability and accuracy.

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Section: Discussionmentioning

confidence: 99%

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

Yang,

Lu,

Yang

2024

Syst Rev

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STOP-Bang for OSA screening in patients with pulmonary hypertension

Fox

Lanasa

Saketkoo

et al. 2023

Respiratory Medicine

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A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Huang,

Zhuang,

Zheng

et al. 2024

NSS

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Objective Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening. Methods The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were “Gaussian Naive Bayes (GNB)”, “Support Vector Machine (SVM)”, “K Neighbors Classifier (KNN)”, and “Logistic Regression (LR)”. Finally, the optimal algorithm was selected to construct the final prediction model. Results Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively. Conclusion We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

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Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea in Patients with Pulmonary Arterial Hypertension

Cited by 3 publications

References 31 publications

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

STOP-Bang for OSA screening in patients with pulmonary hypertension

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

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