Application of Neck Circumference in Four-Variable Screening Tool for Early Prediction of Obstructive Sleep Apnea in Acute Ischemic Stroke Patients

Zhang, Lisan; Zeng, Tingting; Gui, Yaxing; Sun, Yi; Xie, Fei; Zhang, Dan; Hu, Xingyue

doi:10.1016/j.jstrokecerebrovasdis.2019.06.011

Cited by 7 publications

(12 citation statements)

References 34 publications

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“…After screening titles and abstracts, 2279 studies were excluded because they did not meet the predetermined inclusion criteria. Of the remaining 17 studies, 8 full-text articles were excluded [20][21][22][23][24][25][26][27] ; the reasons for exclusion are listed in online supplemental table S1. Nine studies met Open access the inclusion criteria and were included in the review.…”

Section: Resultsmentioning

confidence: 99%

Validation of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnoea in patients with cardiovascular risk factors: a systematic review and meta-analysis

et al. 2021

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IntroductionObstructive sleep apnoea (OSA) is highly prevalent in patients with cardiovascular risk factors and is associated with increased morbidity and mortality. This review presents the predictive parameters of the STOP-Bang questionnaire as a screening tool for OSA in this population.MethodsA search of databases was performed. The inclusion criteria were: (1) use of the STOP-Bang questionnaire to screen for OSA in adults (>18 years) with cardiovascular risk factors; (2) polysomnography or home sleep apnoea testing performed as a reference standard; (3) OSA defined by either Apnoea–Hypopnoea Index (AHI) or Respiratory Disturbance Index; and (4) data on predictive parameters of the STOP-Bang questionnaire. A random-effects model was used to obtain pooled predictive parameters of the STOP-Bang questionnaire.ResultsThe literature search resulted in 3888 articles, of which 9 papers met the inclusion criteria, involving 1894 patients. The average age of the included patients was 58±13 years with body mass index (BMI) of 30±6 kg/m2, and 64% were male. The STOP-Bang questionnaire has a sensitivity of 89.1%, 90.7% and 93.9% to screen for all (AHI ≥5), moderate-to-severe (AHI ≥15) and severe (AHI≥30) OSA, respectively. The specificity was 32.3%, 22.5% and 18.3% and the area under the curve (AUC) was 0.86, 0.65 and 0.52 for all, moderate-to-severe and severe OSA, respectively.ConclusionThe STOP-Bang questionnaire is an effective tool to screen for OSA (AHI≥5) with AUC of 0.86 in patients with cardiovascular risk factors.

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Section: Resultsmentioning

confidence: 99%

Validation of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnoea in patients with cardiovascular risk factors: a systematic review and meta-analysis

et al. 2021

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“…The studies were conducted in seven different countries: the USA, Canada, China, Brazil, Slovakia, India, and Italy. Most studies (72.7%) adopted a cross-sectional study design [ 21 , 22 , 24 , 25 , 28 – 31 ], while two studies were retrospective cohort studies [ 26 , 27 ].…”

Section: Resultsmentioning

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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“…[ 32 , 33 ] The increase in free fatty acid concentration can lead to increased release of inflammatory factors, which can result in insulin resistance and vascular endothelial damage. [ 34 ] Furthermore, neck circumference has good predictive value for the risk of sleep apnea syndrome, [ 35 , 36 ] while sleep apnea syndrome is also closely related to a variety of cardiovascular diseases.…”

Section: Discussionmentioning

confidence: 99%

The correlation between neck circumference and risk factors in patients with hypertension

Zhang¹,

Wu²,

Xu³

et al. 2020

Medicine

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It is necessary to identify the relationship between neck circumference and cardiovascular risk factors in patients with hypertension. Patients with hypertension treated in our hospital were included. The height, weight, neck circumference, waist circumference, fasting blood glucose, 2 h blood glucose (2hPPG), density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and glycated hemoglobin (HbA1c) were analyzed and compared. A total of 2860 patients with hypertension were included. There were significant differences between male and female patients in the neck circumference, waist circumference, fasting blood glucose, Total cholesterol, triacylglycerol, HDL-C, LDL-C, diabetes, metabolic syndrome, dyslipidemia, drinking and smoking (all P < .05); the neck circumference was positively correlated with waist circumference, body mass index (BMI), fasting blood glucose, 2hPPG, HbA1c, triacylglycerol and LDL-C (all P < .05), and negatively correlated with HDL-C (P = .014); as the neck circumference increases, the risk of hypertension, diabetes, metabolic syndrome, abdominal obesity, and dyslipidemia increases accordingly (all P < .05); the area under curve (AUC) was 0.827 and 0.812, and the neck circumference of 37.8 and 33.9 cm was the best cut-off point for male and female patients, respectively. Neck circumference is closely related to cardiovascular risk factors in patients with hypertension, which should be promoted in the screening of cardiovascular diseases.

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Application of Neck Circumference in Four-Variable Screening Tool for Early Prediction of Obstructive Sleep Apnea in Acute Ischemic Stroke Patients

Cited by 7 publications

References 34 publications

Validation of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnoea in patients with cardiovascular risk factors: a systematic review and meta-analysis

Validation of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnoea in patients with cardiovascular risk factors: a systematic review and meta-analysis

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

The correlation between neck circumference and risk factors in patients with hypertension

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