The No-apnea score vs. the other five questionnaires in screening for obstructive sleep apnea-hypopnea syndrome in patients with cerebral infarction

Optimizing Obstructive Sleep Apnea Risk Assessment in Hypertension: Development of a Predictive Nomogram in China

Purpose

Obstructive sleep apnea (OSA) is common in patients with hypertension. Our study aims to construct and validate an objective nomogram that can accurately predict the risk of OSA in patients with hypertension.

Patients and Methods

Retrospective data were collected from patients with hypertension who underwent polysomnography (PSG) at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University, China. All participants were assigned to the training group (used to develop the predictive model). Similarly, data from patients with hypertension who underwent PSG at the Sleep Medicine Center of the Second Affiliated Hospital of Guangdong Medical University, China, were collected, and these participants were assigned to the validation group (used to test the model's performance). Logistic and LASSO regression analyses were used to identify factors and construct the nomogram. C-index, calibration curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to assess the model. Finally, nomogram validation was performed in the validation group.

Results

This study included a training group of 303 patients and a validation group of 217 patients. Based on LASSO and Logistic regression analyses and clinical practicality, we identified gender, age, BMI (body mass index), NC (neck circumference) and ESS (Epworth Sleepiness Scale) as predictors for the nomogram. The C-index is 0.840 in the training group and 0.808 in the validation group. The area under the curve (AUC) of the predictive model and STOP-Bang at the three diagnostic cut-off points of the Apnea-Hypopnea Index (AHI) ≥ 5, AHI ≥ 15 and AHI ≥ 30 were 0.840 vs 0.778, 0.754 vs 0.740, and 0.765 vs 0.751 respectively. The AUC at each intercept point was higher than that of STOP-Bang. DCA and CICA showed that the nomogram is clinically useful.

Conclusion

The nomogram predictive model consisting of the five indicators (gender, age, BMI, NC and ESS) can be useful in determining OSA risk in patients with hypertension.

Evaluation of five questionnaires for obstructive sleep apnea screening in the elderly

Obstructive sleep apnea (OSA) often leads to complications in the elderly. This study compares the usefulness of five screening tools for OSA in elderly patients. Data from elderly patients diagnosed with OSA, collected from the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University from January 2012 to June 2017, is analyzed. The sensitivity, specificity, predictive values, area under the curve (AUC), and diagnostic odds ratio (DOR) of the five screening tools are computed and compared. 273 elderly patients with suspected OSA are included, of whom 189 are male (69.2%). The Berlin Questionnaire has an AUC of 0.670 (95%CI: 0.611-0.725) at different cut-off points. The sensitivity and specificity of the Berlin Questionnaire are noticeably high at 0.653 (0.587-0.719) and 0.608 (0.497-0.719), 0.699 (0.621-0.776) and 0.533 (0.449-0.616), and 0.803 (0.713-0.892) and 0.503 (0.433-0.572) when the AHI is 5, 15, and 30 times/hour, respectively. The GOAL Questionnaire has the highest DOR at AHI cut-off points of 5 and 15 times/hour, while Berlin has the highest DOR at an AHI cut-off point of 30 times/hour. Comprehensive use of these five screening questionnaires for suspected OSA in elderly patients aged 60 years and older is valuable and worth promoting among the elderly population.

Performance of questionnaires to predict sleep-disordered breathing in acute stroke patients

Sleep-disordered breathing is common in stroke and may negatively affect its outcome. Screening for sleep-disordered breathing in this setting is of interest but poorly studied. We aimed to evaluate the performance of eight obstructive sleep apnea screening questionnaires to predict sleep-disordered breathing in acute stroke or transient ischaemic attack patients, and to assess the impact of stroke/transient ischaemic attack-specific factors on sleep-disordered breathing prediction. We analysed acute stroke/transient ischaemic attack patients (N = 195) from a prospective cohort ("Sleep Deficiency and Stroke Outcome study"). Assessments included anthropometrics, stroke-specific parameters, sleep history, an in-hospital respiratory polygraphy within the first week after stroke, and obstructive sleep apnea screening questionnaires (Berlin Questionnaire, Epworth Sleepiness Scale, STOP-BANG, NoSAS, Sleep Apnea Clinical Score, No-Apnea, Sleep Obstructive apnea score optimized for Stroke, SLEEP-IN). In a binary classification task for respiratory event index ≥ 15 per hr, we evaluated the performance of the above-mentioned questionnaires. We used logistic regression to identify predictors for sleep-disordered breathing in this cohort. The areas under the curve for respiratory event index ≥ 15 per hr were: Berlin Questionnaire 0.60; STOP-BANG 0.72; NoSAS 0.69; No-Apnea 0.69; Sleep Apnea Clinical Score 0.75; Epworth Sleepiness Scale 0.50; Sleep Obstructive apnea score optimized for Stroke 0.58; and SLEEP-IN 0.67. The No-Apnea had the lowest false omission rate (0.13), a sensitivity of 0.97 and a specificity of 0.12. In multiple logistic regression analysis (respiratory event index ≥ 15 per hr), age, neck circumference, National Institutes of Health Stroke Scale at admission, prior stroke, cardioembolic stroke aetiology and observed apneas were associated with sleep-disordered breathing. The logistic regression model performed similar (area under the curve 0.80) to Sleep Apnea Clinical Score (p = 0.402) and STOP-BANG (p = 0.127), but outperformed the other questionnaires. Neither existing questionnaires nor our statistical model are sufficient to accurately diagnose sleep-disordered breathing after stroke, thus requiring sleep study evaluation. The No-Apnea questionnaire may help to identify patients amenable to sleep testing.

The comparison of STOP-BANG and no-apnea questionnaires in screening obstructive sleep apnea among commercial drivers

OBJECTIVE

No-apnea questionnaire (NAQ) and STOP-BANG questionnaire (SBQ) are widely used for obstructive sleep apnea (OSA) screening. This investigation aimed to compare the SBQ with the NAQ as an OSA screening tool among commercial drivers.

METHODS

We included eligible commercial drivers who came to the Occupational Health clinic between March 2018 and March 2019. Participants filled out the SBQ, NAQ, and ESS questionnaires. The SBQ scores eight factors to assess OSA risk, with a score of ≥3 indicating high risk. The NAQ scores age and neck circumference for OSA risk, with ≥3 indicating significant risk. The ESS measures daytime sleepiness, with a score of ≥10 indicating excessive sleepiness as the most common symptom of OSA. The patients' scores were evaluated based on the set criteria. A McNemar test was used to determine the differences between SBQ and NAQ. The number of at-risk patients was measured for each screening test, and the correlation between the two screening methods was evaluated by measuring Cohen's kappa coefficient.

RESULTS

A total number of 581 commercial drivers, with a mean age of 44.39 ± 9.16 years, participated. The mean SBQ score was 1.82 ± 0.78, with 17.7% of participants being at high risk of OSA. The mean NAQ score was 3.48 ± 1.94, with 65.7% of participants being at high risk of OSA. About 48.6% of commercial drivers were at high risk, according to the NAQ but not SBQ. In contrast, 0.5% of participants were at high risk, according to SBQ, but not NAQ. Regarding ESS scores, among those identified as high risk for OSA by the SBQ, 13.6% exhibited an ESS score greater than 10. Similarly, within the high-risk group identified by the NAQ, this proportion was 14.1%. Cohen's kappa coefficient was 0.17, which is considerably low. A McNemar test also indicated that the SBQ and the NAQ didn't have equivalent diagnostic outcomes (P-value < 0.001).

CONCLUSIONS

The NAQ identified more professional drivers at risk for OSA compared to the SBQ, suggesting that objective-based questionnaires may be more effective for screening in safety-sensitive jobs like commercial driving. However, further validation with polysomnography and cost-benefit considerations are needed to determine the most efficient and sustainable screening approach.

Sleep Apnea and Stroke: A Narrative Review

TOPIC IMPORTANCE

Stroke is the second-leading cause of death worldwide. OSA is an independent risk factor for stroke and is associated with multiple vascular risk factors. Poststroke OSA is prevalent and closely linked with various stroke subtypes, including cardioembolic stroke and cerebral small vessel disease. Observational studies have shown that untreated poststroke OSA is associated with an increased risk of recurrent stroke, mortality, poorer functional recovery, and longer hospitalizations.

REVIEW FINDINGS

Poststroke OSA tends to be underdiagnosed and undertreated, possibly because patients with stroke and OSA present atypically compared with the general population with OSA. Objective testing, such as the use of ambulatory sleep testing or in-laboratory polysomnography, is recommended for diagnosing OSA. The gold standard for treating OSA is CPAP therapy. Randomized controlled trials have shown that treatment of poststroke OSA using CPAP improves nonvascular outcomes such as cognition and neurologic recovery. However, findings from randomized controlled trials that have evaluated the effect of CPAP on recurrent stroke risk and mortality have been largely negative.

SUMMARY

There is a need for high-quality randomized controlled trials in poststroke OSA that may provide evidence to support the utility of CPAP (and/or other treatment modalities) in reducing recurrent vascular events and mortality. This goal may be achieved by examining treatment strategies that have yet to be trialed in poststroke OSA, tailoring interventions according to poststroke OSA endotypes and phenotypes, selecting high-risk populations, and using metrics that reflect the physiologic abnormalities that underlie the harmful effects of OSA on cardiovascular outcomes.

Predictive value of NoSAS questionnaire combined with the modified Mallampati grade for hypoxemia during routine sedation for gastrointestinal endoscopy

BACKGROUND

The incidence of hypoxemia during painless gastrointestinal endoscopy remains a matter of concem. To date, there is no recognized simple method to predict hypoxemia in digestive endoscopic anesthesia. The NoSAS (neck circumference, obesity, snoring, age, sex) questionnaire, an objective and simple assessment scale used to assess obstructive sleep apnea (OSA), combined with the modified Mallampati grade (MMP), may have certain screening value. This combination may allow anesthesiologists to anticipate, manage, and consequently decrease the occurrence of hypoxemia.

METHODS

This study was a prospective observational trial. The primary endpoint was the incidence of hypoxaemia defined as pulse oxygen saturation (SpO2) < 95% for 10 s. A total of 2207 patients admitted to our hospital for painless gastrointestinal endoscopy were studied. All patients were measured for age, height, weight, body mass index, neck circumference, snoring, MMP, and other parameters. Patients were divided into hypoxemic and non-hypoxemic groups based on the SpO2. The ROC curve was plotted to evaluate the screening value of the NoSAS questionnaire separately and combined with MMP for hypoxemia. The total NoSAS score was evaluated at cut-off points of 8 and 9.

RESULTS

With a NoSAS score ≥ 8 as the critical value for analysis, the sensitivity for hypoxemia was 58.3%, the specificity was 88.4%, and the area under the ROC was 0.734 (P < 0.001, 95% CI: 0.708-0.759). With a NoSAS score ≥ 9 as a critical value, the sensitivity for hypoxemia was 36.50%, the specificity rose to 96.16%, and the area under the ROC was 0.663 (P < 0.001, 95% CI: 0.639-0.688). With the NoSAS Score combined with MMP for analysis, the sensitivity was 78.4%, the specificity was 84%, and the area under the ROC was 0.859 (P < 0.001, 95%CI:0.834-0.883).

CONCLUSIONS

As a new screening tool, the NoSAS questionnaire is simple, convenient, and useful for screening hypoxemia. This questionnaire, when paired withMMP, is likely to be helpful for the screening of hypoxemia.

Validation of the STOP questionnaire as a screening tool for OSA among different populations: a systematic review and meta-regression analysis

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is a sleep breathing disorder associated with adverse health outcomes, but it remains largely underdiagnosed. The STOP questionnaire is a simple tool for screening OSA and is widely used in various populations. The objective of this study was to determine the predictive parameters of the STOP questionnaire to detect OSA in sleep clinics, medical population, surgical population, commercial drivers, and the general population.

METHODS

Electronic databases were searched from January 2008 to April 2021. Pooled predictive parameters were recalculated using 2 × 2 contingency tables and random-effects meta-analyses were performed. The combined test characteristics at different OSA severities [any OSA (AHI≥5), moderate-to-severe OSA (AHI≥15), severe OSA (AHI≥30)] were used to compare the accuracy of the STOP questionnaire with polysomnography. The quality of the studies was evaluated using Cochrane Methods criteria.

RESULTS

Twenty-four studies met the inclusion criteria: 16 were in the sleep clinic population (n=8,132), four in medical population (n=1,023), two in the surgical population (n=258), and one study each on commercial drivers (n=85) and general population (n=4,770). A STOP score ≥2 showed excellent sensitivity to the different OSA severities for the sleep clinic population (>89%) and to severe OSA for the medical population (85.6%). In both populations, the STOP questionnaire also had excellent discriminative power to exclude severe OSA [negative predictive values (NPV) >84%]. The pooled sensitivity and NPV for the surgical population with moderate-to-severe OSA was 81% and 75%.

CONCLUSIONS

This meta-analysis suggests that the STOP questionnaire is a valid and effective screening tool for OSA among these population.

A meta-analysis of the diagnostic value of NoSAS in patients with sleep apnea syndrome

OBJECTIVE

The NoSAS score is a new tool widely used in recent years to screen for obstructive sleep apnea. A number of studies have shown that the NoSAS score is more accurate than previous tools, such as the Berlin, STOP-Bang, and STOP questionnaires. Therefore, this meta-analysis assessed the diagnostic value of the NoSAS score for sleep apnea syndrome in comparison to polysomnography.

METHODS

Two researchers searched the PubMed, EMBASE, Cochrane, and Web of Science databases through November 13, 2020. This paper used Endnote9.3 software to manage the literature and RevMan 5.3 and STATA12.0 software to perform the meta-analysis.

RESULTS

A total of 10 studies were included in this meta-analysis, including 14,510 patients. The meta-analysis showed that the pooled sensitivity was 0.798 (95% CI 0.757, 0.833), the pooled specificity was 0.582 (95% CI 0.510, 0.651), the positive likelihood ratio was 1.909 (95% CI 1.652, 2.206), the negative likelihood ratio was 0.347 (95% CI 0.300, 0.403), the diagnostic OR was 5.495 (95% CI 4.348, 6.945), and the area under the SROC curve was 0.77 (95% CI 0.73, 0.80). The NoSAS score has good efficacy in identifying patients likely to have obstructive sleep apnea.

CONCLUSION

The NoSAS score can accurately identify patients likely to have obstructive sleep apnea. Therefore, in the absence of polysomnography, one should use the NoSAS score to evaluate patients with suspected sleep apnea syndrome.

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

Introduction

Obstructive 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.

Methods

A 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.

Results

The 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.

Conclusion

The STOP-Bang questionnaire is an effective tool to screen for OSA (AHI≥5) with AUC of 0.86 in patients with cardiovascular risk factors.