Simple and Unbiased OSA Prescreening: Introduction of a New Morphologic OSA Prediction Score

Standardised Tool for the Assessment of Bruxism

OBJECTIVE

This paper aims to present and describe the Standardised Tool for the Assessment of Bruxism (STAB), an instrument that was developed to provide a multidimensional evaluation of bruxism status, comorbid conditions, aetiology and consequences.

METHODS

The rationale for creating the tool and the road map that led to the selection of items included in the STAB has been discussed in previous publications.

RESULTS

The tool consists of two axes, specifically dedicated to the evaluation of bruxism status and consequences (Axis A) and of bruxism risk and etiological factors and comorbid conditions (Axis B). The tool includes 14 domains, accounting for a total of 66 items. Axis A includes the self-reported information on bruxism status and possible consequences (subject-based report) together with the clinical (examiner report) and instrumental (technology report) assessment. The Subject-Based Assessment (SBA) includes domains on Sleep Bruxism (A1), Awake Bruxism (A2) and Patient's Complaints (A3), with information based on patients' self-report. The Clinically Based Assessment (CBA) includes domains on Joints and Muscles (A4), Intra- and Extra-Oral Tissues (A5) and Teeth and Restorations (A6), based on information collected by an examiner. The Instrumentally Based Assessment (IBA) includes domains on Sleep Bruxism (A7), Awake Bruxism (A8) and the use of Additional Instruments (A9), based on the information gathered with the use of technological devices. Axis B includes the self-reported information (subject-based report) on factors and conditions that may have an etiological or comorbid association with bruxism. It includes domains on Psychosocial Assessment (B1), Concurrent Sleep-related Conditions Assessment (B2), Concurrent Non-Sleep Conditions Assessment (B3), Prescribed Medications and Use of Substances Assessment (B4) and Additional Factors Assessment (B5). As a rule, whenever possible, existing instruments, either in full or partial form (i.e. specific subscales), are included. A user's guide for scoring the different items is also provided to ease administration.

CONCLUSIONS

The instrument is now ready for on-field testing and further refinement. It can be anticipated that it will help in collecting data on bruxism in such a comprehensive way to have an impact on several clinical and research fields.

The development of the Standardised Tool for the Assessment of Bruxism (STAB): An international road map

This paper summarises the background reasoning and work that led to the selection of the items included in the Standardised Tool for the Assessment of Bruxism (STAB), also introducing the list of items. The instrument is currently being tested for face validity and on-field comprehension. The underlying premise is that the different motor activities included in the bruxism spectrum (e.g. clenching vs. grinding, with or without teeth contact) potentially need to be discriminated from each other, based on their purportedly different aetiology, comorbidities and potential consequences. Focus should be on a valid impression of the activities' frequency, intensity and duration. The methods that can be used for the above purposes can be grouped into strategies that collect information from the patient's history (subject-based), from the clinical assessment performed by an examiner (clinically based) or from the use of instruments to measure certain outcomes (instrumentally based). The three strategies can apply to all aspects of bruxism (i.e. status, comorbid conditions, aetiology and consequences). The STAB will help gathering information on many aspects, factors and conditions that are currently poorly investigated in the field of bruxism. To this purpose, it is divided into two axes. Axis A includes the self-reported information on bruxism status and potential consequences (subject-based report) together with the clinical (examiner report) and instrumental assessment (technology report). Axis B includes the self-reported information (subject-based report) on factors and conditions that may have an etiological or comorbid role for bruxism. This comprehensive multidimensional assessment system will allow building predictive model for clinical and research purposes.

Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults

BACKGROUND

Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires.

METHODS

This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals.

RESULTS

Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient.

CONCLUSIONS

We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment.

TRIAL REGISTRATION

Retrospectively registered.

Establishment and Application Evaluation of an Improved Obstructive Sleep Apnea Screening Questionnaire for Chinese Community: The CNCQ-OSA

Objective

Obstructive sleep apnea (OSA) is a common sleep-disordered breathing disease. We aimed to establish an improved screening questionnaire without physical examinations for OSA named the CNCQ-OSA (Chinese community questionnaire for OSA).

Methods

A total of 2585 participants who visited sleep medicine center and underwent overnight polysomnography were grouped into two independent cohorts: derivation (n = 2180) and validation (n = 405). The CNCQ-OSA was designed according to the baseline of patients in derivation cohort. We comprehensively analyzed the data to evaluate the predictive value of the CNCQ-OSA, compared to the GOAL questionnaire, STOP-Bang questionnaire (SBQ) and NoSAS questionnaire.

Results

The CNCQ-OSA included seven variables: loud snoring, BMI ≥ 25 kg/m², male gender, apnea, sleepiness, hypertension and age ≥30, with a total score ranging from 7 to 16.7 points (≥13.5 points indicating high risk of OSA, ≥14.5 points indicating extremely high risk). In the derivation and validation cohorts, the areas under the curve of the CNCQ-OSA were 0.761 and 0.767, respectively. In the validation cohort, the sensitivity and specificity of a CNCQ-OSA score ≥13.5 points for the apnea-hypopnea index (AHI) ≥5/h were 0.821 and 0.559, respectively (Youden index, 0.380), and the score ≥14.5 points were 0.494 and 0.887, respectively (Youden index, 0.375). The CNCQ-OSA had a better predictive value for AHI ≥ 5/h, AHI > 15/h and AHI > 30/h, with the highest Youden index, compared to the other questionnaires.

Conclusion

The CNCQ-OSA can effectively identify the risk of OSA, which is appropriate for self-screening at home without physical examinations.

Do brachycephaly and nose size predict the severity of obstructive sleep apnea (OSA)? A sample-based geometric morphometric analysis of craniofacial variation in relation to OSA syndrome and the role of confounding factors

Obstructive sleep apnea is a common disorder that leads to sleep fragmentation and is potentially bidirectionally related to a variety of comorbidities, including an increased risk of heart failure and stroke. It is often considered a consequence of anatomical abnormalities, especially in the head and neck, but its pathophysiology is likely to be multifactorial in origin. With geometric morphometrics, and a large sample of adults from the Study for Health in Pomerania, we explore the association of craniofacial morphology to the apnea-hypopnea index used as an estimate of obstructive sleep apnea severity. We show that craniofacial size and asymmetry, an aspect of morphological variation seldom analysed in obstructive sleep apnea research, are both uncorrelated to apnea-hypopnea index. In contrast, as in previous analyses, we find evidence that brachycephaly and larger nasal proportions might be associated to obstructive sleep apnea severity. However, this correlational signal is weak and completely disappears when age-related shape variation is statistically controlled for. Our findings suggest that previous work might need to be re-evaluated, and urge researchers to take into account the role of confounders to avoid potentially spurious findings in association studies.

Orofacial Pain and Snoring/Obstructive Sleep Apnea in Individuals with Head and Neck Cancer: A Critical Review

AIMS

(1) To summarize current knowledge on the prevalence, intensity, and descriptors of orofacial pain and snoring/obstructive sleep apnea (OSA) before and after head and neck cancer (HNC) treatment; and (2) to propose future directions for research.

METHODS

The median prevalence for each condition was estimated from the most recent systematic reviews (SRs) and updated with new findings retrieved from the PubMed, Web of Science, Embase, and Cochrane databases up to December 2021.

RESULTS

The prevalence of HNC pain seems relatively stable over time, with a median of 31% before treatment in three studies to a median of 39% at 1 month to 16 years after treatment in six studies. HNC pain intensity remains mild to moderate. There was a threefold increase in temporomandibular pain prevalence after surgery (median 7.25% before to 21.3% after). The data for snoring prevalence are unreliable. The OSA/HNC prevalence seems relatively stable over time, with a median of 72% before treatment in three studies to 77% after treatment in 14 studies.

CONCLUSION

With the exception of temporomandibular pain, the prevalence of HNC pain and OSA seems to be stable over time. Future studies should: (1) compare the trajectory of change over time according to each treatment; (2) compare individuals with HNC to healthy subjects; (3) use a standardized and comparable method of data collection; and (4) assess tolerance to oral or breathing devices, since HNC individuals may have mucosal sensitivity or pain.