Auto Fluorescence Allows Us to Detect Early Signs of Oral Cancer and Much More

2021 ◽  
Author(s):  
Randall L. Weisel
Keyword(s):  
Sleep Apnea ◽  
Screening Tool ◽  
Healthcare Providers ◽  
Systemic Diseases ◽  
Novel Approach ◽  
Oral Environment ◽  
Primary Disorder ◽  
Sleep Related Breathing Disorder ◽  
Auto Fluorescence ◽  
By Products

This paper introduces a novel approach, using autofluorescence, to objectively examine the oral cavity for inflammation and infection. Many systemic diseases are perpetuated by microorganisms that colonize in the oral environment. They enter the cardiovascular system by enzymatic processes that open the oral mucosa to allow their entry. A majority of the microbes are anaerobes and/or facultative anaerobes. When they enter the host, they metabolize blood. Their waste by products contains iron elements within a compound called porphyrin. Porphyrin will fluoresce when exposed to certain wavelengths of light. Healthcare providers can utilize this natural occurring process to objectively see these harmful pathogens. This may indicate that the host has a Sleep Related Breathing Disorder (SRBD). Sleep apnea is a primary disorder of SRBD’s. This technology offers medical and dental fields a screening tool for a pandemic healthcare problem.

scholarly journals Implementing an educational program to increase preoperative screening for obstructive sleep apnea using the stop-bang questionnaire

2016 ◽  
Vol 5 (1) ◽  
pp. 56 ◽  
Author(s):  
Adam Davies ◽  
Monaghan W. Patrick ◽  
Hogan Gerard
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Technology Acceptance ◽  
Registered Nurse ◽  
Screening Tool ◽  
Perioperative Complications ◽  
Nurse Anesthetists ◽  
Perceived Usefulness ◽  
Obstructive Sleep ◽  
Registered Nurse Anesthetists

<p><strong>Background:</strong> Obstructive sleep apnea (OSA) is a potentially fatal disease process that has been linked to higher rates of morbidity and mortality as well as increased perioperative complications. OSA is characterized by repetitive pauses in breathing during sleep. Greater than 92% of women and 82% of men who are plagued by moderate to severe sleep apnea are undiagnosed and may go unrecognized in the perioperative setting. The gap between a high prevalence of undiagnosed OSA in the adult population and the low level of clinical recognition has been well-documented. The term “STOP-BANG” is an acronym for eight independent elements predictive of OSA—three are OSA-related symptoms, three are physiological measurements, and two are patient characteristics.</p><p><strong>Methods:</strong> This project used a quasi-experimental design using a 16-question self-developed survey based on the technology acceptance model (TAM). Participants were asked to read an educational pamphlet on OSA and then complete the survey.</p><p><strong>Results:</strong> This study found strong evidence to suggest that among Certified Registered Nurse Anesthetists (CRNAs) and Student Registered Nurse Anesthetists (SRNAs), those with higher scores on Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Attitude toward Use (AT), tend to have a higher Behavioral Intention to Use (BIU) the STOP-BANG screening tool.</p><p><strong>Conclusions:</strong> The results suggest that programs targeted at raising CRNAs’ and SRNAs’ PEOU, PU, and AT regarding the STOP-BANG questionnaire will culminate in increased use of the STOP-BANG screening tool. The use of this screening tool will detect patients previously unidentified as having OSA, and ultimately prevent perioperative complications associated with this disease.</p>

scholarly journals Validation of the Spanish Version of the STOP-Bang Questionnaire: Usefulness as a Screening Tool for Obstructive Sleep Apnea in Adults

2020 ◽  
pp. 014556132093233
Author(s):  
Beatriz Delgado-Vargas ◽  
Leticia Acle-Cervera ◽  
Gianmarco Narciso López
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Screening Tool ◽  
Sleep Apnea Syndrome ◽  
Spanish Version ◽  
Obstructive Sleep ◽  
Significant Difference ◽  
Good Internal Consistency ◽  
The Mean ◽  
Apnea Syndrome

Objectives: Obstructive sleep apnea syndrome (OSAS) is an increasing health problem, the diagnosis of which is generally delayed due to long waiting lists for the tests used to identify it. Therefore, tools that help on classifying patients at higher risk of suffering this syndrome have been developed. Methods: One hundred ninety-three consecutive patients, with and without OSAS, filled in the Spanish version of the STOP-Bang questionnaire in Hospital Universitario de Torrejón (Spain). Polysomnographies were performed to diagnose the presence and severity of the OSAS. Statistics analysis of the demographic characteristics of the sample and the questionnaire results was performed. Results: Most patients were male (73%) and the mean age was 50.4 years (ranging from 19-77 years). Cronbach α coefficient in the sample was 0.8072. A statistically significant difference was noted in the questionnaire scores between patients with OSAS and those without the syndrome. Conclusions: The Spanish version of the STOP-Bang questionnaire possess a good internal consistency that allows us to rely on it as a screening tool for patients with OSAS. In our sample, a difference in the questionnaire score was appreciated between patients with and without the syndrome, which strongly supports the utility of the questionnaire for its purpose.

scholarly journals Cardiorespiratory Polygraphy for Detection of Obstructive Sleep Apnea in Patients With Atrial Fibrillation

2021 ◽  
Vol 8 ◽  
Author(s):  
Michiel Delesie ◽  
Lieselotte Knaepen ◽  
Johan Verbraecken ◽  
Karolien Weytjens ◽  
Paul Dendale ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Screening Tool ◽  
Area Under The Curve ◽  
Screening Tools ◽  
Apnea Hypopnea Index ◽  
Automated Algorithm ◽  
Obstructive Sleep ◽  
At Home

Background: Obstructive sleep apnea (OSA) is a modifiable risk factor of atrial fibrillation (AF) but is underdiagnosed in these patients due to absence of good OSA screening pathways. Polysomnography (PSG) is the gold standard for diagnosing OSA but too resource-intensive as a screening tool. We explored whether cardiorespiratory polygraphy (PG) devices using an automated algorithm for Apnea-Hypopnea Index (AHI) determination can meet the requirements of a good screening tool in AF patients.Methods: This prospective study validated the performance of three PGs [ApneaLink Air (ALA), SOMNOtouch RESP (STR) and SpiderSAS (SpS)] in consecutive AF patients who were referred for PSG evaluation. Patients wore one of the three PGs simultaneously with PSG, and a different PG during each of three consecutive nights at home. Severity of OSA was classified according to the AHI during PSG (&lt;5 = no OSA, 5–14 = mild, 15–30 = moderate, &gt;30 = severe).Results: Of the 100 included AF patients, PSG diagnosed at least moderate in 69% and severe OSA in 33%. Successful PG execution at home was obtained in 79.1, 80.2 and 86.8% of patients with the ALA, STR and SpS, respectively. For the detection of clinically relevant OSA (AHI ≥ 15), an area under the curve of 0.802, 0.772 and 0.803 was calculated for the ALA, STR and SpS, respectively.Conclusions: This study indicates that home-worn PGs with an automated AHI algorithm can be used as OSA screening tools in AF patients. Based on an appropriate AHI cut-off value for each PG, the device can guide referral for definite PSG diagnosis.

scholarly journals 0572 Using Anthropometric Measures to Screen for Obstructive Sleep Apnea in the Sleep Heart Health Study Cohort

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A219-A219
Author(s):  
K D Vana ◽  
G E Silva ◽  
J D Carreon ◽  
S F Quan
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Screening Tool ◽  
Health Study ◽  
Heart Health ◽  
Height Ratio ◽  
Cut Point ◽  
Proxy Variables ◽  
Obstructive Sleep ◽  
Significant Difference

Abstract Introduction Individuals at high risk for obstructive sleep apnea (OSA) may not access sleep clinics for reasons including immobility, transportation difficulties, or living in rural areas. An easy-to-administer OSA screening tool for different body types, independent of witnessed apneas or body mass index (BMI), is lacking to identify this group quickly. We compared the sensitivities (SNs), specificities (SPs), and receiving operator curves (ROCs) of the neck circumference/height ratio (NHR) and waist circumference/height ratio (WHR) in predicting moderate and severe OSA (apnea-hypopnea index [AHI] ≥15/hr) with the SN, SP, and ROC of the derived Stop-Bang Questionnaire (dSBQ), which was created from proxy variables from the Sleep Heart Health Study (SHHS). Methods Data from the SHHS baseline evaluation were used and included participants (N=5431) who completed polysomnograms and had neck and waist circumferences, height measurements, and the SHHS proxy variables. This data then was divided randomly into 1/3 for derivation and 2/3 for validation analyses. Results No statistical differences were seen for gender, age, or ethnicity between the derivation and validation samples. In the validation sample (n=3621), the NHR cut-point of 0.21 resulted in a SN of 91% and a SP of 26% for AHI ≥15/hr. The WHR cut-point of 0.51 resulted in a SN of 91% and a SP of 21% for AHI ≥15/hr. Comparing the validation NHR and the dSBQ ROC curves showed no significant difference (AUCs=0.69 and 0.70, respectively; p=0.22). However, the ROC curve for WHR was significantly lower than for the dSBQ (AUCs=0.63 and 0.70, respectively; p&lt;0.0001). Comparing the derivation and validation ROCs showed no significant differences between NHR ROCs, p=0.81, or between WHR ROCs, p=0.67. Conclusion The NHR is a viable screening tool, independent of witnessed apneas and BMI, that can be used for different body types and is statistically comparable to the dSBQ. Support This work was supported by U01HL53938 and U01HL53938-07S (University of Arizona).

A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK

2019 ◽  
Vol 19 (04) ◽  
pp. 1950026 ◽  
Author(s):  
SINAM AJITKUMAR SINGH ◽  
SWANIRBHAR MAJUMDER
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Learning Algorithm ◽  
Low Cost ◽  
Performance Outcomes ◽  
Classification Systems ◽  
Analysis Model ◽  
Time Frequency ◽  
Novel Approach ◽  
Obstructive Sleep

Obstructive sleep apnea (OSA) is the most common and severe breathing dysfunction which frequently freezes the breathing for longer than 10[Formula: see text]s while sleeping. Polysomnography (PSG) is the conventional approach concerning the treatment of OSA detection. But, this approach is a costly and cumbersome process. To overcome the above complication, a satisfactory and novel technique for interpretation of sleep apnea using ECG were recording is under development. The methods for OSA analysis based on ECG were analyzed for numerous years. Early work concentrated on extracting features, which depend entirely on the experience of human specialists. A novel approach for the prediction of sleep apnea disorder based on the convolutional neural network (CNN) using a pre-trained (AlexNet) model is analyzed in this study. After filtering per-minute segment of the single-lead ECG recording accompanied by continuous wavelet transform (CWT), the 2D scalogram images are generated. Finally, CNN based on deep learning algorithm is adopted to enhance the classification performance. The efficiency of the proposed model is compared with the previous methods that used the same datasets. Proposed method based on CNN is able to achieve the accuracy of 86.22% with 90% sensitivity in per-minute segment OSA classification. Based on per-recording OSA diagnosis, our works correctly classify all the abnormal apneic recording with 100% accuracy. Our OSA analysis model using time-frequency scalogram generates excellent independent validation performance with different state-of-the-art OSA classification systems. Experimental results proved that the proposed method produces excellent performance outcomes with low cost and less complexity.

scholarly journals Development of a Pregnancy-Specific Screening Tool for Sleep Apnea

2012 ◽  
Vol 08 (04) ◽  
pp. 389-394 ◽  
Author(s):  
Francesca L. Facco ◽  
David W. Ouyang ◽  
Phyllis C. Zee ◽  
William A. Grobman
Keyword(s):  
Sleep Apnea ◽  
Screening Tool

scholarly journals 0906 Identification Of Physical Exam Findings With High Predictive Value For Moderate To Severe Pediatric Obstructive Sleep Apnea(osa) In Overweight/obese Children

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A344-A345
Author(s):  
A Ajisebutu ◽  
I Kak ◽  
N Thompson ◽  
R Honomichl ◽  
D Moul ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Significant Interaction ◽  
Screening Tool ◽  
Multivariate Model ◽  
Older Age ◽  
Obese Patients ◽  
Obese Children ◽  
Obstructive Sleep ◽  
Age And Sex

Abstract Introduction Obstructive sleep apnea(OSA) is highly prevalent and under-diagnosed in the overweight/obese pediatric population largely due to limitations of existing pediatric OSA screening instruments including lack of efficiency and practical implementation and lack of careful consideration of physical examination(PE) findings with high predictive value for OSA. We sought to identify PE finding(s) predictive of pediatric OSA in overweight/obese patients to inform development of an OSA screening tool. Methods Overweight/obese patients presenting to the Cleveland Clinic weight-management clinic between 2013-2018 with polysomnogram (PSG) data were included. The association of PE predictors: age, sex, race (white, black, other), neck (NC), waist circumference (WC), tonsil size (TS), height, systolic and diastolic blood pressure (BP) percentiles) in relation to OSA defined by apnea-hypopnea index (AHI)≥5,i.e. clinically significant pediatric OSA, were assessed using univariate and multivariate logistic regression models (OR,95%CI). Results Retrospective analysis of 180 overweight/obese patients (BMI percentile&gt;85th for age and sex) and age 12.5±3.7 years were included. The multivariate model showed that only WC was significantly associated (1.03, 1.00 - 1.07, p=0.038) with OSA defined as AHI≥5. A statistically significant interaction of age and sex was observed such that the likelihood of OSA increased in males with older age and conversely decreased in females with older age. (1.26,1.04 -1.52, p=0.038) The reduced multivariate model, which included age, sex, WC, and age*sex interaction term, correctly discriminated AHI &lt;5 vs. ≥ 5 66.5% of the time. Conclusion In this large clinic-based overweight/obese pediatric sample, males, older age and WC were significant predictors of OSA and TS was not. A significant interaction of age and sex was observed supporting increased OSA with increasing age in males. Data generated supports value of PE findings of age, sex and WC to incorporate in development of an OSA screening tool for overweight/obese children. Support  

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