scholarly journals Machine Learning Validated Screening Tool to Predict Obstructive Sleep Apnea in Cancer Patients

2021 ◽  
Author(s):  
A. Paul ◽  
K.A. Wong ◽  
A. Das ◽  
D.C. Lim ◽  
M. Tan
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cancer Patients ◽  
Screening Tool ◽  
Obstructive Sleep

419 STOP-Bang Score and History of Radiation Predicts Risk of Obstructive Sleep Apnea in Cancer Patients: A Machine Learning Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A166-A166
Author(s):  
Ankita Paul ◽  
Karen Wong ◽  
Anup Das ◽  
Diane Lim ◽  
Miranda Tan
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cancer Patients ◽  
Prediction Accuracy ◽  
Screening Tools ◽  
Cancer Type ◽  
Sleep Study ◽  
Obstructive Sleep ◽  
History Of

Abstract Introduction Cancer patients are at an increased risk of moderate-to-severe obstructive sleep apnea (OSA). The STOP-Bang score is a commonly used screening questionnaire to assess risk of OSA in the general population. We hypothesize that cancer-relevant features, like radiation therapy (RT), may be used to determine the risk of OSA in cancer patients. Machine learning (ML) with non-parametric regression is applied to increase the prediction accuracy of OSA risk. Methods Ten features namely STOP-Bang score, history of RT to the head/neck/thorax, cancer type, cancer stage, metastasis, hypertension, diabetes, asthma, COPD, and chronic kidney disease were extracted from a database of cancer patients with a sleep study. The ML technique, K-Nearest-Neighbor (KNN), with a range of k values (5 to 20), was chosen because, unlike Logistic Regression (LR), KNN is not presumptive of data distribution and mapping function, and supports non-linear relationships among features. A correlation heatmap was computed to identify features having high correlation with OSA. Principal Component Analysis (PCA) was performed on the correlated features and then KNN was applied on the components to predict the risk of OSA. Receiver Operating Characteristic (ROC) - Area Under Curve (AUC) and Precision-Recall curves were computed to compare and validate performance for different test sets and majority class scenarios. Results In our cohort of 174 cancer patients, the accuracy in determining OSA among cancer patients using STOP-Bang score was 82.3% (LR) and 90.69% (KNN) but reduced to 89.9% in KNN using all 10 features mentioned above. PCA + KNN application using STOP-Bang score and RT as features, increased prediction accuracy to 94.1%. We validated our ML approach using a separate cohort of 20 cancer patients; the accuracies in OSA prediction were 85.57% (LR), 91.1% (KNN), and 92.8% (PCA + KNN). Conclusion STOP-Bang score and history of RT can be useful to predict risk of OSA in cancer patients with the PCA + KNN approach. This ML technique can refine screening tools to improve prediction accuracy of OSA in cancer patients. Larger studies investigating additional features using ML may improve OSA screening accuracy in various populations Support (if any):

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Support Vector ◽  
Learning Models ◽  
Obstructive Sleep ◽  
Screening Questionnaires ◽  
Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

scholarly journals Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

2021 ◽  
Author(s):  
Satoru Tsuiki ◽  
Takuya Nagaoka ◽  
Tatsuya Fukuda ◽  
Yuki Sakamoto ◽  
Fernanda R. Almeida ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Apnea Hypopnea Index ◽  
Cephalometric Analysis ◽  
Obstructive Sleep ◽  
Main Region

Abstract Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. Results The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. Conclusions A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

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).

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