scholarly journals Characterization of Genetic and Phenotypic Heterogeneity of Obstructive Sleep Apnea Using Electronic Health Records

2019 ◽  
Author(s):  
Olivia J. Veatch ◽  
Christopher R. Bauer ◽  
Navya Josyula ◽  
Diego R. Mazzotti ◽  
Brendan T. Keenan ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Electronic Health Records ◽  
Medical Center ◽  
Genomic Variation ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Health Records ◽  
Obstructive Sleep ◽  
Electronic Health

ABSTRACTObstructive sleep apnea (OSA) is defined by frequent episodes of reduced or complete cessation of airflow during sleep and is linked to negative health outcomes. Understanding the genetic factors influencing expression of OSA may lead to new treatment strategies. Electronic health records can be leveraged to both validate previously reported OSA-associated genomic variation and detect novel relationships between these variants and comorbidities. We identified candidate single nucleotide polymorphisms (SNPs) via systematic literature review of existing research. Using datasets available at Geisinger (n=39,407) and Vanderbilt University Medical Center (n=24,084), we evaluated associations between 48 SNPs and OSA diagnosis, defined using clinical codes. We also evaluated associations between these SNPs and OSA severity measures obtained from sleep reports at Geisinger (n=6,571). Finally, we used a phenome-wide approach to perform discovery and replication analyses testing associations between OSA candidate SNPs and other clinical codes and laboratory values. Ten SNPs were associated with OSA diagnosis in at least one dataset, and one additional SNP was associated following meta-analysis across all datasets. Three other SNPs were solely associated in subgroups defined by established risk factors (i.e., age, sex, and BMI). Five OSA diagnosis-associated SNPs, and 16 additional SNPs, were associated with OSA severity measures. SNPs associated with OSA diagnosis were also associated with codes reflecting cardiovascular disease, diabetes, celiac disease, peripheral nerve disorders and genitourinary symptoms. Results highlight robust OSA-associated SNPs, and provide evidence of convergent mechanisms influencing risk for co-occurring conditions. This knowledge can lead to more personalized treatments for OSA and related comorbidities.

scholarly journals Characterization of genetic and phenotypic heterogeneity of obstructive sleep apnea using electronic health records

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Olivia J. Veatch ◽  
Christopher R. Bauer ◽  
Brendan T. Keenan ◽  
Navya S. Josyula ◽  
Diego R. Mazzotti ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Electronic Health Records ◽  
Phenotypic Heterogeneity ◽  
Health Records ◽  
Obstructive Sleep ◽  
Electronic Health

scholarly journals Evidence Supports No Relationship between Obstructive Sleep Apnea and Premolar Extraction: An Electronic Health Records Review

2015 ◽  
Vol 11 (12) ◽  
pp. 1443-1448 ◽  
Author(s):  
Ann J. Larsen ◽  
D. Brad Rindal ◽  
John P. Hatch ◽  
Sheryl Kane ◽  
Stephen E. Asche ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Electronic Health Records ◽  
Health Records ◽  
Premolar Extraction ◽  
Obstructive Sleep ◽  
Electronic Health

scholarly journals Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1450
Author(s):  
Jayroop Ramesh ◽  
Niha Keeran ◽  
Assim Sagahyroon ◽  
Fadi Aloul
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Electronic Health Records ◽  
Clinical Data ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Health Records ◽  
Obstructive Sleep ◽  
Electronic Health

Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.

420 Evaluating the Rate of Referral for Obstructive Sleep Apnea in a Pre-Doctoral Dental Clinic Using the STOP-Bang Questionnaire

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A166-A166
Author(s):  
Nathan Guess ◽  
Henry Fischbach ◽  
Andy Ni ◽  
Allen Firestone
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
High Risk ◽  
Electronic Health Record ◽  
Health Record ◽  
Dental Clinic ◽  
Neck Size ◽  
Previous Diagnosis ◽  
Obstructive Sleep ◽  
Electronic Health

Abstract Introduction The STOP-Bang Questionnaire is a validated instrument to assess an individual’s risk for obstructive sleep apnea (OSA). The prevalence of OSA is estimated at 20% in the US with only 20% of those individuals properly diagnosed. Dentists are being asked to screen and refer patients at high risk for OSA for definitive diagnosis and treatment. The aim of this study was to determine whether patients in a dental school student clinic who were identified as high-risk for OSA, were referred for evaluation of OSA. Methods All new patients over the age of 18 admitted to The Ohio State University - College of Dentistry complete an “Adult Medical History Form”. Included in this study were 21,312 patients admitted between July 2017 and March 2020. Data were extracted from the history form to determine the STOP-Bang Score for all patients: age, sex, BMI, self-reported snoring-, stopped breathing/choking/gasping while sleeping-, high blood pressure-, neck size over 17” (males) or 16” (females)-, and tiredness. Each positive response is a point, for a maximum of 8 points possible. Additionally, any previous diagnosis of sleep apnea, and the patient’s history of referrals were extracted from the health record. According to clinic policy, if the patient did not have a previous diagnosis for OSA noted in the health history, and scored 5 or more on the STOP-Bang Questionnaire, they should receive a referral for an evaluation for OSA. Notes and referral forms were reviewed to determine if the appropriate referrals occurred for patients at high risk without a previous diagnosis. Results Of the 21,312 patients screened; 1098 (5.2%) screened high-risk for OSA, of which 398 had no previous diagnosis of OSA. Of these 398 patients, none (0%) had referrals for further evaluation for OSA. Conclusion The rate of appropriate referrals from a student dental clinic with an electronic health record was unacceptably low. Continued education and changes to the electronic health record are needed to ensure those at high-risk for OSA are appropriately referred and managed. Support (if any):

Gustatory Function after Radiofrequency Tongue Base Reduction in Patients with Obstructive Sleep Apnea

2011 ◽  
Vol 145 (5) ◽  
pp. 853-857 ◽  
Author(s):  
Young Gyu Eun ◽  
Seung Youp Shin ◽  
Jae Yong Byun ◽  
Myung Gu Kim ◽  
Kun Hee Lee ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Surgical Treatment ◽  
Sleep Apnea ◽  
Medical Center ◽  
Tongue Base ◽  
Safe Procedure ◽  
Gustatory Function ◽  
Obstructive Sleep ◽  
Before And After ◽  
Tongue Base Reduction

Objectives. To investigate the changes in gustatory function as a complication after radiofrequency tongue base reduction (RTBR) in patients with obstructive sleep apnea (OSA). Study Design. Before-and-after study. Setting. Academic tertiary medical center. Subjects and Methods. Thirty-four patients with suspected velopharyngeal collapse only underwent uvulopalatopharyngoplasty (UPPP group). Twenty-five patients with velopharyngeal and retrolingual collapse underwent concurrent UPPP with RTBR (RTBR group). All patients were evaluated before surgery and at 1 and 4 weeks after surgical treatment. A questionnaire was given to assess symptoms of hypogeusia, dysgeusia, hyposmia, and sensation of the tongue. Electrogustometry (EGM) in 4 areas was used to determine gustatory function. Results. Postoperative values for subjective symptoms did not significantly change following surgical treatment in either group. EGM thresholds of all tested in both groups did not significantly change 1 week and 4 weeks after surgery. Conclusions. Gustatory function remained unchanged after RTBR in patients with OSA. The authors suggest that RTBR is a safe procedure in terms of taste sensation in OSA patients.

scholarly journals The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records (Preprint)

2019 ◽  
Author(s):  
Philip Held ◽  
Randy A Boley ◽  
Walter G Faig ◽  
John A O'Toole ◽  
Imran Desai ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Medical Center ◽  
Health Records ◽  
Clinical Notes ◽  
The Road ◽  
Clinical Encounters ◽  
Efficient Data ◽  
Data Points ◽  
Electronic Health ◽  
Using Data

UNSTRUCTURED Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in Chicago, IL to address these concerns with limited burden to clinical workflows. The PEF system proved to be an effective tool with over 98% of all clinical encounters including a completed PEF within 5 months of implementation. In addition, the system has generated over 325,188 unique, readily-accessible data points in under 4 years of use. The PEF system has since been deployed to other settings demonstrating that the system may have broader clinical utility.

Obstructive Sleep Apnea and Its Therapy: Clinical and Polysomnographic Manifestations

PEDIATRICS ◽  
1983 ◽  
Vol 71 (5) ◽  
pp. 737-742 ◽  
Author(s):  
Yitzchak Frank ◽  
Richard E. Kravath ◽  
Charles P. Pollak ◽  
Elliot D. Weitzman
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Airway Obstruction ◽  
Obstructive Sleep Apnea Syndrome ◽  
Medical Center ◽  
Sleep Apnea Syndrome ◽  
Sleep Stages ◽  
Sleep Wake Disorders ◽  
Obstructive Sleep ◽  
Apnea Syndrome

Obstructive sleep apnea syndrome was studied in 32 children, aged 2 to 14 years, in the sleep-wake disorders center at Montefiore Hospital and Medical Center during the years 1977 to 1980. All children under-went all-night polysomnograms; 17 of these children had surgery to relieve airway obstruction and seven had a repeat polysomnographic study 4 to 6 weeks following the surgery. There was a significant improvement in the number of obstructive apneas and in other apnea indices following surgery. There was no significant effect on the durations and the proportions of the various sleep stages, on sleep efficiency, or on the number of awakenings.

Prevalence of undiagnosed obstructive sleep apnea among adult surgical patients in an academic medical center

2009 ◽  
Vol 10 (7) ◽  
pp. 753-758 ◽  
Author(s):  
Kevin J. Finkel ◽  
Adam C. Searleman ◽  
Heidi Tymkew ◽  
Christopher Y. Tanaka ◽  
Leif Saager ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Medical Center ◽  
Academic Medical Center ◽  
Surgical Patients ◽  
Academic Medical ◽  
Obstructive Sleep
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