scholarly journals Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea

2020 ◽  
Vol 16 (2) ◽  
pp. 175-183 ◽  
Author(s):  
Brendan T. Keenan ◽  
H. Lester Kirchner ◽  
Olivia J. Veatch ◽  
Kenneth M. Borthwick ◽  
Vicki A. Davenport ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Electronic Health Record ◽  
Health Record ◽  
Obstructive Sleep ◽  
Electronic Health

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):

scholarly journals Electronic health record cue identifies epilepsy patients at risk for obstructive sleep apnea

2018 ◽  
Vol 8 (6) ◽  
pp. 468-471 ◽  
Author(s):  
Martha A. Mulvey ◽  
Aravindhan Veerapandiyan ◽  
David A. Marks ◽  
Xue Ming
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Electronic Health Record ◽  
Seizure Control ◽  
Health Record ◽  
Obstructive Sleep ◽  
Patients At Risk ◽  
Patients With Epilepsy

BackgroundPrior studies have reported that patients with epilepsy have a higher prevalence of obstructive sleep apnea (OSA) that contributes to poor seizure control. Detection and treatment of OSA can improve seizure control in some patients with epilepsy. In this study, we sought to develop, implement, and evaluate the effectiveness of an electronic health record (EHR) alert to screen for OSA in patients with epilepsy.MethodsA 3-month retrospective chart review was conducted of all patients with epilepsy >18 years of age who were evaluated in our epilepsy clinics prior to the intervention. An assessment for obstructive sleep apnea (AOSA) consisting of 12 recognized risk factors for OSA was subsequently developed and embedded in the EHR. The AOSA was utilized for a 3-month period. Patients identified with 2 or more risk factors were referred for polysomnography. A comparison was made to determine if there was a difference in the number of patients at risk for OSA detected and referred for polysomnography with and without an EHR alert to screen for OSA.ResultsThere was a significant increase in OSA patient recognition. Prior to the EHR alert, 25/346 (7.23%) patients with epilepsy were referred for a polysomnography. Postintervention, 405/414 patients were screened using an EHR alert for AOSA and 134/405 (33.1%) were referred for polysomnography (p < 0.001).ConclusionAn intervention with AOSA cued in the EHR demonstrated markedly improved identification of epilepsy patients at risk for OSA and referral for polysomnography.

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 Effectiveness of Electronic Health Interventions in Improving Treatment Adherence for Adults With Obstructive Sleep Apnea: Meta-Analytic Review

10.2196/16972 ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. e16972 ◽  
Author(s):  
Jiska Joëlle Aardoom ◽  
Lisa Loheide-Niesmann ◽  
Hans C Ossebaard ◽  
Heleen Riper
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Treatment Adherence ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Cpap Adherence ◽  
Cpap Treatment ◽  
Obstructive Sleep ◽  
Analytic Review ◽  
Electronic Health

Background Poor adherence to continuous positive airway pressure (CPAP) treatment by adults with obstructive sleep apnea (OSA) is a common issue. Strategies delivered by means of information and communication technologies (ie, electronic health [eHealth]) can address treatment adherence through patient education, real-time monitoring of apnea symptoms and CPAP adherence in daily life, self-management, and early identification and subsequent intervention when device or treatment problems arise. However, the effectiveness of available eHealth technologies in improving CPAP adherence has not yet been systematically studied. Objective This meta-analytic review was designed to investigate the effectiveness of a broad range of eHealth interventions in improving CPAP treatment adherence. Methods We conducted a systematic literature search of the databases of Cochrane Library, PsycINFO, PubMed, and Embase to identify relevant randomized controlled trials in adult OSA populations. The risk of bias in included studies was examined using seven items of the Cochrane Collaboration risk-of-bias tool. The meta-analysis was conducted with comprehensive meta-analysis software that computed differences in mean postintervention adherence (MD), which was defined as the average number of nightly hours of CPAP use. Results The meta-analysis ultimately included 18 studies (N=5429 adults with OSA) comprising 22 comparisons between experimental and control conditions. Postintervention data were assessed at 1 to 6 months after baseline, depending on the length of the experimental intervention. eHealth interventions increased the average nightly use of CPAP in hours as compared with care as usual (MD=0.54, 95% CI 0.29-0.79). Subgroup analyses did not reveal significant differences in effects between studies that used eHealth as an add-on or as a replacement to care as usual (P=.95), between studies that assessed stand-alone eHealth and blended strategies combining eHealth with face-to-face care (P=.23), or between studies of fully automated interventions and guided eHealth interventions (P=.83). Evidence for the long-term follow-up effectiveness of eHealth adherence interventions remains undecided owing to a scarcity of available studies and their mixed results. Conclusions eHealth interventions for adults with OSA can improve adherence to CPAP in the initial months after the start of treatment, increasing the mean nightly duration of use by about half an hour. Uncertainty still exists regarding the timing, duration, intensity, and specific types of eHealth interventions that could be most effectively implemented by health care providers.

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 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 Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership's Common Data Model: a pilot feasibility study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeong-Whun Kim ◽  
Seok Kim ◽  
Borim Ryu ◽  
Wongeun Song ◽  
Ho-Young Lee ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Data Model ◽  
University Hospital ◽  
Common Data Model ◽  
Health Record ◽  
Medical Outcome ◽  
Obstructive Sleep ◽  
Electronic Health ◽  
Sleep Laboratories ◽  
Pilot Feasibility Study

AbstractWell-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format—the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract–transform–load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data. We believe that this study represents the first CDM conversion of PSG. In the future, CDM transformation will enable network research in sleep medicine and will contribute to presenting more relevant clinical evidence.

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.

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