806 Barriers to screening and diagnosis of obstructive sleep apnea during inpatient traumatic brain injury rehabilitation

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A314-A315
Author(s):  
Bridget Cotner ◽  
Risa Nakase-Richardson ◽  
Becky Gius ◽  
Lauren Fournier ◽  
Alexa Watach ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Obstructive Sleep Apnea ◽  
Brain Injury ◽  
Sleep Apnea ◽  
Inpatient Rehabilitation ◽  
Screening Tools ◽  
Diagnostic Tools ◽  
Evidence Based ◽  
Implementation Barriers ◽  
Obstructive Sleep

Abstract Introduction Obstructive Sleep Apnea (OSA) is prevalent after moderate to severe traumatic brain injury (TBI) and may diminish recovery when left untreated. Despite the demonstrated importance of treating OSA following TBI, assessment for OSA during or soon after inpatient rehabilitation for TBI is limited. Little is known about barriers to implementing OSA screening and early diagnosis during inpatient rehabilitation thus hindering the translation of evidence-based OSA assessment procedures into clinical practice and potentially delaying necessary OSA treatment. The current analysis explored facilitators and barriers to implementing OSA screening tools in an inpatient rehabilitation setting from the perspectives of end user stakeholders. Methods Patients, families, industry, clinical providers and administrators participated in a two-day meeting following completion of a diagnostic clinical trial of OSA screening and diagnostic tools during inpatient rehabilitation. Stakeholders were provided with open ended questions generated by study investigators and given the opportunity to respond on paper or a “graffiti wall” (i.e., white board). Example questions include “What are the greatest needs of the healthcare system related to sleep apnea and TBI?” and “What are the key things we need to consider to move results into real-world practice?” Qualitative content analyses using a rapid matrix approach were conducted from stakeholder feedback obtained during the two-day meeting, which included a guided review of emerging OSA research and discussion of potential implementation barriers of OSA assessment during inpatient rehabilitation. Results Improved screening and treatment practices for OSA were the greatest needs identified. To meet these needs, stakeholders identified the importance of improving patient, family, and staff understanding of OSA (e.g., health literacy) and other sleep disorders through education; inpatient rehabilitation access to resources (technology; sleep providers); and reimbursement for additional inpatient procedures. Conclusion Although treatment of OSA is crucial for recovery during inpatient rehabilitation following TBI, barriers to earlier recognition, diagnosis, and treatment of OSA exists across several different domains, including education, resources, and funding policies. Findings support future implementation efforts to translate evidence-based care into practice to improve patient outcomes. Support (if any) PCORI-NCT03033901

The Use of Nasal Trumpet in a Traumatic Brain Injury Patient With Obstructive Sleep Apnea

PM&R ◽  
2009 ◽  
Vol 1 (10) ◽  
pp. 977-979 ◽  
Author(s):  
Jaspal R. Singh ◽  
Miriam Segal ◽  
Richard Malone ◽  
Mohammed Zubair
Keyword(s):  
Traumatic Brain Injury ◽  
Obstructive Sleep Apnea ◽  
Brain Injury ◽  
Sleep Apnea ◽  
Traumatic Brain Injury Patient ◽  
Obstructive Sleep ◽  
Injury Patient

788 Is Dysphagia a Marker for Obstructive Sleep Apnea (OSA) in Traumatic Brain Injury (TBI) Patients: A VA TBI Model System Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A307-A307
Author(s):  
Christine Matarese ◽  
Risa Nakase-Richardson ◽  
Emily Almeida ◽  
John Whyte ◽  
Sagarika Nallu ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Sleep Apnea ◽  
Central Sleep Apnea ◽  
Inpatient Rehabilitation ◽  
Post Injury ◽  
Obstructive Sleep ◽  
Significant Difference ◽  
Level 1 ◽  
Peg Tube

Abstract Introduction Recent work has highlighted prevalent obstructive not central sleep apnea following traumatic brain injury (TBI). Treatment of comorbid OSA may facilitate neurologic recovery but widespread screening is limited. Mixed support exists for the presence of dysphagia as a biomarker of OSA in the general population and stroke patients. Dysphagia is common following TBI; however, no study has examined the relation between OSA and dysphagia in this cohort. Leveraging data from a recent six-center clinical trial of OSA and TBI during inpatient rehabilitation, this secondary analysis examined the association between OSA severity indices and proxy measures of dysphagia. Methods Level 1 polysomnography (PSG) was used to assess OSA (AHI ≥ 5 and ≥ 15) during inpatient rehabilitation for the overall sample (N=248; 203 male; 60.6% severe injury) evaluated at a median of 120.6 days post-TBI and subset ≤ 60 days post-injury. Dysphagia was approximated as the presence of a PEG tube and/or a modified texture diet (MTD) on the day of PSG. Chi square and Fisher’s Exact tests were utilized for group comparisons. Results As previously reported, OSA in this cohort was prevalent (68.2% (n=169) at AHI ≥ 5 and 33.5% (n=83) AHI ≥ 15) with predominantly obstructive events. 27.4% (n=68) met criteria for dysphagia combining proxy measures (34 peg; 49 MTD). No significant difference was found for presence of dysphagia across OSA severity cutoffs (AHI ≥ 5 & 15; p=0.1029 & 0.5959). When examining OSA across the individual proxy measures, persons without a peg tube were significantly more likely to have OSA at AHI ≥ 5 (62.5% vs 5.65%; p=0.0003) and AHI ≥ 15 (31.05% vs 2.42%; p=0.0353). When examining participants less than 60 days post-TBI, the group differences remained. Conclusion The incidence of dysphagia in TBI patients, as indexed by a modified diet or presence of a feeding tube, was not elevated in those with OSA. Sample bias (for undergoing Level 1 PSG and improvement facilitating inpatient rehabilitation admission) may have contributed to findings. Finally, future work with more sensitive indices of dysphagia is needed to accurately evaluate this association. Support (if any) PCORI (CER-1511–33005), NIIDLRR (90DPTB0004)

Cognitive Impairment in Patients With Traumatic Brain Injury and Obstructive Sleep Apnea

2007 ◽  
Vol 88 (10) ◽  
pp. 1284-1288 ◽  
Author(s):  
Mark C. Wilde ◽  
Richard J. Castriotta ◽  
Jenny M. Lai ◽  
Strahil Atanasov ◽  
Brent E. Masel ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Obstructive Sleep Apnea ◽  
Cognitive Impairment ◽  
Brain Injury ◽  
Sleep Apnea ◽  
Obstructive Sleep

801 Obstructive Sleep Apnea Symptoms Predict Cognitive Function Following Mild Traumatic Brain Injury

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A312-A312
Author(s):  
Andrew Le ◽  
Natalie Dailey ◽  
Michael Grandner ◽  
William Killgore
Keyword(s):  
Traumatic Brain Injury ◽  
Obstructive Sleep Apnea ◽  
Brain Injury ◽  
Reaction Time ◽  
Sleep Apnea ◽  
Cognitive Function ◽  
Cognitive Processing ◽  
Sleep Disturbances ◽  
Healthy Controls ◽  
Obstructive Sleep

Abstract Introduction Sleep disturbances are commonly reported following mild traumatic brain injury (mTBI). Specifically, one of these disturbances is obstructive sleep apnea (OSA), which involves repeated episodes of reduced upper-airway flow during sleep. When compared to the general population, OSA is reported at a much higher rate among the mTBI population. However, little research has investigated the relationship between OSA and cognitive performance among the mTBI population. We predicted that in those who suffered a mTBI, symptoms of sleep apnea would be predictive lower cognitive processing. Methods We collected data from 37 healthy controls (Mean age = 24.3 □ 5.8) and 145 participants with mTBI (Mean age = 24.3 □ 6.8), ranging from 2 weeks to 12 months post-injury. Participants completed the Pittsburg Sleep Quality Index (PSQI) including questions indicative of OSA, such as “cannot breathe comfortably” and “cough or snore loudly” during sleep. We calculated the PSQI Sleep Disturbance (PSQI-SD) composite score, which ranged from 0 to 2. Participants completed the Automated Neuropsychological Assessment Metrics (ANAM4), a novel computer-based assessment, to measure reaction time (RT). Results When comparing the percentage of participants in each group endorsing sleep disturbances on the PSQI-SD, we found a significant difference in the proportion of individuals scoring a 2 on PSQI-SD between the groups (□2(2) = 13.55, p = .001). In healthy controls, 8% scored 0, 89% scored 1, and 3% scored 2 on the PSQI-SD. In contrast, following mTBI, 1.4% scored 0, 72.4% scored 1, and 26.2% scored 2 on the PSQI-SD. Furthermore, PSQI-SD significantly predicted RT (□ = .18, p = .03) in the mTBI group, a relationship not observed in the control group (□ = .30, p = .07). Conclusion mTBI increases the incidence of sleep disturbances and symptoms related to OSA. Furthermore, sleep disturbances and OSA-related symptoms were predictive of cognitive performance in individuals who sustained a mTBI, but not healthy controls. Increases in PSQI-SD scores were associated with increased RT, indicating greater deficits in cognitive function, specifically reaction time. These data provide evidence that higher severity in respiratory symptoms relating to sleep apnea hinders cognitive processing, particularly for individuals who have suffered a mTBI. Support (if any):

Risk Factors of Obstructive Sleep Apnea among Individuals with Moderate to Severe Traumatic Brain Injury

2019 ◽  
Vol 100 (12) ◽  
pp. e199
Author(s):  
Kimberley Monden ◽  
Dave Mellick ◽  
Kathleen Bell ◽  
Jesse Fann ◽  
Jeanne Hoffman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Traumatic Brain Injury ◽  
Obstructive Sleep Apnea ◽  
Brain Injury ◽  
Sleep Apnea ◽  
Severe Traumatic Brain Injury ◽  
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):

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