Irene L Katzan1, Nicolas R Thompson2, Ken Uchino3, Nancy Foldvary-Schaefer4. 1. Center for Outcomes Research & Evaluation, Cleveland Clinic, Cleveland, OH, USA; Cerebrovascular Center, Cleveland Clinic, Cleveland, OH, USA. Electronic address: katzani@ccf.org. 2. Center for Outcomes Research & Evaluation, Cleveland Clinic, Cleveland, OH, USA. 3. Cerebrovascular Center, Cleveland Clinic, Cleveland, OH, USA. 4. Sleep Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
Abstract
BACKGROUND: A majority of stroke patients suffer from obstructive sleep apnea (OSA), which can go unrecognized as the current OSA screens do not perform well in stroke patients. The objective of this study is to modify the existing OSA screening tools for use in stroke patients. METHODS: The cohort study consisted of patients who completed the validated OSA STOP screen and underwent polysomnography within one year. Six prediction models were created and sensitivity and specificity of various cut points were calculated. RESULTS: There were 208 patients with mean age of 55.4 years; 61.0% had sleep apnea. Models with the highest c-statistics included the STOP items plus BMI, age, and sex (STOP-BAG). Addition of neck circumference and other variables did not significantly improve the models. The STOP-BAG2 model, using continuous variables, had a greater sensitivity of 0.94 (95% CI 0.89-0.98) and specificity 0.60 (95% CI 0.49-0.71) compared to the STOP-BAG model, which used dichotomous variables, and had a sensitivity of 0.91 (95% CI 0.85-0.96) and specificity of 0.48 (95% CI 0.37-0.60). CONCLUSIONS: The STOP-BAG screen can be used to identify cerebrovascular patients at an increased risk of OSA. The use of continuous variables (STOP-BAG2) is preferable if automated score calculation is available. It can improve the efficiency of evaluation for OSA and lead to improved outcomes of patients with cerebrovascular disease.
BACKGROUND: A majority of strokepatients suffer from obstructive sleep apnea (OSA), which can go unrecognized as the current OSA screens do not perform well in strokepatients. The objective of this study is to modify the existing OSA screening tools for use in strokepatients. METHODS: The cohort study consisted of patients who completed the validated OSA STOP screen and underwent polysomnography within one year. Six prediction models were created and sensitivity and specificity of various cut points were calculated. RESULTS: There were 208 patients with mean age of 55.4 years; 61.0% had sleep apnea. Models with the highest c-statistics included the STOP items plus BMI, age, and sex (STOP-BAG). Addition of neck circumference and other variables did not significantly improve the models. The STOP-BAG2 model, using continuous variables, had a greater sensitivity of 0.94 (95% CI 0.89-0.98) and specificity 0.60 (95% CI 0.49-0.71) compared to the STOP-BAG model, which used dichotomous variables, and had a sensitivity of 0.91 (95% CI 0.85-0.96) and specificity of 0.48 (95% CI 0.37-0.60). CONCLUSIONS: The STOP-BAG screen can be used to identify cerebrovascularpatients at an increased risk of OSA. The use of continuous variables (STOP-BAG2) is preferable if automated score calculation is available. It can improve the efficiency of evaluation for OSA and lead to improved outcomes of patients with cerebrovascular disease.
Authors: Devin L Brown; Kevin He; Sehee Kim; Chia-Wei Hsu; Erin Case; Ronald D Chervin; Lynda D Lisabeth Journal: Sleep Med Date: 2020-05-15 Impact factor: 3.492
Authors: Irene L Katzan; Nicolas R Thompson; Harneet K Walia; Douglas E Moul; Nancy Foldvary-Schaefer Journal: J Clin Sleep Med Date: 2019-12-04 Impact factor: 4.062
Authors: Allison R McCarter; Paul C Timm; Paul W Shepard; David J Sandness; Thao Luu; Stuart J McCarter; Lucas Dueffert; Max Dresow; John C Feemster; Gregory D Cascino; Elson L So; Gregory A Worrell; Jeffrey W Britton; Akil Sherif; Keerthi Jaliparthy; Anwar A Chahal; Virend K Somers; Erik K St Louis Journal: Epilepsia Date: 2018-09-24 Impact factor: 5.864
Authors: Lindsay Megenhardt; Sarah Ransdell; Jodi Clark-LoCascio; Roy A Pleasants; Mark Brantly; Charlie Strange Journal: Sleep Breath Date: 2021-04-30 Impact factor: 2.816
Authors: S Liamsombut; R Kaw; L Wang; J Bena; N Andrews; N Collop; T Stierer; M Gillinov; M Tarler; H Kayyali; I Katzan; N Foldvary-Schaefer Journal: Sleep Med Date: 2021-05-15 Impact factor: 4.842