Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index

Background and Objective: Obstructive sleep apnea (OSA) is an important health problem, which is commonly under-diagnosed especially in workplace settings. We tried to obtain a model with more objective variables due to the greater reliability in occupational settings. Materials and Methods: A total...
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Autor*in:	Arezu Najafi [verfasserIn] Elham Afzalinejad [verfasserIn] Khosro Sadeghniiat-Haghighi [verfasserIn] Zahra Banafsheh Alemohammad [verfasserIn] Samaneh Akbarpour [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Sleep apnea; Obstructive; Polysomnography; Predictive value of tests

Übergeordnetes Werk:	In: Journal of Sleep Sciences - Tehran University of Medical Sciences, 2020, 4(2020), 3-4
Übergeordnetes Werk:	volume:4 ; year:2020 ; number:3-4

Links:	Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ051094010

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520			\|a Background and Objective: Obstructive sleep apnea (OSA) is an important health problem, which is commonly under-diagnosed especially in workplace settings. We tried to obtain a model with more objective variables due to the greater reliability in occupational settings. Materials and Methods: A total of 374 suspected patients with OSA who underwent their first olysomnography (PSG) at Baharloo Sleep Clinic in Tehran, Iran, were enrolled in the study. Before PSG, all patients completed a ques-tionnaire including demographic characteristics. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured for all participants. Furthermore, a blood sample was collected for measuring fasting blood sugar (FBS) and hemoglobin A1c (HbA1c). All the patients underwent full PSG. Respiratory Disturbance Index (RDI) was calculated and recorded for all patients. Different multiple adjusted logistic regression models were constructed to find the best model for prediction of OSA. Results: A total of 271 (72.5%) participants were men. The mean age and body mass index (BMI) were 48.58 ± 13.04 years and 30.4 ± 5.0 kg/m2, respectively. The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals. Conclusion: In this study, we considered only objective parameters to predict OSA which enhances reliability for diag-nosis especially in occupational settings.
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allfieldsSound	(DE-627)DOAJ051094010 (DE-599)DOAJ484b2873fd734607952bde165d323191 DE-627 ger DE-627 rakwb eng Arezu Najafi verfasserin aut Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and Objective: Obstructive sleep apnea (OSA) is an important health problem, which is commonly under-diagnosed especially in workplace settings. We tried to obtain a model with more objective variables due to the greater reliability in occupational settings. Materials and Methods: A total of 374 suspected patients with OSA who underwent their first olysomnography (PSG) at Baharloo Sleep Clinic in Tehran, Iran, were enrolled in the study. Before PSG, all patients completed a ques-tionnaire including demographic characteristics. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured for all participants. Furthermore, a blood sample was collected for measuring fasting blood sugar (FBS) and hemoglobin A1c (HbA1c). All the patients underwent full PSG. Respiratory Disturbance Index (RDI) was calculated and recorded for all patients. Different multiple adjusted logistic regression models were constructed to find the best model for prediction of OSA. Results: A total of 271 (72.5%) participants were men. The mean age and body mass index (BMI) were 48.58 ± 13.04 years and 30.4 ± 5.0 kg/m2, respectively. The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals. Conclusion: In this study, we considered only objective parameters to predict OSA which enhances reliability for diag-nosis especially in occupational settings. Sleep apnea; Obstructive; Polysomnography; Predictive value of tests Medicine R Elham Afzalinejad verfasserin aut Khosro Sadeghniiat-Haghighi verfasserin aut Zahra Banafsheh Alemohammad verfasserin aut Samaneh Akbarpour verfasserin aut In Journal of Sleep Sciences Tehran University of Medical Sciences, 2020 4(2020), 3-4 (DE-627)1742335519 24762946 nnns volume:4 year:2020 number:3-4 https://doaj.org/article/484b2873fd734607952bde165d323191 kostenfrei https://jss.tums.ac.ir/index.php/jss/article/view/138 kostenfrei https://doaj.org/toc/2476-2938 Journal toc kostenfrei https://doaj.org/toc/2476-2946 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2020 3-4
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The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals. 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author	Arezu Najafi
spellingShingle	Arezu Najafi misc Sleep apnea; Obstructive; Polysomnography; Predictive value of tests misc Medicine misc R Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index
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topic_title	Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index Sleep apnea; Obstructive; Polysomnography; Predictive value of tests
topic	misc Sleep apnea; Obstructive; Polysomnography; Predictive value of tests misc Medicine misc R
topic_unstemmed	misc Sleep apnea; Obstructive; Polysomnography; Predictive value of tests misc Medicine misc R
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title	Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index
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title_full	Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index
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author_browse	Arezu Najafi Elham Afzalinejad Khosro Sadeghniiat-Haghighi Zahra Banafsheh Alemohammad Samaneh Akbarpour
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title_sort	development of an objective tool for predicting obstructive sleep apnea among adults: pan apnea index
title_auth	Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index
abstract	Background and Objective: Obstructive sleep apnea (OSA) is an important health problem, which is commonly under-diagnosed especially in workplace settings. We tried to obtain a model with more objective variables due to the greater reliability in occupational settings. Materials and Methods: A total of 374 suspected patients with OSA who underwent their first olysomnography (PSG) at Baharloo Sleep Clinic in Tehran, Iran, were enrolled in the study. Before PSG, all patients completed a ques-tionnaire including demographic characteristics. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured for all participants. Furthermore, a blood sample was collected for measuring fasting blood sugar (FBS) and hemoglobin A1c (HbA1c). All the patients underwent full PSG. Respiratory Disturbance Index (RDI) was calculated and recorded for all patients. Different multiple adjusted logistic regression models were constructed to find the best model for prediction of OSA. Results: A total of 271 (72.5%) participants were men. The mean age and body mass index (BMI) were 48.58 ± 13.04 years and 30.4 ± 5.0 kg/m2, respectively. The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals. Conclusion: In this study, we considered only objective parameters to predict OSA which enhances reliability for diag-nosis especially in occupational settings.
abstractGer	Background and Objective: Obstructive sleep apnea (OSA) is an important health problem, which is commonly under-diagnosed especially in workplace settings. We tried to obtain a model with more objective variables due to the greater reliability in occupational settings. Materials and Methods: A total of 374 suspected patients with OSA who underwent their first olysomnography (PSG) at Baharloo Sleep Clinic in Tehran, Iran, were enrolled in the study. Before PSG, all patients completed a ques-tionnaire including demographic characteristics. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured for all participants. Furthermore, a blood sample was collected for measuring fasting blood sugar (FBS) and hemoglobin A1c (HbA1c). All the patients underwent full PSG. Respiratory Disturbance Index (RDI) was calculated and recorded for all patients. Different multiple adjusted logistic regression models were constructed to find the best model for prediction of OSA. Results: A total of 271 (72.5%) participants were men. The mean age and body mass index (BMI) were 48.58 ± 13.04 years and 30.4 ± 5.0 kg/m2, respectively. The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals. Conclusion: In this study, we considered only objective parameters to predict OSA which enhances reliability for diag-nosis especially in occupational settings.
abstract_unstemmed	Background and Objective: Obstructive sleep apnea (OSA) is an important health problem, which is commonly under-diagnosed especially in workplace settings. We tried to obtain a model with more objective variables due to the greater reliability in occupational settings. Materials and Methods: A total of 374 suspected patients with OSA who underwent their first olysomnography (PSG) at Baharloo Sleep Clinic in Tehran, Iran, were enrolled in the study. Before PSG, all patients completed a ques-tionnaire including demographic characteristics. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured for all participants. Furthermore, a blood sample was collected for measuring fasting blood sugar (FBS) and hemoglobin A1c (HbA1c). All the patients underwent full PSG. Respiratory Disturbance Index (RDI) was calculated and recorded for all patients. Different multiple adjusted logistic regression models were constructed to find the best model for prediction of OSA. Results: A total of 271 (72.5%) participants were men. The mean age and body mass index (BMI) were 48.58 ± 13.04 years and 30.4 ± 5.0 kg/m2, respectively. The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals. Conclusion: In this study, we considered only objective parameters to predict OSA which enhances reliability for diag-nosis especially in occupational settings.
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title_short	Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index
url	https://doaj.org/article/484b2873fd734607952bde165d323191 https://jss.tums.ac.ir/index.php/jss/article/view/138 https://doaj.org/toc/2476-2938 https://doaj.org/toc/2476-2946
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author2	Elham Afzalinejad Khosro Sadeghniiat-Haghighi Zahra Banafsheh Alemohammad Samaneh Akbarpour
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The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals. 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Development of an Objective Tool for Predicting Obstructive Sleep Apnea among Adults: PAN Apnea Index

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