BACKGROUND: Obstructive sleep apnea (OSA) is a largely underdiagnosed, common condition, which is important to diagnose preoperatively because it has implications for perioperative management. Our purpose in this study was to identify independent clinical predictors of a diagnosis of OSA in a general surgical population, develop a perioperative sleep apnea prediction (P-SAP) score based on these variables, and validate the P-SAP score against standard overnight polysomnography. METHODS: A retrospective, observational study was designed to identify patients with a known diagnosis of OSA. Independent predictors of a diagnosis of OSA were derived by logistic regression, based on which prediction tool (P-SAP score) was developed. The P-SAP score was then validated in patients undergoing overnight polysomnography. RESULTS: The P-SAP score was derived from 43,576 adult cases undergoing anesthesia. Of these, 3884 patients (7.17%) had a documented diagnosis of OSA. Three demographic variables: age > 43 years, male gender, and obesity; 3 history variables: history of snoring, diabetes mellitus Type 2, and hypertension; and 3 airway measures: thick neck, modified Mallampati class 3 or 4, and reduced thyromental distance were identified as independent predictors of a diagnosis of OSA. A diagnostic threshold P-SAP score > or = 2 showed excellent sensitivity (0.939) but poor specificity (0.323), whereas for a P-SAP score > or = 6, sensitivity was poor (0.239) with excellent specificity (0.911). Validation of this P-SAP score was performed in 512 patients with similar accuracy. CONCLUSION: The P-SAP score predicts diagnosis of OSA with dependable accuracy across mild to severe disease. The elements of the P-SAP score are derived from a typical university hospital surgical population.
BACKGROUND: Obstructive sleep apnea (OSA) is a largely underdiagnosed, common condition, which is important to diagnose preoperatively because it has implications for perioperative management. Our purpose in this study was to identify independent clinical predictors of a diagnosis of OSA in a general surgical population, develop a perioperative sleep apnea prediction (P-SAP) score based on these variables, and validate the P-SAP score against standard overnight polysomnography. METHODS: A retrospective, observational study was designed to identify patients with a known diagnosis of OSA. Independent predictors of a diagnosis of OSA were derived by logistic regression, based on which prediction tool (P-SAP score) was developed. The P-SAP score was then validated in patients undergoing overnight polysomnography. RESULTS: The P-SAP score was derived from 43,576 adult cases undergoing anesthesia. Of these, 3884 patients (7.17%) had a documented diagnosis of OSA. Three demographic variables: age > 43 years, male gender, and obesity; 3 history variables: history of snoring, diabetes mellitus Type 2, and hypertension; and 3 airway measures: thick neck, modified Mallampati class 3 or 4, and reduced thyromental distance were identified as independent predictors of a diagnosis of OSA. A diagnostic threshold P-SAP score > or = 2 showed excellent sensitivity (0.939) but poor specificity (0.323), whereas for a P-SAP score > or = 6, sensitivity was poor (0.239) with excellent specificity (0.911). Validation of this P-SAP score was performed in 512 patients with similar accuracy. CONCLUSION: The P-SAP score predicts diagnosis of OSA with dependable accuracy across mild to severe disease. The elements of the P-SAP score are derived from a typical university hospital surgical population.
Authors: Francisco Del Olmo-Arroyo; Ricardo Hernandez-Castillo; Antonio Soto; Juancarlo Martínez; William Rodríguez-Cintrón Journal: Sleep Breath Date: 2015-02-03 Impact factor: 2.816
Authors: Babak Mokhlesi; Margaret D Hovda; Benjamin Vekhter; Vineet M Arora; Frances Chung; David O Meltzer Journal: Chest Date: 2013-09 Impact factor: 9.410
Authors: Frances Chung; Stavros G Memtsoudis; Satya Krishna Ramachandran; Mahesh Nagappa; Mathias Opperer; Crispiana Cozowicz; Sara Patrawala; David Lam; Anjana Kumar; Girish P Joshi; John Fleetham; Najib Ayas; Nancy Collop; Anthony G Doufas; Matthias Eikermann; Marina Englesakis; Bhargavi Gali; Peter Gay; Adrian V Hernandez; Roop Kaw; Eric J Kezirian; Atul Malhotra; Babak Mokhlesi; Sairam Parthasarathy; Tracey Stierer; Frank Wappler; David R Hillman; Dennis Auckley Journal: Anesth Analg Date: 2016-08 Impact factor: 5.108
Authors: Christina H Shin; Sebastian Zaremba; Scott Devine; Milcho Nikolov; Tobias Kurth; Matthias Eikermann Journal: BMJ Open Date: 2016-01-13 Impact factor: 2.692