Introduction: Obstructive sleep apnea (OSA) is a common breathing disorder with numerous health consequences, including greater risk of complications perioperatively. Undiagnosed OSA is known to place surgical patients at a higher risk of serious adverse events, including stroke and death. Polysomnography (PSG) assessment is the current gold standard test for diagnosing OSA. However, due to the significant time commitment and cost associated with PSG, a substantial number of OSA patients go undiagnosed before the perioperative period. Although the STOP-Bang questionnaire screening tool is currently used to help detect OSA patients, the low specificity to screen people without the disease is considered a major limitation. There is a clear need to develop a quick and effective prediction rule with higher overall accuracy to help streamline OSA diagnosis. Tracheal breathing sound analysis in awake patients at the bedside has shown potential to screen OSA patients with higher specificity compared to the STOP-Bang questionnaire. To date, no screening tools exist to detect OSA patients that combine the results of breathing sound analysis and STOP-Bang. Objectives: The present study aimed to develop a prediction rule, using both breathing sound analysis and variables in the STOP-Bang questionnaire, to better streamline the diagnosis of OSA. Methods: This prospective cohort study recruited patients referred for PSG at the Ottawa Hospital Sleep Centre from November 2016 to May 2017. The study conduct was approved by the Ottawa Health Science Network Research Ethics Board (#20160494-01H). After obtaining informed consent, anthropomorphic, breathing sound recordings, and STOP-Bang questionnaire data was collected from over 400 consenting patients. All patients that met the eligibility criteria were included. The breathing sound analysis and STOP-Bang results were utilized to design a prediction rule using logistic regression. Sensitivity, specificity, and likelihood ratio were used to compare the diagnostic performance of the final model. Results: Of the 439 consenting study participants, 280 study participants data were eligible for inclusion in the logistic regression analysis. Physician sleep specialists diagnosed 114 participants (41%) with moderate-to-severe OSA and 166 participants (59%) with normal-to-mild OSA. At a predicted probability of moderate-to-severe OSA greater than or equal to 0.5, breathing sound analysis had a similar sensitivity of 75.9 (95%CI; 65.4, 82.0) and higher specificity of 74.5% (95%CI; 68.5, 82.0) when compared to STOP-Bang with a sensitivity and specificity of 68.4% (95%CI; 58.9, 76.6) and 63.2% (95%CI: 55.0, 70.1), respectively. The sensitivity and specificity for the Safe-OSA rule, obtained by combining breathing sound analysis and STOP-Bang variables, were determined to be 75.4% (95%CI; 65.4, 82.0) and 74.5% (95%CI; 68.5, 82.0), respectively. A sensitivity analysis using a likelihood ratio test showed that breathing sound analysis contributed significantly to the performance of the Safe-OSA rule. The Safe-OSA rule was determined to be reasonably discriminative and well calibrated. The five-fold cross-validation showed similar results for the final model in the derivation and testing subsamples, which provides support for the internal validity of the Safe-OSA rule in our study population. Conclusion: The present study lends further support for the future testing of tracheal breathing sound analysis as a potential method to screen for moderate-to-severe OSA to help streamline patient care in the perioperative setting. Trial registration: ClinicalTrials.gov identifier NCT02987283.