Breathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes.OBJECTIVE To evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection. DESIGN, SETTING, AND PARTICIPANTSA cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020. MAIN OUTCOMES AND MEASURES Accuracy of OSA prediction models.RESULTS A total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (β = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (β = −0.20; 95% CI, −0.35 to −0.05) were found to be associated with estimation error. CONCLUSIONS AND RELEVANCEIn this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.

Background: Common data model (CDM) is a standardized data structure defined to efficiently use different sources in hospitals. A study using the CDM is scarce for orthopedic outcome researches due to the complexity of variables. We aimed to test the feasibility of applying CDM in the orthopedic field and analyzed risk factors for periprosthetic joint infection (PJI) after total joint arthroplasty (TJA) using CDM.Methods: We undertook a retrospective cohort study of all primary and revision hip and knee TJAs at our institution from January 2003 to October 2017. We identified potential risk factors for PJI after TJAs in the literatures, which included preoperative demographic/social factors, previous medical history, intraoperative factors, laboratory results and others. The data sourced from EMR was extracted, transformed, and loaded into CDM.Results: Variables such as demographic/social factors, medical history and laboratory results could be converted into CDM, but the other known risk factors could not. In total, 12,320 primary hip and knee TJAs and 120 revision arthroplasties were identified. Among them, 34 revisions were done because of PJI. Risk factors of PJI were hypertension and urinary tract infection after total hip arthroplasty, and age (70-79 years), male sex, anemia, steroid use, and urinary tract infection after total knee arthroplasty. Conclusions: This study demonstrates that orthopedic outcome researches using CDM is feasible although data converting to CDM was possible for limited factors. Further data transforming technologies need to be developed to analyze more factors relevant to orthopedic area, such as intraoperative factors and imaging findings.