Dylan Bertoni1, Laura M Sterni2, Kevin D Pereira1, Gautam Das3, Amal Isaiah4. 1. Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA. 2. Division of Pulmonology, Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA. 3. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA. 4. Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA. aisaiah@som.umaryland.edu.
Abstract
BACKGROUND: Approximately 500,000 children undergo tonsillectomy and adenoidectomy (T&A) annually for treatment of obstructive sleep disordered breathing (oSDB). Although polysomnography is beneficial for preoperative risk stratification in these children, its expanded use is limited by the associated costs and resources needed. Therefore, we used machine learning and data from potentially wearable sensors to identify children needing postoperative overnight monitoring based on the polysomnographic severity of oSDB. METHODS: Children aged 2-17 years undergoing polysomnography were included. Six machine learning models were created using (i) clinical parameters and (ii) nocturnal actigraphy and oxygen desaturation index. The prediction performance for polysomnography-derived severity of oSDB measured by apnea hypopnea index (AHI) >2 and >10 were evaluated. RESULTS: One hundred and ninety children were included. One hundred and eight were male (57%), mean age was 6.7 years [95% confidence interval; 6.1, 7.2], and mean AHI was 10.6 [7.8, 13.4]. Predictive performance utilizing clinical parameters was poor for both AHI > 2 (accuracy range: 48-56% for all models) and AHI > 10 (50-61%). Combining oximetry and actigraphy improved the accuracy to 87-89% for AHI > 2 and 95-96% for AHI > 10. CONCLUSIONS: Machine learning with oximetry and actigraphy identifies most children needing overnight monitoring as determined by polysomnographic severity of oSDB, supporting a potential resource-conscious screening pathway for children undergoing T&A. IMPACT: We provide proof of principle for the utility of machine learning, oximetry, and actigraphy to screen for severe obstructive sleep apnea syndrome (OSAS) in children. Clinical parameters perform poorly in predicting the severity of OSAS, which is confirmed in the current study. The predictive accuracy for severe OSAS was improved by a smaller subset of quantifiable physiologic parameters, such as oximetry. The results of this study support a lower cost, patient-friendly screening pathway to identify children in need of in-hospital observation after surgery.
BACKGROUND: Approximately 500,000 children undergo tonsillectomy and adenoidectomy (T&A) annually for treatment of obstructive sleep disordered breathing (oSDB). Although polysomnography is beneficial for preoperative risk stratification in these children, its expanded use is limited by the associated costs and resources needed. Therefore, we used machine learning and data from potentially wearable sensors to identify children needing postoperative overnight monitoring based on the polysomnographic severity of oSDB. METHODS:Children aged 2-17 years undergoing polysomnography were included. Six machine learning models were created using (i) clinical parameters and (ii) nocturnal actigraphy and oxygen desaturation index. The prediction performance for polysomnography-derived severity of oSDB measured by apnea hypopnea index (AHI) >2 and >10 were evaluated. RESULTS: One hundred and ninety children were included. One hundred and eight were male (57%), mean age was 6.7 years [95% confidence interval; 6.1, 7.2], and mean AHI was 10.6 [7.8, 13.4]. Predictive performance utilizing clinical parameters was poor for both AHI > 2 (accuracy range: 48-56% for all models) and AHI > 10 (50-61%). Combining oximetry and actigraphy improved the accuracy to 87-89% for AHI > 2 and 95-96% for AHI > 10. CONCLUSIONS: Machine learning with oximetry and actigraphy identifies most children needing overnight monitoring as determined by polysomnographic severity of oSDB, supporting a potential resource-conscious screening pathway for children undergoing T&A. IMPACT: We provide proof of principle for the utility of machine learning, oximetry, and actigraphy to screen for severe obstructive sleep apnea syndrome (OSAS) in children. Clinical parameters perform poorly in predicting the severity of OSAS, which is confirmed in the current study. The predictive accuracy for severe OSAS was improved by a smaller subset of quantifiable physiologic parameters, such as oximetry. The results of this study support a lower cost, patient-friendly screening pathway to identify children in need of in-hospital observation after surgery.