Predictor Of Mortality In Obstructive Sleep Apnoea: Results of Explainable Deep Learning based Survival Analysis From a 15-Year Follow-Up

Introduction: Obstructive sleep apnoea (OSA) is a respiratory disorder with comorbidities of several nature, from cardiovascular to renal ones. OSA is typically treated through continuous positive airway pressure (CPAP), but more investigations are needed to confirm its benefits. Methods: In this work, survival analysis has been exploited and enhanced with eXplainable Artifical Intelligence (XAI) to investigate the impact of comorbidities and compare the model reliability. The dataset encompasses both clinical and polisomnography-based data for a total of 45 different features. Results: 1394 OSA patients followed for 15 years were enrolled. All the selected features have been studied by means of DL-models and time-dependent XAI techniques. The variables impacting on mortality are reported below in descending order with respect their importance: 1. Age, Years of CPAP, Renal dysfunction, COPD, BMI categories, Sex, and Anemia for the CoxTime; 2. Age, AHI, Renal dysfunction, SaO2 min, Years of CPAP, COPD, and Anemia for the LogHazard. Conclusion: Advancing age, severity of OSA, comorbidity, including chronic kidney disease, COPD and anaemia, are crucial contributors to increased mortality.