Intermittent hypoxemia during hemodialysis: AI-based identification of arterial oxygen saturation saw-tooth pattern.

Abstract

Background: Maintenance hemodialysis patients experience high morbidity and mortality, primarily from cardiovascular and infectious diseases. It was discovered recently that low arterial oxygen saturation (SaO₂) is associated with a pro-inflammatory phenotype and poor patient outcomes. Sleep apnea is highly prevalent in maintenance hemodialysis patients and may contribute to intradialytic hypoxemia. In sleep apnea, normal respiration patterns are disrupted by episodes of apnea because of either disturbed respiratory control (i.e., central sleep apnea) or upper airway obstruction (i.e., obstructive sleep apnea). Intermittent SaO₂ saw-tooth patterns are a hallmark of sleep apnea. Continuous intradialytic measurements of SaO₂ provide an opportunity to follow the temporal evolution of SaO₂ during hemodialysis. Using artificial intelligence, we aimed to automatically identify patients with repetitive episodes of intermittent SaO₂ saw-tooth patterns.

Methods: The analysis utilized intradialytic SaO₂ measurements by the Crit-Line device (Fresenius Medical Care, Waltham, MA). In patients with an arterio-venous fistula as vascular access, this FDA approved device records 150 SaO₂ measurements per second in the extracorporeal blood circuit of the hemodialysis system. The average SaO₂ of a 10-second segment is computed and streamed to the cloud. Periods comprising thirty 10-second segments (i.e., 300 s or five minutes) were independently adjudicated by two researchers for the presence or absence of SaO₂ saw-tooth pattern. We built one-dimensional convolutional neural networks (1D-CNN), a state-of-the-art deep learning method, for SaO₂ pattern classification and randomly assigned SaO₂ time series segments to either a training (80%) or a test (20%) set.

Results: We analyzed 4,075 consecutive 5-minute segments from 89 hemodialysis treatments in 22 hemodialysis patients. While 891 (21.9%) segments showed saw-tooth pattern, 3,184 (78.1%) did not. In the test data set, the rate of correct SaO₂ pattern classification was 96% with an area under the receiver operating curve of 0.995 (95% CI: 0.993 to 0.998).

Conclusion: Our 1D-CNN algorithm accurately classifies SaO₂ saw-tooth pattern. The SaO₂ pattern classification can be performed in real time during an ongoing hemodialysis treatment, provide timely alert in the event of respiratory instability or sleep apnea, and trigger further diagnostic and therapeutic interventions.