Arterial oxygen saturation and hypoxemia in hemodialysis patients with COVID-19

Abstract

Background Maintenance hemodialysis (MHD) patients are particularly vulnerable to COVID-19, a viral disease that may cause interstitial pneumonia, impaired alveolar gas exchange, and hypoxemia. We ascertained the time course of intradialytic arterial oxygen saturation (SaO2) in MHD patients between 4 weeks pre- and the week post-diagnosis of COVID-19. Methods We conducted a quality improvement project in confirmed COVID-19 in-center MHD patients from 11 dialysis facilities. In patients with an arterio-venous access SaO2 was measured 1x/minute during dialysis using the Crit-Line monitor (Fresenius Medical Care, Waltham, MA). We extracted demographic, clinical, treatment, and laboratory data and COVID-19 related symptoms from the patients’ electronic health records. Results Intradialytic SaO2 was available in 52 patients (29 males; age 66.5±15.7 years) contributing 338 hemodialysis treatments. Mean time between onset of symptoms indicative of COVID-19 and diagnosis was 1.1 days (median 0; range 0 to 9). Prior to COVID-19 diagnosis the rate of hemodialysis treatments with hypoxemia, defined as treatment-level average SaO2 < 90%, increased from 2.8% (2 to 4 weeks pre-diagnosis) to 12.2% (1 week) and 20.7% (3 days pre-diagnosis). Intradialytic oxygen supplementation increased sharply post-diagnosis. Eleven patients died from COVID-19 within 5 weeks. Compared to patients who recovered from COVID-19, demised patients showed a more pronounced decline in SaO2 prior to COVID-19 diagnosis. Conclusion In hemodialysis patients, hypoxemia may precede the onset of clinical symptoms and the diagnosis of COVID-19. A steep decline of SaO2 is associated with poor patient outcomes. Measurements of SaO2 may aid the pre-symptomatic identification of patients with COVID-19.

Identification of arterial oxygen intermittency in oximetry data

“…The main advantages of the proposed method compared to the ODD and ODI are: (1) the identification of oxygen desaturations is dependent on the definition of a baseline, which in most practical situations may be particularly difficult to determine, even more so for patients with comorbidities that can affect their Oxygen Saturation (as is the case of dialysis patients) 11 , 19 ; (2) the ODI definition varies between authors, and they are usually tailored for the specific application of identifying sleep apnea events, by looking at a very specific shape in the oxygen saturation time series 37 ; (3) since the definition of desaturations is so specific, deviations from most common scenarios can jeopardize its predictive ability, situations like dialysis patients or patients in high altitude already present reduced average of oxygen saturation, which can impact the ODI through the baseline calculation 19 , 49 ; (4) some of the ODI definitions specify the desaturation minimal duration (usually 10 s), making them more difficult to identify in time series with low sampling frequency (e.g., 0.1 Hz) 20 , 40 , 46 . The proposed method is more robust against these shortcomings for the ODI: it does not depend on any baseline definition; it is also insensitive to the averages of the Oxygen Saturation; its definition is more precise, and it is robust against the sampling frequency of the input time series, and the only free parameter ( ) is directly interpretable in terms of the timescale of the problem.…”

“…Recently, technologies have been introduced to continuously measure SaO 2 in the extracorporeal blood circuit during hemodialysis. Such technologies have shown to be valuable for the identification of intradialytic hypoxemia 8 – 10 and COVID-19 monitoring 11 , and other clinical outcomes 12 . When reviewing intradialytic SaO 2 time series, we noticed a peculiar intermittent oscillatory SaO 2 pattern.…”