Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure.

BACKGROUND: Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals. METHOD: We conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient's relative blood volume (RBV) decreases at a rate of at least - 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records. RESULTS: Treatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data. CONCLUSIONS: The findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.

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

BACKGROUND: Maintenance hemodialysis (MHD) patients are particularly vulnerable to coronavirus disease 2019 (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-diagnosis 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 1×/min during dialysis using the Crit-Line monitor (Fresenius Medical Care, Waltham, MA, USA). 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; mean ± standard deviation age 66.5 ± 15.7 years) contributing 338 HD treatments. Mean time between onset of symptoms indicative of COVID-19 and diagnosis was 1.1 days (median 0; range 0-9). Prior to COVID-19 diagnosis the rate of HD treatments with hypoxemia, defined as treatment-level average SaO2 <90%, increased from 2.8% (2-4 weeks pre-diagnosis) to 12.2% (1 week) and 20.7% (3 days pre-diagnosis). Intradialytic O2 supplementation increased sharply post-diagnosis. Eleven patients died from COVID-19 within 5 weeks. Compared with patients who recovered from COVID-19, demised patients showed a more pronounced decline in SaO2 prior to COVID-19 diagnosis. CONCLUSIONS: In HD 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.