Dialysis Access Monitoring Using a Digital Stethoscope-Based Deep Learning System

Study Overview
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Brief Summary
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This study will enroll end-stage renal disease (ESRD) patients on hemodialysis with a maturing arteriovenous fistula (AVF) for hemodialysis access. A study staff member will mark with indelible ink on each participant's skin the three sites on the upper extremity where the Eko CORE digital stethoscope will be used to take sound recordings. 9 recordings will be taken (3 at each site) once per week during weekly dialysis treatments.

Detailed Description
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Hemodialysis (HD) is conducted through a hemodialysis vascular access, such that the patient's blood can be removed from the patient, circulated through the dialysis machine, and then returned to the patient. HD vascular accesses may take the form of an arteriovenous fistula (AVF), an arteriovenous graft (AVG), or a temporary or permanent dialysis catheter. Because of its low complication rate and good longevity (durability), the AVF is the preferred access; current Kidney Disease Outcomes Quality Improvement Fistula First recommendations are that any patient with an estimated glomerular filtration rate of 25 mL/min should undergo AVF creation surgery in preparation for eventual HD. Stage IV is chosen as the optimal time for AVF creation, even though the patient does not yet need HD. This is because (1) it usually takes  8 weeks to schedule a patient for AVF creation surgery, (2) it takes another 12-16 weeks after creation for the AVF to mature and become ready for use, and (3) as many as 60% of surgically created AVFs fail to mature at all. Considering the time lag to maturation and the possible need to intervene on an access that is failing to mature, a significant lead time is required to avoid encountering a patient in stage V CKD who needs HD immediately and then has to resort to a tunneled dialysis catheter with all its attendant risks of infection, thrombosis and central vein stenosis. Being able to actively monitor an AVF from the time of surgical creation with a device that can predict whether that AVF will fail vs. mature, will empower patients and their physicians to decide much earlier to intervene and put that AVF back on track to maturation. This means that many more patients will be able to start HD when needed, using their AVF as a dialysis access, and therefore reduce the use of tunneled dialysis catheters. This type of monitoring, however, requires frequent measurements in order to track progression toward maturity. By training an AI/ML algorithm to analyze sound signals recorded from the patient's nascent AVF, the algorithm can detect when an AVF is likely or not to mature. If trained properly, it should be able to detect an unlikely to mature condition very early after surgery, perhaps even within the first two weeks. This will prompt the patient's physician to consider intervention on the AVF that would increase the likelihood of maturation and increase the overall maturation rate. A digital, AI/ML-enabled stethoscope, perhaps even used in the patient's home in the future, could easily facilitate that monitoring requirement as well as transmit the relevant information to the patient's physician so that timely intervention could be arranged. The Eko CORE is an FDA-cleared and CE-marked electronic stethoscope. The CORE allows the audio recording of lung, heart, and other body sounds. The CORE features 40x audio amplification, ambient noise reduction, a 4000Hz sample rate, and 3 audio filters. The de-identified auscultatory CORE sound recordings transmit wirelessly via Bluetooth to the secure, HIPAA-compliant Eko application on a smartphone or tablet, which allows the user to playback sound recordings, annotate notes on recorded audio, and save recordings. This data is synced in real-time to a secure, HIPAA-compliant, cloud-based Amazon Web Services (AWS) database server managed by Eko Devices. This prospective, observational, longitudinal, proof of concept study will be enrolling patients at the University of North Carolina (UNC) Kidney Center. This study will enroll a minimum of 25, maximum of 50, patients with end-stage renal disease (ESRD) patients in the hemodialysis unit (HDU) with maturing AVFs who are being dialysed through a tunneled dialysis catheter (TDC). Recordings will be taken once per week during the patient's HD appointment. Duration of the study will extend from the time of enrollment until one of the following occurs first: (a) abandonment of the AVF, or (b) 2 weeks of measurement after documentation of successful cannulation defined by 2-needle cannulation for 3 successive dialysis treatments, or (c) 6 months of study participation. These datasets will be used to derive an AI/ML model to predict the likelihood of a new AVF to mature or fail.

Official Title
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Dialysis Access Monitoring Using a Digital Stethoscope-Based Deep Learning System

Conditions
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Dialysis Access Malfunction, Dialysis; Complications

Intervention / Treatment
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* Device: Eko CORE Digital Stethoscope


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Adults aged 18 years or older Potential subject is able and willing to give informed consent End-stage renal disease, stage V, on active HD treatment Participant has a maturing AVF that has not yet been cannulated Participant is willing to have repeat Eko recordings taken weekly from the time of enrollment until the access is declared mature and suitable for HD access or failed and unsuitable for HD access Exclusion Criteria: Younger than 18 years Pregnant Incarcerated Unable and/or unwilling to give informed consent Presence of an active infection or open wounds along potential access and data collection site Unable to complete study activities according to the above schedule

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Adults with AVFs currently in the process of maturing<br>Adults with ESRD who currently have a maturing AVF that has not yet been cannulated | Device: Eko CORE Digital Stethoscope<br>* The digital stethoscope will be used to take 9 recordings around each subject's maturing AVF once per week until study participation is over<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Development of deep learning-scale database of maturing AVF sounds. | The maturing AVFs sounds recorded during this study will be used to create a database used for AI/ML model development. | 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Correlation between AI/ML model output and the AVF maturation status | A secondary aim of this study is to demonstrate whether Eko data scientists can create an artificial intelligence-machine learning (AI/ML) model that can detect changes over time in maturing AVFs that may predict the likelihood of AVF maturation or failure | 6 months | 
| Time frame of how soon after surgery the developed algorithm can accurately predict the likelihood of AVF failure-to-mature | An additional aim of this study is to determine how early after surgery the model can produce clinically acceptable notification of the failure-to-mature state | 6 months |