Diality is a medical device company that aims to improve lives impacted by kidney disease through the development of the next generation of hemodialysis machines for at-clinic or at-home usage. A hemodialysis machine continuously extracts blood from a patient, runs it through a dialyzer--which acts as an artificial kidney to filter out waste and excess fluids--and reintroduces the filtered blood into the patient's system. This treatment process occurs three times per week for three to four hours per session depending on the patient's support needs. A common issue with treatment currently is knowing the correct Blood Volume Removal Rate (BVRR) for each patient. If the BVRR is too low, the treatment becomes inefficient and takes longer than required, but if it is too high, the patient's blood pressure could drop significantly putting the patient at risk of complications such as dizziness or loss of consciousness. Poor reactions such as these would require the treatment to be stopped immediately, necessitating another full day of treatment to make up for the lost time. Currently, there is no device on Diality’s machines that gauges the patient’s ratio of red blood cells to total volume, or Hematocrit (HCT), in real time. By having a direct input of the patient’s hematocrit value, caretakers or clinical professionals would be able to monitor the BVRR and adjust the treatment as necessary to improve the patient's treatment experience and overall outcomes.
This project directly benefits physicians, clinics, and most importantly patients by streamlining their treatment and mitigating the test-and-adjust period most patients experience when they first receive dialysis treatment. This sensor will help ensure minimal disruption to the patients' treatment and improve their quality of life
To solve this issue, we developed a real-time HCT sensor to be directly integrated into Diality's newest generation of hemodialysis machines. Our project consisted of two overarching needs: develop a real-time sensor and easily integrate it into Diality's machine design.
Our approach utilized an optics system with two sets of LEDs and photodiodes mounted in a clip that would envelop the tubing. The clip compresses the tubing so the walls become perpendicular to the light to avoid the additional cost of adding a new component to the disposable blood tubing. Through pressure and feasibility tests, a specific cross section and opening distance was chosen, so minimal pressure drops would be introduced and the tubing could be easily added and removed for reliability of results and ease of installation and removal for physicians and technicians that will be administering the treatment.
Wavelengths of 810nm and 1300nm were chosen for the LEDs because the shape of red blood cells deflect 810nm light and allow 1300nm to pass through, while plasma/water will deflect 1300nm and allow 810nm to pass through. With these light intensity values, we could calculate the concentration of the two blood components through Beer's Law and create a dimensionless ratio of the two values, which is proportional to the HCT. Because this design is optics-based and it expected to operate in ambient light, we implemented a transimpedance amplifier to increase the gain of our signal and show significant value differences for the smaller light intensity changes from our system.
To test our methodology and design, we ran bovine (cow) blood through the tubing and sensor and created a HCT function from the empirical data. The lab we purchased the blood from provided the HCT value to compare our ratio to. To get additional data points to add to our function, we used a saline solution to act as plasma and dilute the known hematocrit by 1% at a time. This process can be seen in the "Project Video" section below.
Over the course of two quarters, the team designed, manufactured, and assembled the full prototype and successfully integrated it into the faceplate of Diality's pump fixture. We conducted pressure tests to measure the pressure changes being introduced into the system by our design, force tests to measure the required force to place the tubing into the clip, signal testing with blood to determine the best gain for our system, and data collection to create our hematocrit function. These results gave us the proper data to create a sensor that minimally affects their current machine design, remains easy to use and clean for those that administer the treatment, and provides significant value changes for an accurate measurement of hematocrit.
By the end of our project, we were able to produce a HCT sensor capable of reading hematocrit in real-time within 3% error. The final deliverables include the HCT sensor clip, the sensor shell used to integrate the sensor with the pump faceplate, the HCT sensor software that records the values and calculates the ratio, and the HCT function for 19-29%.