Home renal monitoring for cancer patients: technical assessment and patient acceptability
Session type: Poster / e-Poster / Silent Theatre session
Recruitment to cancer clinical trials is challenging and usually limited to patients with preserved kidney function. Restrictions that allow those with an eGFR >50ml/min is arbitrary and not a risk-based approach driven by current clinical science. Due to increased survival rates for both conditions, there is now a significant population who have both cancer and chronic kidney disease. The aim of this research was to assess whether new technological advances in point of care (POC) creatinine meters and digital science could be used to give these people access to oncology clinical trials through personalised risk-based monitoring. We created an approach that explored the potential and acceptability of using a POC device, data capture via a smartphone, and risk-categorisation through an Acute Kidney Injury (AKI) algorithm, to enable decision-making and the first step in addressing this unmet clinical need.
Three POC devices were evaluated for usability, size and complexity. A smart phone app was developed, which captures device data and sends securely to a Cloud environment. Creatinine testing, calibration and patient acceptability in the hospital was conducted over a 2 week period with 17 interactions (patient/carer/nurse) and including two patient focus groups.
The Nova Biomedical Creatinine StatSensor® was the preferred device chosen to enable home creatinine readings with good user feedback and stable performance characteristics. The smartphone app user interface design was acceptable with patients based on patient acceptability testing- producing near-real time creatinine readings which could be reviewed by the medical team.
This proof of concept demonstrated that creatinine can be measured by a POC device, the data captured by an app and reported in near-real time. Further work is now being conducted to develop a clinical rules engine based on the NHS/NICE published algorithm for AKI; and apply this in clinical trials to alert investigators to impending AKI.