The impact of COVID-19 on the NHS, especially on critical care units, has resulted in an unprecedented pressure on surgery waiting lists. Transitioning patients in and out of critical care units safely and efficiently is going to be key to clearing this backlog and getting patients the treatment they need. Patient monitoring systems which use Artificial Intelligence (AI) to flag early warning signs for patient deterioration in critical care settings could be a key tool to help clinicians to improve outcomes and reduce critical care length of stay for patients.
Unfortunately, available early warning score models currently used in most critical care settings are severely limited by their static nature and generic thresholds. These limitations mean that they can fail to identify subtle signs of patient deterioration until it is too late. In contrast, AI-driven technology has the potential to transform early warning systems, as it can anticipate patient deterioration in real-time and allow clinicians to take proactive preventive action.
One AI-driven patient monitoring system which has been successfully deployed in a critical care setting is the STABILITY platform, developed by UK start-up Rinicare. A product evaluation at Wythenshawe Hospital, part of Manchester University NHS Foundation Trust, is currently underway to assess the platform amongst cardiac surgery patients in its Cardiothoracic Critical Care Unit.
The first offering available on the STABILITY platform is STABILITY UO (urine output), a CE-marked software solution developed to predict the risk of developing renal complications or acute kidney injury (AKI) after cardiac surgery. Powered by a clinically validated algorithm, STABILITY UO’s predictive power has been shown to accurately predict dangerous episodes of low urine output that, without intervention, correlate strongly with increased morbidity and mortality.
Acute Kidney Injury (AKI) is a major healthcare burden, estimated to cost the NHS alone £1.02 billion per year, and is a particular concern for clinicians managing cardiac patients following surgery. AKI occurs in up to 12 per cent of patients in general undergoing surgery procedures and about a third of patients following cardiac surgery.
Using routinely-taken physiological measurements, STABILITY UO can predict the future probability of low urine output in time for clinicians to take preventive action to reduce the risk of developing AKI. This risk prediction is personalised for each patient, is available after only six hours of post-operative monitoring and is updated hourly.
A vital aspect of the STABILITY UO platform for clinical teams is that it can be integrated into the hospital’s local IT network, and then accessed seamlessly through bedside monitors, nursing PCs or tablets. This allows nurses to input the regular urine output readings without major disruption to their routine.
Data from the Wythenshawe Hospital product evaluation should be available later in 2021, demonstrating the impact that AI-driven patient monitoring can have on high-risk critical care patients. The Rinicare team is also developing new applications for the STABILITY platform. The next offering focuses on postoperative atrial fibrillation (AF) which affects up to one third of patients after cardiac surgery. Rates of postoperative AF have remained stubbornly high for decades, so this is another really exciting area for the application of AI-driven technology.
The potential benefits of clinical risk prediction technology for the NHS during this crucial recovery phase from the pandemic are far reaching, and could have an almost immediate effect on the number of surgeries a critical care unit has capacity for. The ongoing evaluations of solutions like STABILITY show that we are fast approaching the point where patients and clinicians can both start to truly appreciate the benefits of AI technology in the healthcare setting.
See CMFT Research & Innovation Press Release.