Shock index (SI) is the measure of heart rate divided by systolic blood pressure, and is useful in predicting the severity of hypovolemic shock and hemodynamic condition. For many critical care patients, heart rate and blood pressure can appear “normal” while the SI score is abnormal.
Despite being a valuable clinical tool, caregivers are forced to manually compute shock index using physiologic data from the monitors which creates a time and energy barrier. Researchers at the University of Michigan have developed a tool that allows SI to be calculated using real-time streaming data to provide a measure that can be consulted during the clinical decision making process.
The Signal Quality Index (Lite) technology assesses each incoming value of heart rate and systolic blood pressure and determines if it’s optimal, “clean” data for computing SI. This mechanism improves the confidence in the utilization of SI, while avoiding anomalous values.
As opposed to taking traditional vital signs, SI is a far better estimate of perfusion status. However, in critical care scenarios where there are several patients demanding attention, taking the time to manually compute SI is a significant burden on clinicians. And while clinicians have a constant stream of data from physiological monitors, there is not always a way to tell if that data is a divergent from the norm. Not only does SQI-Lite automatically compute SI in real-time, it also ensures that only quality data is being processed by the algorithm.
In addition to ensuring a robustness in shock index computation, SQI-Lite also provides a severity assessment. Based on values of computed SI, clinicians are able to categorize patients into three different severity levels: normal, moderate, and severe. The patients are continuously ranked in comparison to other patients being monitored in the same way, allowing caregivers to prioritize their most critical patients.