The mathematics & principals of complex systems theory are at the heart of Therapeutic Monitoring Systems’ clinical decision support software technology. Similar to the complexity seen in global weather patterns and financial markets, human biology is a multitude of discreet, yet tightly interconnected genomic and physiological variables that together manifest in systemic health or disease. And similar to our inability to always accurately determine future weather patterns or the price of financial securities even with access to vast amount of current weather and financial market data, the ability of physicians, nurses and other caregivers to diagnose & predict a patient’s disease trajectory and risk of rapid deterioration is fraught with uncertainty. This is especially most apparent in critically ill patients, where despite round-the-clock care and continuous physiological vital sign monitoring, material & unexpected changes in health status can manifest over the course of hours or even just minutes following injury, operation or infection.
Complex systems theory teaches us analysis of the individual components of a complex system do not allow complete understanding of the system as a whole and that even infinite knowledge of the present is still not sufficient to predict the future properties of the system and hence, eliminate systemic uncertainly. For example, knowledge of today’s temperature, barometric pressure, wind patterns and any other number of innumerable weather metrics does not provide enough information to accurately predict future weather. The same is seen with patients in a hospital ICU; despite continuous, round-the-clock monitoring of vital signs and other clinical indicators, it is still not sufficient to predict patient prognosis with certainty.
Recognizing that human physiology is a complex system means that the application of non-linear dynamical theory can be used to decipher both inherent and emergent complexity that manifests as changes in systemic properties. Rather than gathering data at discreet intervals from individual components of the system, complexity science applies mathematical tools to analyze the continuous, emergent time series of data inherent to the complex system. In patients the time series of emergent data are physiological waveform vital signs.
The mathematical tool most widely used to describe the changes in complexity to these time series is variability analysis. Over 20 years of peer-reviewed studies from clinicians worldwide has clearly showed the strong correlation between changes in vital sign variability and improvement or deterioration in health.