Variability is a tool that reflects the systemic properties of an underlying complex system

Health is characterized by robust, complex variability, and illness by a reduction in the degree and complexity of variability. Monitoring physiologic variability (e.g. heart rate variability  and respiration rate variability) can uncover otherwise hidden information within the degree and patterns of variation that inform regarding the status of a patient’s health.

Remarkably, degree and complexity of physiologic variability has a lot to say about underlying health. Decades of research demonstrate that loss of healthy physiologic variability is associated with age & illness, correlates with illness severity, and is associated with decreased physiologic reserve. For example, a reduction in heart rate variability (HRV), defined as inter-beat changes in heart rate, has been associated with sepsis^18-20 and septic shock.^21,22 Moreover we have demonstrated that HRV monitoring provides early detection of infection in neutropenic patients, and determines severity of illness in critically ill patients.^23,24 HRV analysis has also shown prognostic capabilities in adult septic patients as an early marker of multiple organ dysfunction syndrome²⁵ and as an early predictor of death in emergency departments.²⁶ Respiratory rate variability (RRV), defined as the breath-to-breath changes in the respiratory rate, is also altered in association with critical illness, with a reduction in RRV during organ failure ²⁷and restrictive lung disease.²⁸ Reduced RRV has also been evaluated as a marker of increased stress during weaning from mechanical ventilation.^29-31 Moreover, others and we have demonstrated that reduced RRV is statistically associated with and predictive of extubation failure.^32,33

Variability analysis documents degree and character of fluctuations of physiologic parameters, namely rhythms over intervals-in-time, in contrast to traditional vital sign point-in-time assessment. Variability analysis characterizes the patterns of fluctuations with the time-series of a biologic signal (e.g. time series of inter-beat or inter-breath time intervals). The degree and character of this fluctuation can be measured mathematically in several complementary domains of variability. Over 100 techniques of variability analysis have been utilized in the medical literature. While many are correlated, each technique provides a unique perspective. No single technique offers a definitive characterization, and investigators agree a plurality of techniques offers the most complete evaluation.^34-36 Panels of variability analysis techniques applied to heart rate, respiratory rate, blood pressure, neutrophil count, temperature and more have demonstrated the following: (1) patterns of variability provide additional clinically useful information to the absolute value of that parameter, (2) altered variation is present in association with age and illness, and (3) degree of alteration correlates with severity of illness.³⁷

A variety of techniques exist to quantify and characterize variation over intervals-in-time, classified as Statistical, Geometric, Energetic, Informational and Invariant. Briefly, the statistical domain characterizes the statistical properties of the data distribution; the geometric domain describes those properties that are related to the shape of the dataset in specific mathematical spaces; the energetic domain includes those measures related to the energy or power of a signal; the informational domain describes the degree of irregularity/complexity inherent to the order of a signal; the invariant domain describes the properties of a system that demonstrates fractality or patterns that demonstrate multi-scale self-similarity.³⁸ Each technique provides a unique perspective; no single technique offers a definitive characterization of biologic signals, and thus a plurality of techniques offers the most complete evaluation.^34-36

Relevant Papers:

1. Bravi A, Longtin A, Seely AJE.  Review and classification of variability analysis techniques with clinical applications.  Biomed Eng Online. 2011 Oct 10;10:90. Review.
2. Seely AJE.  Complexity at the bedside.   J Crit Care. 2011 Jun;26(3):323-4.
3. Seely AJE, Macklem PT. Complex Systems and the Technology of Variability Analysis, Critical Care 2004 8(6):R367-84.
4. Seely AJE, Christou NV. Multiple Organ Dysfunction Syndrome: Exploring the paradigm of complex non-linear systems. Critical Care Medicine 2000; 28:2193.