Complexity Sciences Dramatically Improve Biomarker Research and Use

  • James Caldwell Palmer


This presentation argues that Complexity Sciences already dramatically improve medical biomarkers as part of a sea-change regarding: actionable clinical biomarker uses, search strategies, and future research. This medical sector-wide impact is unevenly known and appreciated. Complexity Sciences are contributing significantly to understanding biomarkers across the dimensions of the human phenotype because Complexity Sciences advantageously explain the changes in humans which characterize the continuous dynamic variability of their existence.

Complexity sciences contribute significantly to biomarkers search and applications because humans in the context of existence and evolution are continuously dynamically variable—and it happens that complexity sciences and related maths successfully provide advanced concepts and enhanced measures of the emergent, self-organizing, nonlinear, always changing human interactions with self, others, and environments—built and natural. Scientific methods of research and application call for measurement and methods to be in accordance with the subject or our study. The Complexity Sciences multiple fields of research are theories which, like all theories, are about change. Complexity Sciences methods and measures are a now decades old set of validated concepts, methods, and measures highly suitable to explain complex signals bioinformatics, as studied here—and to contribute to other medical research and clinical topics across the breadth of healthcare.

Examples to illustrate clinical practice and clinical studies of nonlinear dynamic biomarkers will describe Vital Sign Variability—HRVD (Heart Rate Variability Dynamics), Respiratory Rate, Temperature Curve Complexity, and Blood Pressure Variability. Other examples from the broader literature on dynamic biomarkers will be cited.

The contributions of complexity sciences and nonlinear analytics are part of a broader Nonlinear Dynamic Turn across multiple aspects of human endeavor—medicine, biology, psychology, physiology, and economics, inter alia. A brief section will mention how this Turn moves away from “average” or summary statistics, which can be inadequate for ontological issues (how change characterizes humans) and epistemological (how do we measure change in humans).

The last section Extensions and Futures points to the “other half of the sky” owned by psychological aspects of humans and the need to extend variability analysis and integrate psychological, as a broad term, interaction dynamics. Other extensions and futures involve: moving biomarkers into practice, developing variability indices, and expanding collaborative applied research.



The learning by my colleague, David Introcaso, is much appreciated from our conversations with multiple clinicians and researchers over the last few years. Thanks for conversations about HRV and infection/sepsis to Drs. Ryan Arnold, Barnaby Douglas, and Andrew Seely.


  1. 1.
    Panta rhei. Lucretius. Heraclitus, quote on change. In: Hicks RD, editor. Diogenes Laërtius’s lives of eminent philosophers. Boston: Harvard University Press; 1925.Google Scholar
  2. 2.
    Turing AM. The chemical basis of morphogenesis. Philos Trans R Soc. 1952;B237:37–52.Google Scholar
  3. 3.
    Rothwell PM. Limitations of the usual blood-pressure hypothesis and importance of variability, instability, and episodic hypertension. Lancet. 2010;375(9718):938–48.CrossRefGoogle Scholar
  4. 4.
    Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlöf B, et al.; ASCOT-BPLA and MRC Trial Investigators. Effects of β blockers and calcium-channel blockers on within-individual variability in blood pressure and risk. Lancet Neurol. 2010;9(5):469–80.CrossRefGoogle Scholar
  5. 5.
    Rothwell PM. Does blood pressure variability modulate cardiovascular risk? Curr Hypertens Rep. 2011;13(3):177–86.CrossRefGoogle Scholar
  6. 6.
    Varela M, Churruca J, Gonzalez A, Martin A, Ode J, Galdos P. Temperature curve complexity predicts survival in critically ill patients. Am J Respir Crit Care Med. 2006;174(3):290–8.CrossRefGoogle Scholar
  7. 7.
    Snell N, Newbold P. The clinical utility of biomarkers in asthma and COPD. Curr Opin Pharmacol. 2008;8(3):222–35.CrossRefGoogle Scholar
  8. 8.
    Stacey R. Complex responsive processes in organizations: learning and knowledge creation. London: Routledge; 2001.Google Scholar
  9. 9.
    Kelso JAS, Engstrøm D. The complementary nature. Cambridge: MIT Press; 2006.Google Scholar
  10. 10.
    Prigogine I, Stengers I. Order out of chaos. Boulder: New Science Library; 1984.Google Scholar
  11. 11.
    Lorentz EN. Deterministic nonperiodic flow. J Atmos Sci. 1963;20(2):130–41.CrossRefGoogle Scholar
  12. 12.
    Haken, H. The science of structure: synergetics. New York: Van Nostrand Reinhold; 1984.Google Scholar
  13. 13.
    Bak P. How nature works: the science of self-organized criticality. New York: Copernicus; 1996.CrossRefGoogle Scholar
  14. 14.
    Mandelbrot BB. Fractal geometry: what is it and what does it do? Proc R Soc Lond A. 1989;423(1864):3–16.CrossRefGoogle Scholar
  15. 15.
    Turing A. The chemical basis of morphogenesis. Philos Trans. 1952;B.237:37–72.Google Scholar
  16. 16.
    Palmer JC, Introcaso D, O’Shea M. Heart Rate Variability: A Diagnostic and Prognostic Biomarkers for Identifying Infection-Associated Sepsis. Available from
  17. 17.
    Fairchild KD, O’Shea TM. Heart rate characteristics: physiomarkers for detection of late-onset neonatal sepsis. Clin Perinatol. 2010;37(3):581–98.CrossRefGoogle Scholar
  18. 18.
    O’Brien E. Stabilizing blood pressure variability: a new therapeutic target in hypertension. Medicographia. 2012;34:1.Google Scholar
  19. 19.
    O’Brien E, Fitzgerald D. The history of blood pressure measurement. J Hum Hypertens. 1994;8(2):73–84.PubMedGoogle Scholar
  20. 20.
    Pearce JMS. A brief history of the clinical thermometer. Q J Med. 2002;95(4):251–2.CrossRefGoogle Scholar
  21. 21.
    Varela M, Churruca J, Gonzalez A, Martin A, Ode J, Galdos P. Temperature curve complexity predicts survival in critically ill patients. Am J Respir Crit Care Med. 2006;174(3):290–8.CrossRefGoogle Scholar
  22. 22.
    Papaioannou ME, Chouvarda IG, Maglaveras NK, Pneumatikos IA. Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock. Critical Care. 2012;16(2):R51.CrossRefGoogle Scholar
  23. 23.
    Varela M, Calvo M, Chana M, Gomez-Mestre I, Asensio R, Galdos P. Clinical implications of temperature curve complexity in critically ill patients. Crit Care Med. 2005;33(12):2764–71.CrossRefGoogle Scholar
  24. 24.
    Pincus S. Approximate entropy (ApEn) as a complexity measure. Chaos. 1995;5(1):110–7.CrossRefGoogle Scholar
  25. 25.
    Papaioannou VE, Chouvarda IG, Maglaveras NK, Baltopoulos GI, Pneumatikos IA. Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients. Physiol Meas. 2013;34(11):1449–66.CrossRefGoogle Scholar
  26. 26.
    Jost K, Pramana I, Delgado-Eckert E, Kumar N, Datta AN, Frey U, et al. Dynamics and complexity of body temperature in preterm infants nursed in incubators. PLOS ONE 2017;12(4):e0176670.CrossRefGoogle Scholar
  27. 27.
    West G, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science. 1997;276(5309):122–6.CrossRefGoogle Scholar
  28. 28.
    Boser SR, Park H, Perry SF, Ménache MG, Green FH. Fractal geometry of airway remodeling in human asthma. Am J Respir Crit Care Med. 2005;172(7):817–23.CrossRefGoogle Scholar
  29. 29.
    Papaioannou VE, Chouvarda I, Maglaveras N, Dragoumanis C, Pneumatikos I. Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: a study of physiologic complexity in surgical critically ill patients. J Crit Care. 2011;26(3):262–72.CrossRefGoogle Scholar
  30. 30.
    Brewster JF, Graham MR, Mutch W. Convexity, Jensen’s inequality and benefits of noisy mechanical ventilation. J R Soc Interface. 2005;2(4):395–6.CrossRefGoogle Scholar
  31. 31.
    Venegas JG, Harris RS, Simon BA. A comprehensive equation for the pulmonary pressure-volume curve. J Appl Physiol. 1998;84(1):389–95.CrossRefGoogle Scholar
  32. 32.
    Denny M. The fallacy of the average: on the ubiquity, utility and continuing novelty of Jensen’s inequality. J Exp Bio. 2017; 220(Pt 2):139–46.CrossRefGoogle Scholar
  33. 33.
    Savage S. The flaw of averages: why we underestimate risk in the face of uncertainty. Hoboken: Wiley; 2009.Google Scholar
  34. 34.
    Gibson W. The future is already here – it’s just unevenly distributed. NPR Talk of the Nation, 30 November, 1999 show.
  35. 35.
    Aerts JM, Haddad WM, An G, Vodovotz Y. From data patterns to mechanistic models in acute critical illness. J Crit Care. 2014;29(4):604–10.CrossRefGoogle Scholar
  36. 36.
    Evans PA, Hawkins K, Williams PR. Rheometry for blood coagulation studies. Rheo Rev. 2006:255–91.Google Scholar
  37. 37.
    Brown MR, Curtis DJ, Rees P, Summers HD, Hawkins K, Evans PA, et al. Fractal discrimination of random fractal aggregates and its application in biomarker analysis for blood coagulation. Chaos Solitons Fractals. 2012;45(8):1025–32.CrossRefGoogle Scholar
  38. 38.
    Longo, Montévil M. Perspectives on organisms: biological time, symmetries and singularities. Heidelberg: Springer; 2014.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • James Caldwell Palmer
    • 1
  1. 1.DManDenverUSA

Personalised recommendations