Skip to main content
SpringerLink
Log in

Data Collection and Analysis in the ICU

  • Chapter
  • First Online:
Neurocritical Care Informatics

Abstract

Data in the intensive care unit (ICU) can be broadly categorized as “phenotypic” and “physiologic.” Examples of “phenotypic” data include demographics, age, sex, laboratory values, and physician and nursing notes. Examples of “physiologic” data include common vital signs (blood pressure, heart rate, respiratory rate, core temperature) and other parameters generated from bedside monitoring devices (intracranial pressure, electroencephalogram). Most ICUs offer continuous 24/7 monitoring of these physiologic data (both numeric and waveforms) but lack the capability for data collection, integration, and analysis. The ability to do all of this in real time is virtually impossible. Analytical tools are also typically limited to averages and trends, while recent studies have demonstrated that complex systems analysis may provide greater insight into the dynamics of critical illness. This chapter will review various types of data in the ICU and methods of analysis including complex systems analysis and “patient state” tracking in the ICU.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Drewry AM, Fuller BM, Bailey TC, Hotchkiss RS. Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study. Crit Care. 2013;17(5):R200. https://doi.org/10.1186/cc12894.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Karmali SN, Sciusco A, May SM, Ackland GL. Heart rate variability in critical care medicine: a systematic review. Intensive Care Med Exp. 2017;5(1):33. https://doi.org/10.1186/s40635-017-0146-1.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Seely AJ, Macklem PT. Complex systems and the technology of variability analysis. Crit Care. 2004;8(6):R367–84. https://doi.org/10.1186/cc2948.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Schmidt JM, Sow D, Crimmins M, Albers D, Agarwal S, Claassen J, Connolly ES, Elkind MS, Hripcsak G, Mayer SA. Heart rate variability for preclinical detection of secondary complications after subarachnoid hemorrhage. Neurocrit Care. 2014;20(3):382–9. https://doi.org/10.1007/s12028-014-9966-y.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sykora M, Czosnyka M, Liu X, Donnelly J, Nasr N, Diedler J, Okoroafor F, Hutchinson P, Menon D, Smielewski P. Autonomic impairment in severe traumatic brain injury: a multimodal neuromonitoring study. Crit Care Med. 2016;44(6):1173–81. https://doi.org/10.1097/CCM.0000000000001624.

    Article  PubMed  Google Scholar 

  6. Hajar R. The pulse in ancient medicine. Part 1. Heart Views. 2018;19(1):36–43. https://doi.org/10.4103/HEARTVIEWS.HEARTVIEWS_23_18.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lewis O. Stephen Hales and the measurement of blood pressure. J Hum Hypertens. 1994;8(12):865–71.

    CAS  PubMed  Google Scholar 

  8. Segall HN. How Korotkoff, the surgeon, discovered the auscultatory method of measuring arterial pressure. Ann Intern Med. 1975;83(4):561–2.

    Article  CAS  Google Scholar 

  9. Gauer OH. Kreislauf des blutes. In: Rosemann U, editor. Lehrbuch der Physiologie des Menschen, vol. 1. 28th ed. Munchen: Urban und Schwarzenberg; 1960.

    Google Scholar 

  10. Scheer BV, Perel A, Pfeiffer UJ. Clinical review: complications and risk factors of peripheral arterial catheters used for haemodynamic monitoring in anaesthesia and intensive care medicine. Crit Care. 2002;6(3):199.

    Article  Google Scholar 

  11. Bur A, Herkner H, Vlcek M, Woisetschlager C, Derhaschnig U, Delle Karth G, Laggner AN, Hirschl MM. Factors influencing the accuracy of oscillometric blood pressure measurement in critically ill patients. Crit Care Med. 2003;31(3):793–9. https://doi.org/10.1097/01.CCM.0000053650.12025.1A.

    Article  PubMed  Google Scholar 

  12. Lehman LW, Saeed M, Talmor D, Mark R, Malhotra A. Methods of blood pressure measurement in the ICU. Crit Care Med. 2013;41(1):34–40. https://doi.org/10.1097/CCM.0b013e318265ea46.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kumar A, Anel R, Bunnell E, Habet K, Zanotti S, Marshall S, Neumann A, Ali A, Cheang M, Kavinsky C, Parrillo JE. Pulmonary artery occlusion pressure and central venous pressure fail to predict ventricular filling volume, cardiac performance, or the response to volume infusion in normal subjects. Crit Care Med. 2004;32(3):691–9.

    Article  Google Scholar 

  14. Marik PE, Baram M, Vahid B. Does central venous pressure predict fluid responsiveness? A systematic review of the literature and the tale of seven mares. Chest. 2008;134(1):172–8. https://doi.org/10.1378/chest.07-2331.

    Article  PubMed  Google Scholar 

  15. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996;93(5):1043–1065.

    Google Scholar 

  16. Brennan M, Palaniswami M, Kamen P. Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans Biomed Eng. 2001;48(11):1342–7. https://doi.org/10.1109/10.959330.

    Article  CAS  PubMed  Google Scholar 

  17. Brennan M, Palaniswami M, Kamen P. Poincare plot interpretation using a physiological model of HRV based on a network of oscillators. Am J Physiol Heart Circ Physiol. 2002;283(5):H1873–86. https://doi.org/10.1152/ajpheart.00405.2000.

    Article  CAS  PubMed  Google Scholar 

  18. Tulppo MP, Makikallio TH, Takala TE, Seppanen T, Huikuri HV. Quantitative beat-to-beat analysis of heart rate dynamics during exercise. Am J Phys. 1996;271(1 Pt 2):H244–52. https://doi.org/10.1152/ajpheart.1996.271.1.H244.

    Article  CAS  Google Scholar 

  19. Press WH, Rybicki G. Fast algorithm for spectral analysis of unevenly sampled data. Astrophys J. 1989;338:277–80.

    Article  Google Scholar 

  20. Bauer A, Malik M, Schmidt G, Barthel P, Bonnemeier H, Cygankiewicz I, Guzik P, Lombardi F, Muller A, Oto A, Schneider R, Watanabe M, Wichterle D, Zareba W. Heart rate turbulence: standards of measurement, physiological interpretation, and clinical use: International Society for Holter and Noninvasive Electrophysiology Consensus. J Am Coll Cardiol. 2008;52(17):1353–65. https://doi.org/10.1016/j.jacc.2008.07.041.

    Article  PubMed  Google Scholar 

  21. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82–7. https://doi.org/10.1063/1.166141.

    Article  CAS  PubMed  Google Scholar 

  22. Penzel T, Kantelhardt JW, Grote L, Peter JH, Bunde A. Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Trans Biomed Eng. 2003;50(10):1143–51. https://doi.org/10.1109/TBME.2003.817636.

    Article  PubMed  Google Scholar 

  23. Schmidt G, Malik M, Barthel P, Schneider R, Ulm K, Rolnitzky L, Camm AJ, Bigger JT Jr, Schomig A. Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet. 1999;353(9162):1390–6. https://doi.org/10.1016/S0140-6736(98)08428-1.

    Article  CAS  PubMed  Google Scholar 

  24. Watanabe MA, Schmidt G. Heart rate turbulence: a 5-year review. Heart Rhythm. 2004;1(6):732–8. https://doi.org/10.1016/j.hrthm.2004.09.003.

    Article  PubMed  Google Scholar 

  25. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215–20.

    Article  CAS  Google Scholar 

  26. Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E. 2005;71(2 Pt 1):021906.

    Article  Google Scholar 

  27. Kantelhardt JW, a Zschiegner S, Koscielny-bunde E, Bunde A, Havlin S, Stanley HE. Multifractal detrended fluctuation analysis of nonstationary time series. Phys A Stat Mech Appl. 2002;316(1–4):87–114.

    Article  Google Scholar 

  28. Vandendriessche B, Peperstraete H, Rogge E, Cauwels P, Hoste E, Stiedl O, Brouckaert P, Cauwels A. A multiscale entropy-based tool for scoring severity of systemic inflammation. Crit Care Med. 2014;42(8):e560–9.

    Article  CAS  Google Scholar 

  29. Vandendriessche B, Abas M, Dick TE, Loparo KA, Jacono FJ. A framework for patient state tracking by classifying multiscalar physiologic waveform features. IEEES Trans Biomed Eng. 2017;64(12):2890–2900.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Neurology and Neurocritical Care, San Francisco, CA, USA

    Jaspreet Kaur Mann

  2. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA

    Farhad Kaffashi

  3. Byteflies, Antwerp, Belgium

    Benjamin Vandendriessche

  4. Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Frank J. Jacono

  5. Division of Pulmonary, Louis Stoke VA Medical Center, Cleveland, OH, USA

    Frank J. Jacono

  6. ISSACS: Institute for Smart, Secure and Connected Systems, Cleveland, OH, USA

    Kenneth Loparo

  7. Department of Electrical Engineering and Computer Science, IoT Collaborative, Case Western Reserve University, Cleveland, OH, USA

    Kenneth Loparo

Authors
  1. Jaspreet Kaur Mann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farhad Kaffashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benjamin Vandendriessche
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frank J. Jacono
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kenneth Loparo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kenneth Loparo .

Editor information

Editors and Affiliations

  1. University Hospitals Cleveland Medical Center, Cleveland, OH, USA

    Michael De Georgia

  2. Case Western Reserve University, Cleveland, OH, USA

    Kenneth Loparo

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer-Verlag GmbH Germany

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mann, J.K., Kaffashi, F., Vandendriessche, B., Jacono, F.J., Loparo, K. (2020). Data Collection and Analysis in the ICU. In: De Georgia, M., Loparo, K. (eds) Neurocritical Care Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59307-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-59307-3_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-59305-9

  • Online ISBN: 978-3-662-59307-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation