Skip to main content
Top
Published in: Neurocritical Care 2/2014

01-12-2014 | Review Article

Multimodality Monitoring: Informatics, Integration Data Display and Analysis

Authors: J. Michael Schmidt, Michael De Georgia, The Participants in the International Multidisciplinary Consensus Conference on Multimodality Monitoring

Published in: Neurocritical Care | Special Issue 2/2014

Login to get access

Abstract

The goal of multimodality neuromonitoring is to provide continuous, real-time assessment of brain physiology to prevent, detect, and attenuate secondary brain injury. Clinical informatics deals with biomedical data, information, and knowledge including their acquisition, storage, retrieval, and optimal use for clinical decision-making. An electronic literature search was conducted for English language articles describing the use of informatics in the intensive care unit setting from January 1990 to August 2013. A total of 64 studies were included in this review. Clinical informatics infrastructure should be adopted that enables a wide range of linear and nonlinear analytical methods be applied to patient data. Specific time epochs of clinical interest should be reviewable. Analysis strategies of monitor alarms may help address alarm fatigue. Ergonomic data display that present results from analyses with clinical information in a sensible uncomplicated manner improve clinical decision-making. Collecting and archiving the highest resolution physiologic and phenotypic data in a comprehensive open format data warehouse is a crucial first step toward information management and two-way translational research for multimodality monitoring. The infrastructure required is largely the same as that needed for telemedicine intensive care applications, which under the right circumstances improves care quality while reducing cost.
Appendix
Available only for authorised users
Literature
1.
go back to reference Bellazzi R, Diomidous M, Sarkar IN, Takabayashi K, Ziegler A, McCray AT. Data analysis and data mining: current issues in biomedical informatics. Methods Inf Med. 2011;50:536–44.CrossRefPubMedCentralPubMed Bellazzi R, Diomidous M, Sarkar IN, Takabayashi K, Ziegler A, McCray AT. Data analysis and data mining: current issues in biomedical informatics. Methods Inf Med. 2011;50:536–44.CrossRefPubMedCentralPubMed
2.
go back to reference Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Annf Intern Med. 2009;151:W-65–94. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Annf Intern Med. 2009;151:W-65–94.
5.
go back to reference Chelico J, PhD A, Wajngurt D. Architectural design of a data warehouse to support operational and analytical queries across disparate clinical databases. 2007. p. 901. Chelico J, PhD A, Wajngurt D. Architectural design of a data warehouse to support operational and analytical queries across disparate clinical databases. 2007. p. 901.
6.
go back to reference Martich G, Waldmann C, Imhoff M. Clinical informatics in critical care. J Intensiv Care Med. 2004;19:154.CrossRef Martich G, Waldmann C, Imhoff M. Clinical informatics in critical care. J Intensiv Care Med. 2004;19:154.CrossRef
7.
go back to reference Chou D, Sengupta S. Infrastructure and security. Burlington: Academic Press; 2008. Chou D, Sengupta S. Infrastructure and security. Burlington: Academic Press; 2008.
8.
go back to reference Kull L, Emerson R. Continuous EEG monitoring in the intensive care unit: technical and staffing considerations. J Clin Neurophysiol. 2005;22:107–18.CrossRefPubMed Kull L, Emerson R. Continuous EEG monitoring in the intensive care unit: technical and staffing considerations. J Clin Neurophysiol. 2005;22:107–18.CrossRefPubMed
9.
go back to reference Signorini DF, Piper IR, Jones PA, Howells TP. Importance of textual data in multimodality monitoring. Crit Care Med. 1997;25:2048–50.CrossRefPubMed Signorini DF, Piper IR, Jones PA, Howells TP. Importance of textual data in multimodality monitoring. Crit Care Med. 1997;25:2048–50.CrossRefPubMed
10.
go back to reference Diringer MN. Treatment of fever in the neurologic intensive care unit with a catheter-based heat exchange system. Crit Care Med. 2004;32:559–64.CrossRefPubMed Diringer MN. Treatment of fever in the neurologic intensive care unit with a catheter-based heat exchange system. Crit Care Med. 2004;32:559–64.CrossRefPubMed
11.
go back to reference Diedler J, Sykora M, Rupp A, et al. Impaired cerebral vasomotor activity in spontaneous intracerebral hemorrhage. Stroke. 2009;40:815–9.CrossRefPubMed Diedler J, Sykora M, Rupp A, et al. Impaired cerebral vasomotor activity in spontaneous intracerebral hemorrhage. Stroke. 2009;40:815–9.CrossRefPubMed
12.
go back to reference Steiner LA, Czosnyka M, Piechnik SK, et al. Continuous monitoring of cerebrovascular pressure reactivity allows determination of optimal cerebral perfusion pressure in patients with traumatic brain injury. Crit Care Med. 2002;30:733–8.CrossRefPubMed Steiner LA, Czosnyka M, Piechnik SK, et al. Continuous monitoring of cerebrovascular pressure reactivity allows determination of optimal cerebral perfusion pressure in patients with traumatic brain injury. Crit Care Med. 2002;30:733–8.CrossRefPubMed
13.
go back to reference Goldberger AL. Applications of chaos to physiology and medicine. In: Kim JH, Stringer J, editors. Applied chaos. New York: Wiley; 1992. p. 321–31. Goldberger AL. Applications of chaos to physiology and medicine. In: Kim JH, Stringer J, editors. Applied chaos. New York: Wiley; 1992. p. 321–31.
14.
go back to reference Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol. 1987;59:256–62.CrossRefPubMed Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol. 1987;59:256–62.CrossRefPubMed
15.
go back to reference Szabo BM, van Veldhuisen DJ, van der Veer N, Brouwer J, De Graeff PA, Crijns HJ. Prognostic value of heart rate variability in chronic congestive heart failure secondary to idiopathic or ischemic dilated cardiomyopathy. Am J Cardiol. 1997;79:978–80.CrossRefPubMed Szabo BM, van Veldhuisen DJ, van der Veer N, Brouwer J, De Graeff PA, Crijns HJ. Prognostic value of heart rate variability in chronic congestive heart failure secondary to idiopathic or ischemic dilated cardiomyopathy. Am J Cardiol. 1997;79:978–80.CrossRefPubMed
16.
go back to reference Kirkness CJ, Burr RL, Mitchell PH. Intracranial pressure variability and long-term outcome following traumatic brain injury. Acta Neurochir Suppl. 2008;102:105–8.CrossRefPubMed Kirkness CJ, Burr RL, Mitchell PH. Intracranial pressure variability and long-term outcome following traumatic brain injury. Acta Neurochir Suppl. 2008;102:105–8.CrossRefPubMed
17.
go back to reference Triedman JK, Cohen RJ, Saul JP. Mild hypovolemic stress alters autonomic modulation of heart rate. Hypertension. 1993;21:236–47.CrossRefPubMed Triedman JK, Cohen RJ, Saul JP. Mild hypovolemic stress alters autonomic modulation of heart rate. Hypertension. 1993;21:236–47.CrossRefPubMed
18.
go back to reference Mussalo H, Vanninen E, Ikaheimo R, et al. Heart rate variability and its determinants in patients with severe or mild essential hypertension. Clin Physiol. 2001;21:594–604.CrossRefPubMed Mussalo H, Vanninen E, Ikaheimo R, et al. Heart rate variability and its determinants in patients with severe or mild essential hypertension. Clin Physiol. 2001;21:594–604.CrossRefPubMed
19.
go back to reference van Boven AJ, Jukema JW, Haaksma J, Zwinderman AH, Crijns HJ, Lie KI. Depressed heart rate variability is associated with events in patients with stable coronary artery disease and preserved left ventricular function. REGRESS Study Group. Am Heart J. 1998;135:571–6.CrossRefPubMed van Boven AJ, Jukema JW, Haaksma J, Zwinderman AH, Crijns HJ, Lie KI. Depressed heart rate variability is associated with events in patients with stable coronary artery disease and preserved left ventricular function. REGRESS Study Group. Am Heart J. 1998;135:571–6.CrossRefPubMed
20.
go back to reference Axelrod S, Lishner M, Oz O, Bernheim J, Ravid M. Spectral analysis of fluctuations in heart rate: an objective evaluation of autonomic nervous control in chronic renal failure. Nephron. 1987;45:202–6.CrossRefPubMed Axelrod S, Lishner M, Oz O, Bernheim J, Ravid M. Spectral analysis of fluctuations in heart rate: an objective evaluation of autonomic nervous control in chronic renal failure. Nephron. 1987;45:202–6.CrossRefPubMed
21.
go back to reference Toweill DL, Kovarik WD, Carr R, et al. Linear and nonlinear analysis of heart rate variability during propofol anesthesia for short-duration procedures in children. Pediatr Crit Care Med. 2003;4:308–14.CrossRefPubMed Toweill DL, Kovarik WD, Carr R, et al. Linear and nonlinear analysis of heart rate variability during propofol anesthesia for short-duration procedures in children. Pediatr Crit Care Med. 2003;4:308–14.CrossRefPubMed
22.
go back to reference Ryan SM, Goldberger AL, Pincus SM, Mietus J, Lipsitz LA. Gender- and age-related differences in heart rate dynamics: are women more complex than men? J Am Coll Cardiol. 1994;24:1700–7.CrossRefPubMed Ryan SM, Goldberger AL, Pincus SM, Mietus J, Lipsitz LA. Gender- and age-related differences in heart rate dynamics: are women more complex than men? J Am Coll Cardiol. 1994;24:1700–7.CrossRefPubMed
23.
go back to reference Vikman S, Makikallio TH, Yli-Mayry S, et al. Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. Circulation. 1999;100:2079–84.CrossRefPubMed Vikman S, Makikallio TH, Yli-Mayry S, et al. Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. Circulation. 1999;100:2079–84.CrossRefPubMed
24.
go back to reference Hornero R, Aboy M, Abasolo D, McNames J, Goldstein B. Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension. IEEE Trans Biomed Eng. 2005;52:1671–80.CrossRefPubMed Hornero R, Aboy M, Abasolo D, McNames J, Goldstein B. Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension. IEEE Trans Biomed Eng. 2005;52:1671–80.CrossRefPubMed
25.
go back to reference Papaioannou VE, Maglaveras N, Houvarda I, Antoniadou E, Vretzakis G. Investigation of altered heart rate variability, nonlinear properties of heart rate signals, and organ dysfunction longitudinally over time in intensive care unit patients. J Crit Care. 2006;21:95–103 discussion-4.CrossRefPubMed Papaioannou VE, Maglaveras N, Houvarda I, Antoniadou E, Vretzakis G. Investigation of altered heart rate variability, nonlinear properties of heart rate signals, and organ dysfunction longitudinally over time in intensive care unit patients. J Crit Care. 2006;21:95–103 discussion-4.CrossRefPubMed
26.
go back to reference Burr RL, Kirkness CJ, Mitchell PH. Detrended fluctuation analysis of intracranial pressure predicts outcome following traumatic brain injury. IEEE Trans Biomed Eng. 2008;55:2509–18.CrossRefPubMedCentralPubMed Burr RL, Kirkness CJ, Mitchell PH. Detrended fluctuation analysis of intracranial pressure predicts outcome following traumatic brain injury. IEEE Trans Biomed Eng. 2008;55:2509–18.CrossRefPubMedCentralPubMed
27.
go back to reference Buchman TG. Nonlinear dynamics, complex systems, and the pathobiology of critical illness. Curr Opin Crit Care. 2004;10:378–82.CrossRefPubMed Buchman TG. Nonlinear dynamics, complex systems, and the pathobiology of critical illness. Curr Opin Crit Care. 2004;10:378–82.CrossRefPubMed
29.
go back to reference Godin PJ, Buchman TG. Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med. 1996;24:1107–16.CrossRefPubMed Godin PJ, Buchman TG. Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med. 1996;24:1107–16.CrossRefPubMed
31.
go back to reference Jacono FF, DeGeorgia MA, Wilson CG, Dick TE, Loparo KA. Data acquisition and complex systems analysis in critical care: developing the intensive care unit of the future. J Healthc Eng. 2010;1:336–7. Jacono FF, DeGeorgia MA, Wilson CG, Dick TE, Loparo KA. Data acquisition and complex systems analysis in critical care: developing the intensive care unit of the future. J Healthc Eng. 2010;1:336–7.
32.
go back to reference Morris G, Gardner R. Computer applications. In: Hall J, Schmidt G, Wood L, editors. Principles of critical care. New York: McGraw-Hill; 1992. p. 500–14. Morris G, Gardner R. Computer applications. In: Hall J, Schmidt G, Wood L, editors. Principles of critical care. New York: McGraw-Hill; 1992. p. 500–14.
33.
go back to reference Morris A, Gardner R. Computer applications. Principles of critical care. New York: McGraw-Hill; 1992. p. 500–14. Morris A, Gardner R. Computer applications. Principles of critical care. New York: McGraw-Hill; 1992. p. 500–14.
34.
go back to reference Imhoff M. Detecting relationships between physiological variables using graphical modeling. Annual Symposium, Proceedings of the AMIA; 2002. Imhoff M. Detecting relationships between physiological variables using graphical modeling. Annual Symposium, Proceedings of the AMIA; 2002.
35.
go back to reference Woods DD, Patterson ES, Roth EM. Can we ever escape from data overload? A cognitive systems diagnosis. Cogn Technol Work. 2002;4:22–36.CrossRef Woods DD, Patterson ES, Roth EM. Can we ever escape from data overload? A cognitive systems diagnosis. Cogn Technol Work. 2002;4:22–36.CrossRef
36.
go back to reference De Turck F, Decruyenaere J, Thysebaert P, et al. Design of a flexible platform for execution of medical decision support agents in the intensive care unit. Comput Biol Med. 2007;37:97–112.CrossRefPubMed De Turck F, Decruyenaere J, Thysebaert P, et al. Design of a flexible platform for execution of medical decision support agents in the intensive care unit. Comput Biol Med. 2007;37:97–112.CrossRefPubMed
37.
go back to reference Jennings D, Amabile T, Ross L. Informal assessments: data-based versus theory-based judgments. In: Kahnemann D, Slovic P, Tversky A, editors. Judgments under uncertainty: heuristics and biases. Cambridge: Cambridge University Press; 1982. p. 211–30.CrossRef Jennings D, Amabile T, Ross L. Informal assessments: data-based versus theory-based judgments. In: Kahnemann D, Slovic P, Tversky A, editors. Judgments under uncertainty: heuristics and biases. Cambridge: Cambridge University Press; 1982. p. 211–30.CrossRef
38.
go back to reference Woods D. Human-Computer Interaction and Complex Systems. London: The cognitive engineering of problem representations. Academic Press; 1991. p. 169–88. Woods D. Human-Computer Interaction and Complex Systems. London: The cognitive engineering of problem representations. Academic Press; 1991. p. 169–88.
39.
go back to reference Zhang J, Norman DA. Representations in distributed cognitive tasks. Cognit Sci. 1994;18:87–122.CrossRef Zhang J, Norman DA. Representations in distributed cognitive tasks. Cognit Sci. 1994;18:87–122.CrossRef
40.
go back to reference Roth EM, Patterson ES, Mumaw RJ. Cognitive engineering: issues in user-centered system design. Encyclopedia of software engineering. New York: Wiley; 2002. p. 163–79. Roth EM, Patterson ES, Mumaw RJ. Cognitive engineering: issues in user-centered system design. Encyclopedia of software engineering. New York: Wiley; 2002. p. 163–79.
41.
go back to reference Tufte E, editor. Envisioning information. Cheshire: Graphic Press; 1990. Tufte E, editor. Envisioning information. Cheshire: Graphic Press; 1990.
42.
go back to reference Tufte ER. The visual display of quantitative information. Cheshire: Graphics press; 1983. Tufte ER. The visual display of quantitative information. Cheshire: Graphics press; 1983.
43.
go back to reference Tufte ER. Envisioning information. 1990. Visual Explanations: Images and Quan 2006. Tufte ER. Envisioning information. 1990. Visual Explanations: Images and Quan 2006.
44.
go back to reference Woods DD. Visual momentum: a concept to improve the cognitive coupling of person and computer. Int J Man Mach Stud. 1984;21:229–44.CrossRef Woods DD. Visual momentum: a concept to improve the cognitive coupling of person and computer. Int J Man Mach Stud. 1984;21:229–44.CrossRef
45.
go back to reference Keim DA. Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans Vis Comput Gr. 2000;6:59–78.CrossRef Keim DA. Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans Vis Comput Gr. 2000;6:59–78.CrossRef
46.
go back to reference Faiola A, Newlon C. Advancing Critical Care in the ICU: A human-centered biomedical data visualization systems. Ergonomics and Health Aspects of Work with Computers 2011:119–28. Faiola A, Newlon C. Advancing Critical Care in the ICU: A human-centered biomedical data visualization systems. Ergonomics and Health Aspects of Work with Computers 2011:119–28.
47.
go back to reference Zhang J. Human-centered computing in health information systems Part 1: analysis and design. J Biomed Inform. 2005;38:1–3.CrossRefPubMed Zhang J. Human-centered computing in health information systems Part 1: analysis and design. J Biomed Inform. 2005;38:1–3.CrossRefPubMed
48.
go back to reference Ordóñez P, desJardins M, Lombardi M, Lehmann CU, Fackler J. An animated multivariate visualization for physiological and clinical data in the ICU. 2010: ACM. p. 771–9. Ordóñez P, desJardins M, Lombardi M, Lehmann CU, Fackler J. An animated multivariate visualization for physiological and clinical data in the ICU. 2010: ACM. p. 771–9.
49.
go back to reference Koch S, Staggers N, Weir C, Agutter J, Liu D, Westenskow D. Integrated Information Displays for ICU Nurses: Field Observations, Display Design, and Display Evaluation. 2010: SAGE Publications. pp. 932–6. Koch S, Staggers N, Weir C, Agutter J, Liu D, Westenskow D. Integrated Information Displays for ICU Nurses: Field Observations, Display Design, and Display Evaluation. 2010: SAGE Publications. pp. 932–6.
50.
go back to reference Elson RB, Connelly DP. The impact of anticipatory patient data displays on physician decision making: a pilot study. 1997: American Medical Informatics Association. p. 233. Elson RB, Connelly DP. The impact of anticipatory patient data displays on physician decision making: a pilot study. 1997: American Medical Informatics Association. p. 233.
51.
go back to reference Balas EA. Interactive computer graphics support of medical decision-making: Department of Medical Informatics, University of Utah; 1991. Balas EA. Interactive computer graphics support of medical decision-making: Department of Medical Informatics, University of Utah; 1991.
52.
go back to reference Plaisant C, Milash B, Rose A, Widoff S, Shneiderman B. LifeLines: visualizing personal histories. 1996: ACM. pp. 221–7. Plaisant C, Milash B, Rose A, Widoff S, Shneiderman B. LifeLines: visualizing personal histories. 1996: ACM. pp. 221–7.
53.
go back to reference Alonso D, Rose A, Plaisant C, Norman K. Viewing personal history records: a comparison of tabular format and graphical presentation using lifelines. Behav Inf Technol. 1997;17:249–62.CrossRef Alonso D, Rose A, Plaisant C, Norman K. Viewing personal history records: a comparison of tabular format and graphical presentation using lifelines. Behav Inf Technol. 1997;17:249–62.CrossRef
54.
go back to reference Effken JA, Loeb RG, Kang Y, Lin ZC. Clinical information displays to improve ICU outcomes. Int J Med Inf. 2008;77:765–77.CrossRef Effken JA, Loeb RG, Kang Y, Lin ZC. Clinical information displays to improve ICU outcomes. Int J Med Inf. 2008;77:765–77.CrossRef
55.
go back to reference Chan WW-Y. A survey on multivariate data visualization. Department of Computer Science and Engineering Hong Kong University of Science and Technology 2006;8:1–29. Chan WW-Y. A survey on multivariate data visualization. Department of Computer Science and Engineering Hong Kong University of Science and Technology 2006;8:1–29.
57.
go back to reference Moody GB, Lehman L. Predicting acute hypotensive episodes. The 10th annual physioNet/computers in cardiology challenge. Computers in Cardiology, 2009: IEEE pp. 541–4. Moody GB, Lehman L. Predicting acute hypotensive episodes. The 10th annual physioNet/computers in cardiology challenge. Computers in Cardiology, 2009: IEEE pp. 541–4.
59.
go back to reference Angus D, Kelley M, Schmitz R, White A, Popovich J Jr. Caring for the critically ill patient. Current and projected workforce requirements for care of the critically ill and patients with pulmonary disease: can we meet the requirements of an aging population? JAMA. 2000;284:2762–70.CrossRefPubMed Angus D, Kelley M, Schmitz R, White A, Popovich J Jr. Caring for the critically ill patient. Current and projected workforce requirements for care of the critically ill and patients with pulmonary disease: can we meet the requirements of an aging population? JAMA. 2000;284:2762–70.CrossRefPubMed
60.
go back to reference Young LB, Chan PS, Lu X, Nallamothu BK, Sasson C, Cram PM. Impact of telemedicine intensive care unit coverage on patient outcomes: a systematic review and meta-analysis. Arch Intern Med. 2011;171:498.CrossRefPubMed Young LB, Chan PS, Lu X, Nallamothu BK, Sasson C, Cram PM. Impact of telemedicine intensive care unit coverage on patient outcomes: a systematic review and meta-analysis. Arch Intern Med. 2011;171:498.CrossRefPubMed
61.
go back to reference Cummings J, Krsek C, Vermoch K, Matuszewski K. Intensive care unit telemedicine: review and consensus recommendations. Am J Med Qual. 2007;22:239–50.CrossRefPubMed Cummings J, Krsek C, Vermoch K, Matuszewski K. Intensive care unit telemedicine: review and consensus recommendations. Am J Med Qual. 2007;22:239–50.CrossRefPubMed
62.
64.
go back to reference H.R. 1581. The Patient-Focused Critical Care Enhancement Act, March 18, 2009. H.R. 1581. The Patient-Focused Critical Care Enhancement Act, March 18, 2009.
Metadata
Title
Multimodality Monitoring: Informatics, Integration Data Display and Analysis
Authors
J. Michael Schmidt
Michael De Georgia
The Participants in the International Multidisciplinary Consensus Conference on Multimodality Monitoring
Publication date
01-12-2014
Publisher
Springer US
Published in
Neurocritical Care / Issue Special Issue 2/2014
Print ISSN: 1541-6933
Electronic ISSN: 1556-0961
DOI
https://doi.org/10.1007/s12028-014-0037-1

Other articles of this Special Issue 2/2014

Neurocritical Care 2/2014 Go to the issue