Journal of Healthcare Engineering

Journal of Healthcare Engineering / 2010 / Article

Research Article | Open Access

Volume 1 |Article ID 828614 | 21 pages | https://doi.org/10.1260/2040-2295.1.3.315

SIMON: A Decade of Physiological Data Research and Development in Trauma Intensive Care

Abstract

SIMON (Signal Interpretation and MONitoring) continuously collects and processes bedside medical device data. As of December 2009, SIMON has monitored over 7,630 trauma intensive care unit (TICU) patients, representing approximately 731,000 hours of continuous monitoring, and is currently operational on all TICU beds at Vanderbilt University Medical Center. Parameters captured include heart rate, blood pressures, oxygen saturations, cardiac function variables, intracranial and cerebral perfusion pressures, and EKG waveforms. This repository supports research to identify “new vital signs” based on features of patient physiology observable through dense data capture and analysis, with the goal of improving predictions of patient status. SIMON's alerting and reporting capabilities include web display, sentinel event notification, and daily summary reports of traditional and new vital sign statistics. This allows discoveries to be rapidly tested and implemented in a working clinical environment. The work details SIMON's technology and corresponding design requirements to realize the value of dense physiologic data in critical care.

References

  1. H. W. Cushing, “On Routine Determination of Arterial Tension in Operating Room And Clinic,” Boston Medical and Surgical Journal, vol. 148, pp. 250–256, 1903. View at: Google Scholar
  2. H. Cao, D. E. Lake, M. P. Griffin, and J. R. Moorman, “Increased Nonstationarity of Neonatal Heart Rate before the Clinical Diagnosis of Sepsis,” Annals of Biomedical Engineering, vol. 32, no. 2, pp. 233–244, 2004. View at: Google Scholar
  3. M. P. Griffin, D. E. Lake, E. A. Bissonette, F. E. Harrell Jr., T. M. O'Shea, and J. R. Moorman, “Heart Rate Characteristics: Novel Physiomarkers to Predict Neonatal Infection and Death,” Pediatrics, vol. 116, no. 5, pp. 1070–1074, 2005. View at: Google Scholar
  4. D. Barnaby, K. Ferrick, D. T. Kaplan, S. Shah, P. Bijur, and E. J. Gallagher, “Heart Rate Variability in Emergency Department Patients With Sepsis,” Academic Emergency Medicine, vol. 9, pp. 661–670, 2002. View at: Google Scholar
  5. M. Korach, T. Sharshar, I. Jarrin et al., “Cardiac Variability in Critically Ill Adults: Influence of Sepsis,” Critical Care Medicine, vol. 29, no. 7, pp. 1380–1385, 2001. View at: Google Scholar
  6. M. S. Ellenby, J. McNames, S. Lai et al., “Uncoupling and Recoupling of Autonomic Regulation of the Heart Beat in Pediatric Septic Shock,” Shock, vol. 16, no. 4, pp. 274–277, 2001. View at: Google Scholar
  7. S. Ahmad, T. Ramsay, L. Huebsch et al., “Continuous Multi-parameter Heart Rate Variability Analysis Heralds Onset of Sepsis in Adults,” PLoS ONE, vol. 4, no. 8, e6642, 2009. View at: Google Scholar
  8. J. Pontet, P. Contreras, A. Curbelo et al., “Heart Rate Variability as Early Marker of Multiple Organ Dysfunction Syndrome in Septic Patients,” Journal of Critical Care, vol. 18, no. 3, pp. 156–163, 2003. View at: Google Scholar
  9. S. M. Tibby, H. Frndova, A. Durward, and P. N. Cox, “Novel Method to Quantify Loss of Heart Rate Variability in Pediatric Multiple Organ Failure,” Critical Care Medicine, vol. 31, no. 7, pp. 2059–2067, 2003. View at: Google Scholar
  10. D. Wichterle, J. Simek, M. T. La Rovere, P. J. Schwartz, A. J. Camm, and M. Malik, “Prevalent Low-Frequency Oscillation of Heart Rate: Novel Predictor of Mortality after Myocardial Infarction,” Circulation, vol. 110, no. 10, pp. 1183–1190, 2004. View at: Google Scholar
  11. M. T. La Rovere, G. D. Pinna, R. Maestri et al., “Short-term Heart Rate Variability Strongly Predicts Sudden Cardiac Death in Chronic Heart Failure Patients,” Circulation, vol. 107, no. 4, pp. 565–570, 2003. View at: Google Scholar
  12. N. B. Wood, “The Prediction of a Potentially Fatal Cardiac Event in the Next 2 to 24 Hours and the Prediction of a Myocardial Infarction Related Death Or Sudden Death,” Computers in Cardiology, vol. 28, pp. 509–512, 2001. View at: Google Scholar
  13. R. J. Winchell and D. B. Hoyt, “Analysis of Heart-Rate Variability: A Noninvasive Predictor of Death and Poor Outcome in Patients with Severe Head Injury,” Journal of Trauma, Injury, Infection, and Critical Care, vol. 43, no. 6, pp. 927–933, 1997. View at: Google Scholar
  14. W. H. Cooke, J. Salinas, V. A. Convertino et al., “Heart Rate Variability and its Association with Mortality in Prehospital Trauma Patients,” Journal of Trauma, Injury, Infection, and Critical Care, vol. 60, no. 2, pp. 363–370, 2006. View at: Google Scholar
  15. L. C. Cancio, A. I. Batchinsky, J. Salinas et al., “Heart-Rate Complexity for Prediction of Prehospital Lifesaving Interventions in Trauma Patients,” Journal of Trauma, Injury, Infection, and Critical Care, vol. 65, no. 4, pp. 813–819, 2008. View at: Google Scholar
  16. D. R. King, M. P. Ogilvie, B. M. Pereira et al., “Heart Rate Variability as a Triage Tool in Patients with Trauma During Prehospital Helicopter Transport,” Journal of Trauma, Injury, Infection, and Critical Care, vol. 67, no. 3, pp. 436–440, 2009. View at: Google Scholar
  17. R. B. Panerai, “Assessment of Cerebral Pressure Autoregulation in Humans-A Review of Measurement Methods,” Physiological Measurement, vol. 19, no. 3, pp. 305–338, 1998. View at: Google Scholar
  18. M. Czosnyka, P. Smielewski, Z. Czosnyka et al., “Continuous Assessment of Cerebral Autoregulation: Clinical and Laboratory Experience,” Acta Neurochirurgica Supplement, vol. 86, pp. 581–585, 2003. View at: Google Scholar
  19. V. Castelain, P. Herve, Y. Lecarpentier, P. Duroux, G. Simonneau, and D. Chemla, “Pulmonary Artery Pulse Pressure and Wave Reflection in Chronic Pulmonary Thromboembolism and Primary Pulmonary Hypertension,” Journal of the American College of Cardiology, vol. 37, no. 4, pp. 1085–1092, 2001. View at: Google Scholar
  20. C. J. McDonald, “Protocol-Based Computer Reminders, the Quality of Care and the Non-Perfectability of Man,” New England Journal of Medicine, vol. 295, no. 24, pp. 1351–1355, 1976. View at: Google Scholar
  21. M. C. Chambrin, P. Ravaux, D. Calvelo-Aros, A. Jaborska, C. Chopin, and B. Boniface, “Multicentric Study of Monitoring Alarms in the Adult Intensive Care Unit (ICU): A Descriptive Analysis,” Intensive Care Medicine, vol. 25, no. 12, pp. 1360–1366, 1999. View at: Google Scholar
  22. J. Doyle, I. Kohane, W. Long, and P. Szolovts, The Architecture of MAITA: A Tool for Monitoring, Analysis, and Interpretation, Technical Report, Massachusetts Institute of Technology Laboratory for Computer Science, 1999.
  23. M. Factor, D. H. Gelernter, and D. F. Sittig, “The Multi-Trellis Software Architecture and the Intelligent Cardiovascular Monitor,” Methods of Information in Medicine, vol. 31, no. 1, pp. 44–55, 1992. View at: Google Scholar
  24. J. E. Larsson, B. Hayes-Roth, and D. Gaba, Guardian: Final Evaluation, Technical Report, Stanford University Knowledge Systems Lab, 1996.
  25. A. Seyfang, S. Miksch, and M. Marcos, “Combining Diagnosis and Treatment Using ASBRU,” International Journal of Medical Informatics, vol. 68, no. 1-3, pp. 49–57, 2002. View at: Google Scholar
  26. S. Uckun, B. M. Dawant, and D. P. Lindstrom, “Model-based Diagnosis in Intensive Care Monitoring: The YAQ Approach,” Artificial Intelligence in Medicine, vol. 5, no. 1, pp. 31–48, 1993. View at: Google Scholar
  27. M. C. Reddy, D. W. McDonald, W. Pratt, and M. M. Shabot, “Technology, Work, and Information Flows: Lessons from the Implementation of a Wireless Alert Pager System,” Journal of Biomedical Informatics, vol. 38, no. 3, pp. 229–238, 2005. View at: Google Scholar
  28. H. T. Chen, W. C. Ma, and D. M. Liou, “Design and Implementation of a Real-Time Clinical Alerting System for Intensive Care Unit,” in Proceedings of the Annual Symposium of the American Medical Informatics Association, pp. 131–135, Bethesda, Maryland, 2002. View at: Google Scholar
  29. K. Major, M. M. Shabot, and S. Cunneen, “Wireless Clinical Alerts and Patient Outcomes in the Surgical Intensive Care Unit,” American Surgeon, vol. 68, no. 12, pp. 1057–1060, 2002. View at: Google Scholar
  30. B. Goldstein, J. McNames, B. A. McDonald et al., “Physiologic Data Acquisition System and Database for the Study of Disease Dynamics in the Intensive Care Unit,” Critical Care Medicine, vol. 31, no. 2, pp. 433–441, 2003. View at: Google Scholar
  31. M. Saeed, C. Lieu, G. Raber, and R. G. Mark, “MIMIC II: AMassive Temporal ICU Patient Database to Support Research in Intelligent Patient Monitoring,” Computers in Cardiololgy, vol. 29, pp. 641–644, 2002. View at: Google Scholar
  32. I. Kropyvnytskyy, F. Saunders, P. Schierek, and M. Pols, “A Computer System for Continuous Long-Term Recording, Processing, and Analysis of Physiological Data of Brain Injured Patients in ICU Settings,” Brain Injury, vol. 15, no. 7, pp. 577–583, 2001. View at: Google Scholar
  33. I. Korhonen, J. Ojaniemi, K. Nieminen, M. van Gils, A. Heikela, and A. Kari, “Building the IMPROVE Data Library,” IEEE Engineering in Medicine and Biology Magazine, vol. 16, no. 6, pp. 25–32, 1997. View at: Google Scholar
  34. K. Vinecore, M. Aboy, J. McNames et al., “Design and Implementation of a Portable Physiologic Data Acquisition System,” Pediatric Critical Care Medicine, vol. 8, no. 6, pp. 563–569, 2007. View at: Google Scholar
  35. M. J. Breslow, “Remote ICU Care Programs: Current Status,” Journal of Critical Care, vol. 22, no. 1, pp. 66–76, 2007. View at: Google Scholar
  36. D. Krieger, G. Burk, and R. J. Sclabassi, “Neuronet - A Distributed Real-Time System for Monitoring Neurophysiologic Function in the Medical Environment,” Computer, vol. 24, no. 3, pp. 45–55, 1991. View at: Google Scholar
  37. R. M. Gardner, T. A. Pryor, and H. R. Warner, “The HELP Hospital Information System: Update 1998,” International Journal of Medical Informatics, vol. 54, no. 3, pp. 169–182, 1999. View at: Google Scholar
  38. A. L. Goldberger, L. A. N. Amaral, L. Glass et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000. View at: Google Scholar
  39. P. Nilsson, I. Piper, G. Citerio et al., “The BrainIT Group: Concept and Current Status 2004,” Acta Neurochirurgica Supplement, vol. 95, pp. 33–37, 2005. View at: Google Scholar
  40. H. Cao, P. Norris, A. Ozdas, J. Jenkins, and J. A. Morris Jr., “A Simple Non-Physiological Artifact Filter For Invasive Arterial Blood Pressure Monitoring: A Study of 1852 Trauma ICU Patients,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1417–1420, New York, New York, 2006. View at: Google Scholar
  41. B. M. Dawant, E. J. Manders, and D. P. Lindstrom, “Adaptive Signal Analysis and Interpretation for Real-Time Intelligent Patient Monitoring,” Methods of Information in Medicine, vol. 33, no. 1, pp. 60–63, 1994. View at: Google Scholar
  42. E. J. Manders and B. M. Dawant, “Design of a Dynamically Reconfigurable Critical Care Monitor,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1032–1035, Chicago, Illinois, 1997. View at: Google Scholar
  43. E. L. Grogan, P. R. Norris, T. Speroff et al., “Volatility: A New Vital Sign Identified Using a Novel Bedside Monitoring Strategy,” Journal of Trauma, Injury, Infection, and Critical Care, vol. 58, no. 1, pp. 7–12, 2005. View at: Google Scholar
  44. E. L. Grogan, J. A. Morris Jr., P. R. Norris et al., “Reduced Heart Rate Volatility: An Early Predictor of Death in Trauma Patients,” Annals of Surgery, vol. 240, no. 3, pp. 547–554, 2004. View at: Google Scholar
  45. P. R. Norris, J. A. Morris Jr., A. Ozdas, E. L. Grogan, and A. E. Williams, “Heart Rate Variability Predicts Trauma Patient Outcome as Early as 12 H: Implications for Military and Civilian Triage,” Journal of Surgical Research, vol. 129, no. 1, pp. 122–128, 2005. View at: Google Scholar
  46. M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale Entropy Analysis of Complex Physiologic Time Series,” Physical Review Letters, vol. 89, no. 6, 068102, 2002. View at: Google Scholar
  47. P. R. Norris, S. M. Anderson, J. M. Jenkins, A. E. Williams, and J. A. Morris Jr., “Heart Rate Multiscale Entropy at Three Hours Predicts Hospital Mortality in 3,154 Trauma Patients,” Shock, vol. 30, no. 1, pp. 17–22, 2008. View at: Google Scholar
  48. W. P. Riordan Jr., P. R. Norris, J. M. Jenkins, and J. A. Morris Jr., “Early Loss of Heart Rate Complexity Predicts Mortality Regardless of Mechanism, Anatomic Location, or Severity of Injury in 2178 Trauma Patients,” Journal of Surgical Research, vol. 156, no. 2, pp. 283–289, 2009. View at: Google Scholar
  49. P. R. Norris, J. A. Canter, J. M. Jenkins, J. H. Moore, A. E. Williams, and J. A. Morris Jr., “Personalized Medicine: Genetic Variation and Loss of Physiologic Complexity Are Associated with Mortality in 644 Trauma Patients,” Annals of Surgery, 2009. View at: Google Scholar
  50. B. Goldstein, D. H. Fiser, M. M. Kelly, D. Mickelsen, U. Ruttimann, and M. M. Pollack, “Decomplexification in Critical Illness and Injury: Relationship Between Heart Rate Variability, Severity of Illness, and Outcome,” Critical Care Medicine, vol. 26, no. 2, pp. 352–357, 1998. View at: Google Scholar
  51. A. J. Seely and N. V. Christou, “Multiple Organ Dysfunction Syndrome: Exploring the Paradigm of Complex Nonlinear Systems,” Critical Care Medicine, vol. 28, no. 7, pp. 2193–2200, 2000. View at: Google Scholar
  52. T. G. Buchman, “Nonlinear Dynamics, Complex Systems, and the Pathobiology of Critical Illness,” Current Opinion in Critical Care, vol. 10, no. 5, pp. 378–382, 2004. View at: Google Scholar
  53. P. R. Norris and B. M. Dawant, “Closing the Loop in ICU Decision Support: Physiologic Event Detection, Alerts, and Documentation,” in Proceedings of the Annual Symposium of the American Medical Informatics Association, pp. 498–502, Bethesda, Maryland, 2001. View at: Google Scholar
  54. R. A. Greenes, A. N. Pappalardo, C. W. Marble, and G. O. Barnett, “Design and Implementation of a Clinical Data Management System,” Computers in Biomedical Research, vol. 2, no. 5, pp. 469–485, 1969. View at: Google Scholar

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