Physiologic data acquisition system and database for the study of disease dynamics in the intensive care unit*

ObjectiveTo describe a real-time, continuous physiologic data acquisition system for the study of disease dynamics in the intensive care unit. DesignDescriptive report. SettingA 16-bed pediatric intensive care unit in a tertiary care children’s hospital. PatientsA total of 170 critically ill or injured pediatric patients. InterventionsNone. Main Outcome MeasuresNone. ResultsWe describe a computerized data acquisition and analysis system for the study of critical illness and injury from the perspective of complex dynamic systems. Both parametric (1 Hz) and waveform (125–500 Hz) signals are recorded and analyzed. Waveform data include electrocardiogram, respiration, systemic arterial pressure (invasive and noninvasive), central venous pressure, pulmonary arterial pressure, left and right atrial pressures, intracranial pressure, body temperature, and oxygen saturation. Details of the system components are explained and examples are given from the resultant physiologic database of signal processing algorithms and signal analyses using linear and nonlinear metrics. ConclusionsWe have successfully developed a real-time, continuous physiologic data acquisition system that can capture, store, and archive data from pediatric intensive care unit patients for subsequent time series analysis of dynamic changes in physiologic state. The physiologic signal database generated from this system is available for analysis of dynamic changes caused by critical illness and injury.

[1]  Urs E. Ruttimann,et al.  Decomplexification in critical illness and injury: Relationship between heart rate variability, severity of illness, and outcome , 1998, Critical care medicine.

[2]  A. Goldberger.  Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside , 1996, The Lancet.

[3]  M. H. Kempski,et al.  Heart Rate Power Spectrum and Plasma Catecholamine Levels after Postural Change and Cold Pressor Test , 1994, Pediatric Research.

[4]  G. Breithardt,et al.  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. , 1996 .

[5]  Kari Nieminen,et al.  IBIS Data Library: clinical description of the Finnish database , 2000, Comput. Methods Programs Biomed..

[6]  John Gade,et al.  Technical description of the IBIS Data Library , 2000, Comput. Methods Programs Biomed..

[7]  R J Sclabassi,et al.  Uncoupling and recoupling of autonomic regulation of the heart beat in pediatric septic shock. , 2001, Shock.

[8]  B. Goldstein,et al.  Heart Rate Variability in Intensive Care , 1998 .

[9]  G. Bonsel,et al.  Assessing the outcome of pediatric intensive care. , 1993, The Journal of pediatrics.

[10]  M. Emdin,et al.  Impaired sympathetic response before intradialytic hypotension: a study based on spectral analysis of heart rate and pressure variability. , 1999, Clinical science.

[11]  J. Mcnames,et al.  Precursors to rapid elevations in intracranial pressure , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  G. F. Mandersloot,et al.  The IBIS project: data collection in London , 2000, Comput. Methods Programs Biomed..

[13]  Roger G. Mark,et al.  The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it , 1990, [1990] Proceedings Computers in Cardiology.

[14]  B. Jennett,et al.  Assessment of coma and impaired consciousness. A practical scale. , 1974, Lancet.

[15]  Brahm Goldstein,et al.  Linear and nonlinear analysis of hemodynamic signals during sepsis and septic shock , 2000, Critical care medicine.

[16]  Anthony Slater,et al.  Paediatric index of mortality (PIM): a mortality prediction model for children in intensive care , 1997, Intensive Care Medicine.

[17]  D. Hoyt,et al.  Spectral analysis of heart rate variability in the ICU: a measure of autonomic function. , 1996, The Journal of surgical research.

[18]  I Korhonen,et al.  Building the IMPROVE Data Library. , 1997, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[19]  I. Karhonen,et al.  The challenges in creating critical-care databases , 2001, IEEE Engineering in Medicine and Biology Magazine.

[20]  James McNames,et al.  Automatic detection algorithm of intracranial pressure waveform components , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  T B Kuo,et al.  Spectral analysis of systemic arterial pressure and heart rate signals as a prognostic tool for the prediction of patient outcome in the intensive care unit. , 1997, Critical care medicine.

[22]  Leon Glass,et al.  Dynamical disease: Identification, temporal aspects and treatment strategies of human illness. , 1995, Chaos.

[23]  T G Buchman,et al.  Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. , 1996, Critical care medicine.

[24]  I Kropyvnytskyy,et al.  A computer system for continuous long-term recording, processing, and analysis of physiological data of brain injured patients in ICU settings. , 2001, Brain injury.

[25]  B. Jennett,et al.  ASSESSMENT OF OUTCOME AFTER SEVERE BRAIN DAMAGE A Practical Scale , 1975, The Lancet.

[26]  J Gade,et al.  Collecting EEG signals in the IMPROVE Data Library. , 1997, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[27]  T G Buchman,et al.  Physiologic stability and physiologic state. , 1996, The Journal of trauma.

[28]  B. Goldstein,et al.  Advances in Intensive Care ECG and Cardiac Event Monitoring , 1999 .

[29]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[30]  B Jennett,et al.  Assessment of outcome after severe brain damage. , 1975, Lancet.

[31]  Solange Akselrod,et al.  Heart-rate spectral analysis: A noninvasive probe of cardiovascular regulation in critically ill children with heart disease , 2005, Pediatric Cardiology.