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Quantifying Pulsus Paradoxus

Quantifying Pulsus Paradoxus. Kara Bliley Gina Lee Allison Powers Advisor: Tina V. Hartert, M.D., M.P.H. Background. Pulse oximetry:. Readings are based on pulsatile absorption Arterial blood assumed to be the only pulsatile absorbance between light source and photodetector. Background.

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Quantifying Pulsus Paradoxus

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  1. Quantifying Pulsus Paradoxus Kara Bliley Gina Lee Allison Powers Advisor: Tina V. Hartert, M.D., M.P.H.

  2. Background Pulse oximetry: • Readings are based on pulsatile absorption • Arterial blood assumed to be the only pulsatile absorbance between light source and photodetector

  3. Background • Pulse oximetry is the measure of “functional O2 saturation” which is defined as the percentage of oxyhemoglobin (O2Hb) relative to the total amount of Hb available for binding:

  4. Background Pulsus paradoxus: Tracing Exemplifying Pulsus Paradoxus • Defined as an abnormally large inspiratory decline in systemic arterial pressure (>10mmHg) • Observed in severe asthma, heart failure, and forced respiratory effort Functional O2 saturation time Normal Tracing

  5. Objective • To develop an algorithm to quantify pulsus paradoxus Normal arterial pressure trace: Pulsus paradoxus: Functional O2 saturation time

  6. Methods: Digitizing Data • Developed scales for the printouts • Entered the data by hand into Excel spreadsheets • About six complete cycles of data entered for each • Number of data sets: 1 normal, 8 abnormal

  7. Sample from Spreadsheet

  8. Evaluate data using MATLAB Frequency components Changes in amplitude based on reference point Changes in average value Compare the normal and abnormal data sets to see if there are statistically significant differences Data Analysis Methods Height difference Area under curve

  9. Conclusions about the severity of the abnormality will be drawn based on our findings for each data analysis method we pursue. The conclusions made using our methods will be compared with those made previously by physicians. We are currently blinded to those diagnoses. The method for which the most correct conclusions are drawn will be decidedly the best method of analysis. Clinical Application

  10. Current Status • Data has been entered in Microsoft Excel • We are analyzing data using MATLAB Current Work • We are analyzing using MATLAB • Blind study

  11. Work Completed • Data has been digitized. Future Work • Compare our results with established scale • May need help for analyzing data: • Patrick Norris • Richard Shiavi, Ph.D.

  12. Acknowledgements • VUMC Intensive Care Unit Staff • Patrick Norris • Figures used throughout presentation were obtained from presentations given by our advisor References

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