Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion

DPOP quantifies respiratory modulations in the photoplethysmogram. It has been proposed as a noninvasive surrogate for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. The correlation between DPOP and PPV may degrade due to low perfus...

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Main Authors: Paul S. Addison, Rui Wang, Scott J. McGonigle, Alberto A. Uribe, Sergio D. Bergese
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Anesthesiology Research and Practice
Online Access:http://dx.doi.org/10.1155/2014/980149
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author Paul S. Addison
Rui Wang
Scott J. McGonigle
Alberto A. Uribe
Sergio D. Bergese
author_facet Paul S. Addison
Rui Wang
Scott J. McGonigle
Alberto A. Uribe
Sergio D. Bergese
author_sort Paul S. Addison
collection DOAJ
description DPOP quantifies respiratory modulations in the photoplethysmogram. It has been proposed as a noninvasive surrogate for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. The correlation between DPOP and PPV may degrade due to low perfusion effects. We implemented an automated DPOP algorithm with an optional correction for low perfusion. These two algorithm variants (DPOPa and DPOPb) were tested on data from 20 mechanically ventilated OR patients split into a benign “stable region” subset and a whole record “global set.” Strong correlation was found between DPOP and PPV for both algorithms when applied to the stable data set: R=0.83/0.85 for DPOPa/DPOPb. However, a marked improvement was found when applying the low perfusion correction to the global data set: R=0.47/0.73 for DPOPa/DPOPb. Sensitivities, Specificities, and AUCs were 0.86, 0.70, and 0.88 for DPOPa/stable region; 0.89, 0.82, and 0.92 for DPOPb/stable region; 0.81, 0.61, and 0.73 for DPOPa/global region; 0.83, 0.76, and 0.86 for DPOPb/global region. An improvement was found in all results across both data sets when using the DPOPb algorithm. Further, DPOPb showed marked improvements, both in terms of its values, and correlation with PPV, for signals exhibiting low percent modulations.
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spelling doaj-art-6af3ded6ad344d7d98c0c8f92ba2b2af2025-08-20T03:34:00ZengWileyAnesthesiology Research and Practice1687-69621687-69702014-01-01201410.1155/2014/980149980149Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low PerfusionPaul S. Addison0Rui Wang1Scott J. McGonigle2Alberto A. Uribe3Sergio D. Bergese4Covidien Respiratory & Monitoring Solutions, Edinburgh EH26 0PJ, UKCovidien Respiratory & Monitoring Solutions, Edinburgh EH26 0PJ, UKCovidien Respiratory & Monitoring Solutions, Edinburgh EH26 0PJ, UKDepartment of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USADepartment of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USADPOP quantifies respiratory modulations in the photoplethysmogram. It has been proposed as a noninvasive surrogate for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. The correlation between DPOP and PPV may degrade due to low perfusion effects. We implemented an automated DPOP algorithm with an optional correction for low perfusion. These two algorithm variants (DPOPa and DPOPb) were tested on data from 20 mechanically ventilated OR patients split into a benign “stable region” subset and a whole record “global set.” Strong correlation was found between DPOP and PPV for both algorithms when applied to the stable data set: R=0.83/0.85 for DPOPa/DPOPb. However, a marked improvement was found when applying the low perfusion correction to the global data set: R=0.47/0.73 for DPOPa/DPOPb. Sensitivities, Specificities, and AUCs were 0.86, 0.70, and 0.88 for DPOPa/stable region; 0.89, 0.82, and 0.92 for DPOPb/stable region; 0.81, 0.61, and 0.73 for DPOPa/global region; 0.83, 0.76, and 0.86 for DPOPb/global region. An improvement was found in all results across both data sets when using the DPOPb algorithm. Further, DPOPb showed marked improvements, both in terms of its values, and correlation with PPV, for signals exhibiting low percent modulations.http://dx.doi.org/10.1155/2014/980149
spellingShingle Paul S. Addison
Rui Wang
Scott J. McGonigle
Alberto A. Uribe
Sergio D. Bergese
Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion
Anesthesiology Research and Practice
title Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion
title_full Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion
title_fullStr Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion
title_full_unstemmed Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion
title_short Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion
title_sort calculation of the respiratory modulation of the photoplethysmogram dpop incorporating a correction for low perfusion
url http://dx.doi.org/10.1155/2014/980149
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