Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study
Abstract Background Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored...
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doaj-6d59871fc51b4f7e9e611e6d724407b42020-11-25T02:47:52ZengBMCBMC Anesthesiology1471-22532020-03-0120111210.1186/s12871-020-00972-5Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective studySenthil Packiasabapathy0Varesh Prasad1Valluvan Rangasamy2David Popok3Xinling Xu4Victor Novack5Balachundhar Subramaniam6Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical SchoolHarvard-Massachusetts Institute of Technology Program in Health Sciences and TechnologyDepartment of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical SchoolClinical Research Center, Soroka University Medical Center and Faculty of Health Sciences, Ben-Gurion University of the NegevDepartment of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical SchoolClinical Research Center, Soroka University Medical Center and Faculty of Health Sciences, Ben-Gurion University of the NegevDepartment of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical SchoolAbstract Background Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored the ability of Poincaré plots and coefficient of variation (CV) by measuring intraoperative BPV in predicting adverse outcomes. Methods In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability. Results Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7]. Conclusions In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.http://link.springer.com/article/10.1186/s12871-020-00972-5BP variabilityPoincaré plotCoefficient of variationCardiac surgerySTS risk score |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Senthil Packiasabapathy Varesh Prasad Valluvan Rangasamy David Popok Xinling Xu Victor Novack Balachundhar Subramaniam |
spellingShingle |
Senthil Packiasabapathy Varesh Prasad Valluvan Rangasamy David Popok Xinling Xu Victor Novack Balachundhar Subramaniam Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study BMC Anesthesiology BP variability Poincaré plot Coefficient of variation Cardiac surgery STS risk score |
author_facet |
Senthil Packiasabapathy Varesh Prasad Valluvan Rangasamy David Popok Xinling Xu Victor Novack Balachundhar Subramaniam |
author_sort |
Senthil Packiasabapathy |
title |
Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study |
title_short |
Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study |
title_full |
Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study |
title_fullStr |
Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study |
title_full_unstemmed |
Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study |
title_sort |
cardiac surgical outcome prediction by blood pressure variability indices poincaré plot and coefficient of variation: a retrospective study |
publisher |
BMC |
series |
BMC Anesthesiology |
issn |
1471-2253 |
publishDate |
2020-03-01 |
description |
Abstract Background Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored the ability of Poincaré plots and coefficient of variation (CV) by measuring intraoperative BPV in predicting adverse outcomes. Methods In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability. Results Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7]. Conclusions In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly. |
topic |
BP variability Poincaré plot Coefficient of variation Cardiac surgery STS risk score |
url |
http://link.springer.com/article/10.1186/s12871-020-00972-5 |
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