The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology
Abstract Background Mean arterial pressure (MAP), bispectral index (BIS), and minimum alveolar concentration (MAC) represent valuable, yet dynamic intraoperative monitoring variables. They provide information related to poor outcomes when considered together, however their collective behavior across...
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doaj-26ee53f2e4d642ac86736735557dad0f2020-11-25T01:23:32ZengBMCBMC Medical Research Methodology1471-22882019-01-0119111410.1186/s12874-019-0660-9The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiologyMichael P. Schnetz0Harry S. Hochheiser1David J. Danks2Douglas P. Landsittel3Keith M. Vogt4James W. Ibinson5Steven L. Whitehurst6Sean P. McDermott7Melissa Giraldo Duque8Ata M. Kaynar9Department of Anesthesiology, University of PittsburghDepartment of Biomedical Informatics, University of PittsburghDepartments of Philosophy and Psychology, Carnegie Mellon UniversityDepartment of Biomedical Informatics, University of PittsburghDepartment of Anesthesiology, University of PittsburghDepartment of Anesthesiology, University of PittsburghDepartment of Anesthesiology, University of PittsburghDepartment of Anesthesiology, University of PittsburghDepartment of Anesthesiology, University of PittsburghDepartment of Anesthesiology, University of PittsburghAbstract Background Mean arterial pressure (MAP), bispectral index (BIS), and minimum alveolar concentration (MAC) represent valuable, yet dynamic intraoperative monitoring variables. They provide information related to poor outcomes when considered together, however their collective behavior across time has not been characterized. Methods We have developed the Triple Variable Index (TVI), a composite variable representing the sum of z-scores from MAP, BIS, and MAC values that occur together during surgery. We generated a TVI expression profile, defined as the sequential TVI values expressed across time, for each surgery where concurrent MAP, BIS, and MAC monitoring occurred in an adult patient (≥18 years) at the University of Pittsburgh Medical Center between January and July 2014 (n = 5296). Patterns of TVI expression were identified using k-means clustering and compared across numerous patient, procedure, and outcome characteristics. TVI and the triple low state were compared as prediction models for 30-day postoperative mortality. Results The median frequency MAP, BIS, and MAC were recorded was one measurement every 3, 5, and 5 min. Three expression patterns were identified: elevated, mixed, and depressed. The elevated pattern displayed the highest average MAP, BIS, and MAC values (86.5 mmHg, 45.3, and 0.98, respectively), while the depressed pattern displayed the lowest values (76.6 mmHg, 38.0, 0.66). Patterns (elevated, mixed, depressed) were distinct across the following characteristics: average patient age (52, 53, 54 years), American Society of Anesthesiologists Physical Status 4 (6.7, 16.1, 27.3%) and 5 (0.1, 0.6, 1.6%) categories, cardiac (2.2, 6.5, 16.1%) and emergent (5.8, 10.5, 12.8%) surgery, cardiopulmonary bypass use (0.3, 2.6, 9.8%), intraoperative medication administration including etomidate (3.0, 7.3, 12.6%), hydromorphone (47.6, 26.3, 25.2%), ketamine (11.2, 4.6, 3.0%), dexmedetomidine (18.4, 16.6, 13.6%), phenylephrine (74.0, 74.8, 83.0), epinephrine (2.0, 6.0, 18.0%), norepinephrine (2.4, 7.5, 21.2%), vasopressin (3.4, 7.6, 21.0%), succinylcholine (74.0, 69.0, 61.9%), intraoperative hypotension (28.8, 33.0, 52.3%) and the triple low state (9.4, 30.3, 80.0%) exposure, and 30-day postoperative mortality (0.8, 2.7, 5.6%). TVI was a better predictor of patients that died or survived in the 30 days following surgery compared to cumulative triple low state exposure (AUC 0.68 versus 0.62, p < 0.05). Conclusions Surgeries that share similar patterns of TVI expression display distinct patient, procedure, and outcome characteristics.http://link.springer.com/article/10.1186/s12874-019-0660-9Triple variable indexTriple low stateMean arterial pressureBispectral indexMinimum alveolar concentrationK-means clustering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Michael P. Schnetz Harry S. Hochheiser David J. Danks Douglas P. Landsittel Keith M. Vogt James W. Ibinson Steven L. Whitehurst Sean P. McDermott Melissa Giraldo Duque Ata M. Kaynar |
spellingShingle |
Michael P. Schnetz Harry S. Hochheiser David J. Danks Douglas P. Landsittel Keith M. Vogt James W. Ibinson Steven L. Whitehurst Sean P. McDermott Melissa Giraldo Duque Ata M. Kaynar The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology BMC Medical Research Methodology Triple variable index Triple low state Mean arterial pressure Bispectral index Minimum alveolar concentration K-means clustering |
author_facet |
Michael P. Schnetz Harry S. Hochheiser David J. Danks Douglas P. Landsittel Keith M. Vogt James W. Ibinson Steven L. Whitehurst Sean P. McDermott Melissa Giraldo Duque Ata M. Kaynar |
author_sort |
Michael P. Schnetz |
title |
The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology |
title_short |
The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology |
title_full |
The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology |
title_fullStr |
The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology |
title_full_unstemmed |
The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology |
title_sort |
triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology |
publisher |
BMC |
series |
BMC Medical Research Methodology |
issn |
1471-2288 |
publishDate |
2019-01-01 |
description |
Abstract Background Mean arterial pressure (MAP), bispectral index (BIS), and minimum alveolar concentration (MAC) represent valuable, yet dynamic intraoperative monitoring variables. They provide information related to poor outcomes when considered together, however their collective behavior across time has not been characterized. Methods We have developed the Triple Variable Index (TVI), a composite variable representing the sum of z-scores from MAP, BIS, and MAC values that occur together during surgery. We generated a TVI expression profile, defined as the sequential TVI values expressed across time, for each surgery where concurrent MAP, BIS, and MAC monitoring occurred in an adult patient (≥18 years) at the University of Pittsburgh Medical Center between January and July 2014 (n = 5296). Patterns of TVI expression were identified using k-means clustering and compared across numerous patient, procedure, and outcome characteristics. TVI and the triple low state were compared as prediction models for 30-day postoperative mortality. Results The median frequency MAP, BIS, and MAC were recorded was one measurement every 3, 5, and 5 min. Three expression patterns were identified: elevated, mixed, and depressed. The elevated pattern displayed the highest average MAP, BIS, and MAC values (86.5 mmHg, 45.3, and 0.98, respectively), while the depressed pattern displayed the lowest values (76.6 mmHg, 38.0, 0.66). Patterns (elevated, mixed, depressed) were distinct across the following characteristics: average patient age (52, 53, 54 years), American Society of Anesthesiologists Physical Status 4 (6.7, 16.1, 27.3%) and 5 (0.1, 0.6, 1.6%) categories, cardiac (2.2, 6.5, 16.1%) and emergent (5.8, 10.5, 12.8%) surgery, cardiopulmonary bypass use (0.3, 2.6, 9.8%), intraoperative medication administration including etomidate (3.0, 7.3, 12.6%), hydromorphone (47.6, 26.3, 25.2%), ketamine (11.2, 4.6, 3.0%), dexmedetomidine (18.4, 16.6, 13.6%), phenylephrine (74.0, 74.8, 83.0), epinephrine (2.0, 6.0, 18.0%), norepinephrine (2.4, 7.5, 21.2%), vasopressin (3.4, 7.6, 21.0%), succinylcholine (74.0, 69.0, 61.9%), intraoperative hypotension (28.8, 33.0, 52.3%) and the triple low state (9.4, 30.3, 80.0%) exposure, and 30-day postoperative mortality (0.8, 2.7, 5.6%). TVI was a better predictor of patients that died or survived in the 30 days following surgery compared to cumulative triple low state exposure (AUC 0.68 versus 0.62, p < 0.05). Conclusions Surgeries that share similar patterns of TVI expression display distinct patient, procedure, and outcome characteristics. |
topic |
Triple variable index Triple low state Mean arterial pressure Bispectral index Minimum alveolar concentration K-means clustering |
url |
http://link.springer.com/article/10.1186/s12874-019-0660-9 |
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