Error-checking intraoperative arterial line blood pressures

Error-checking intraoperative arterial line blood pressures Electronic medical records now store a wealth of intraoperative hemodynamic data. However, analysis of such data is plagued by artifacts related to the monitoring environment. Here, we present an algorithm for automated identic fi ation of artifacts and replacement using interpolation of arterial line blood pressures. After IRB approval, minute-by-minute digital recordings of systolic, diastolic, and mean arterial pressures (MAP) obtained during anesthesia care were analyzed using predetermined metrics to identify values anomalous from adjacent neighbors. Anomalous data points were then replaced with linear inter- polation of neighbors. The algorithm was then validated against manual artifact identification in 54 anesthesia records and 41,384 arterial line measurements. To assess the algorithm’s effect on data analysis, we calculated the percent of time spent with MAP below 55 mmHg and above 100 mmHg for both raw and conditioned datasets. Manual review of the dataset identified 1.23% of all pressure readings as artifactual. When compared to manual review, the algorithm identified artifacts with 87.0% sensitivity and 99.4% specificity. The average difference between manual review and algorithm in identifying the start of arterial line monitoring was 0.17, and 2.1 min for the end of monitoring. Application of the algorithm decreased the percent of time below 55 mmHg from http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Clinical Monitoring and Computing Springer Journals

Error-checking intraoperative arterial line blood pressures

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Medicine & Public Health; Anesthesiology; Intensive / Critical Care Medicine; Statistics for Life Sciences, Medicine, Health Sciences
ISSN
1387-1307
eISSN
1573-2614
D.O.I.
10.1007/s10877-018-0167-7
Publisher site
See Article on Publisher Site

Abstract

Electronic medical records now store a wealth of intraoperative hemodynamic data. However, analysis of such data is plagued by artifacts related to the monitoring environment. Here, we present an algorithm for automated identic fi ation of artifacts and replacement using interpolation of arterial line blood pressures. After IRB approval, minute-by-minute digital recordings of systolic, diastolic, and mean arterial pressures (MAP) obtained during anesthesia care were analyzed using predetermined metrics to identify values anomalous from adjacent neighbors. Anomalous data points were then replaced with linear inter- polation of neighbors. The algorithm was then validated against manual artifact identification in 54 anesthesia records and 41,384 arterial line measurements. To assess the algorithm’s effect on data analysis, we calculated the percent of time spent with MAP below 55 mmHg and above 100 mmHg for both raw and conditioned datasets. Manual review of the dataset identified 1.23% of all pressure readings as artifactual. When compared to manual review, the algorithm identified artifacts with 87.0% sensitivity and 99.4% specificity. The average difference between manual review and algorithm in identifying the start of arterial line monitoring was 0.17, and 2.1 min for the end of monitoring. Application of the algorithm decreased the percent of time below 55 mmHg from

Journal

Journal of Clinical Monitoring and ComputingSpringer Journals

Published: Jun 5, 2018

References

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