• Charles Huanghong Du, David Glick, and Avery Tung.
• Pritzker School of Medicine, Chicago, IL, USA.
• J Clin Monit Comput. 2019 Jun 1; 33 (3): 407-412.

AbstractElectronic 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 identification 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 interpolation 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 4.3 to 2.0% (2.1% with manual review) and time above 100 mmHg from 8.8 to 7.3% (7.3% manual). This algorithm's performance was comparable to manual review by a human anesthesiologist and reduced the incidence of abnormal MAP values identified using a sample analysis tool.

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