Optimized Arterial Line Artifact Identification Algorithm Cleans High-Frequency Arterial Line Data With High Accuracy in Critically Ill Patients

Abstract

OBJECTIVES:. High-frequency data streams of vital signs may be used to generate individualized hemodynamic targets for critically ill patients. Central to this precision medicine approach to resuscitation is our ability to screen these data streams for errors and artifacts. However, there is no consensus on the best method for data cleaning. Our goal was to determine whether an error-checking algorithm developed for intraoperative use could be applied to high volumes of arterial line data in an ICU population. DESIGN:. Multicenter observational study. SETTING:. ICUs across Ontario, Canada. PATIENTS:. Nested cohort of ICU patients with shock and/or respiratory failure requiring invasive mechanical ventilation. INTERVENTIONS:. High-frequency blood pressure data was analyzed. Systolic, diastolic, and mean arterial pressure minute averages were calculated. For manual analysis, a trained researcher retrospectively reviewed mean arterial pressure data, removing values that were deemed nonphysiological. The algorithm was implemented and identified artifactual data. MEASUREMENTS AND MAIN RESULTS:. Arterial line data was extracted from 15 patients. A trained researcher manually reviewed 40,798 minute-by-minute data points, then subsequently analyzed them with the algorithm. Manual review resulted in the identification of 119 artifacts (0.29%). The optimized algorithm identified 116 (97%) of these artifacts. Five hundred thirty-seven data points were erroneously removed or modified. Compared with manual review, the modified algorithm incorporating absolute thresholds of greater than 30 and less than 200 mm Hg had 97.5% sensitivity, 98.7% specificity, and a Matthew correlation coefficient of 0.41. CONCLUSIONS:. The error-checking algorithm had high sensitivity and specificity in detecting arterial line blood pressure artifacts compared with manual data cleaning. Given the growing use of large datasets and machine learning in critical care research, methods to validate the quality of high-frequency data is important to optimize algorithm performance and prevent spurious associations based on artifactual data.