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

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.