To the Editor:—
We read the excellent article by Patel and Souter1regarding equipment-related electrocardiographic artifacts. We wish to remind readers of Anesthesiology that the electrocardiographic monitor itself can also introduce artifacts, e.g. , ST-segment depression, in the example described below and documented in figure 1.
An asymptomatic 86-yr-old man was scheduled to undergo cataract extraction during monitored anesthesia care. Upon starting electrocardiographic monitoring, an approximately 2.0-mm ST-segment depression was noted (Philips Component Monitoring System, software version C.1; Bothell, WA). A strip chart recording “confirmed” the ST-segment depression (top trace, fig. 1). However, the patient denied chest pain and was asymptomatic; therefore, an artifact was suspected. Upon investigation, the electrocardiographic monitor was found to be set in filter mode. In diagnostic mode, there was no significant ST-segment depression (bottom trace, fig. 1).
Low-frequency filtering can stabilize baseline drift but can also distort ST segments.2A frequency response of 0.5–40 Hz is considered adequate for routine monitoring, but a frequency response of 0.05–100 Hz is needed for true diagnostic electrocardiographic interpretation.3The Philips filter mode has a 0.5-Hz filter cutoff, and the diagnostic mode uses a 0.05-Hz filter cutoff (personal verbal communication, John J. Wang, M.S., Principal Scientist, Philips Healthcare, Andover, MA, December 2005). The effect of the different electrocardiograph modes on baseline drift can also be seen in figure 1.
One clue that an apparent ST-segment depression may be an artifact is if a properly adjusted ST-segment analyzer displays a value inconsistent with the electrocardiographic trace. This is because the ST-segment module analyzes the less filtered diagnostic mode electrocardiograph; it can still be accurate even if the electrocardiographic monitor is set to filter mode (personal verbal communication, John J. Wang, M.S., December 2005).
Long Beach Veterans Affairs Medical Center, Long Beach, California, and University of California at Irvine, Orange, California. firstname.lastname@example.org