Salvatore P Costa1, Timothy A Beaver2, Joyce L Rollor2, Pantila Vanichakarn3, Patrick C Magnus2, Robert T Palac2. 1. Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire. Electronic address: salvatore.p.costa@hitchcock.org. 2. Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire. 3. Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire; Siriraj Hospital, Mahidol University, Bankok, Thailand.
Abstract
BACKGROUND: Global longitudinal strain (GLS) derived from two-dimensional speckle-tracking is an emerging technology, but lack of industry standards limits its application. Prior studies support using this tool to identify subclinical disease through serial changes, but the variability introduced by a change in vendor or reader is not well defined. METHODS: Fifty study subjects were prospectively identified to include four subgroups to ensure a broad range of GLS: normal (n = 20), left ventricular hypertrophy (n = 10), ST-segment elevation myocardial infarction (n = 10), and systolic heart failure (n = 10). Raw data were obtained using equipment from two vendors during the same session, and GLS was analyzed using an offline workstation. Intraobserver and interobserver variation was measured using correlation coefficients, intraclass correlation coefficients, and Bland-Altman plots. RESULTS: GLS measurements were highly reproducible by the same reader or a different reader using vendor 1 and vendor 2 or comparing vendors (correlation coefficients and intraclass correlation coefficients ≥ 0.95). However, the Bland-Altman plots suggested that the variation in repeat GLS measurements may range from ± 2% to ± 5% on the basis of a change in vendor, reader, or both. CONCLUSIONS: The expected variation in GLS measurements associated with a change in vendor, reader, or both should be considered when making conclusions about significant changes in serial measurements.
BACKGROUND: Global longitudinal strain (GLS) derived from two-dimensional speckle-tracking is an emerging technology, but lack of industry standards limits its application. Prior studies support using this tool to identify subclinical disease through serial changes, but the variability introduced by a change in vendor or reader is not well defined. METHODS: Fifty study subjects were prospectively identified to include four subgroups to ensure a broad range of GLS: normal (n = 20), left ventricular hypertrophy (n = 10), ST-segment elevation myocardial infarction (n = 10), and systolic heart failure (n = 10). Raw data were obtained using equipment from two vendors during the same session, and GLS was analyzed using an offline workstation. Intraobserver and interobserver variation was measured using correlation coefficients, intraclass correlation coefficients, and Bland-Altman plots. RESULTS: GLS measurements were highly reproducible by the same reader or a different reader using vendor 1 and vendor 2 or comparing vendors (correlation coefficients and intraclass correlation coefficients ≥ 0.95). However, the Bland-Altman plots suggested that the variation in repeat GLS measurements may range from ± 2% to ± 5% on the basis of a change in vendor, reader, or both. CONCLUSIONS: The expected variation in GLS measurements associated with a change in vendor, reader, or both should be considered when making conclusions about significant changes in serial measurements.
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