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We sought to derive a predictive model for LV LS, using machine learning techniques with the hope that we may uncover surrogates for LV LS. We found that left ventricular ejection fraction, tricuspid annular plane systolic excursion, sepsis source, height, mitral valve Tei index, LV systolic dimension, aortic valve ejection time, and peak acceleration rate were all predictive of LV LS in this initial exploratory model. 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