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Cardiac time-area curve modelling using piecewise linear regression in mice with heart failure

Cardiac single-slice time-area curves (TAC) depict the left ventricle function at multiple time points throughout cardiac cycle. The character and number of observed TAC phases depends on several factors related to heart condition and experimental procedure. The aim of this work was to assess piecewise linear regression modelling (PLR) as a tool for the TAC shape analysis leading to the curve sectioning with estimation of the segments lengths and dynamics allowing the complex TAC parameterization. Cardiac Magnetic Resonance images of the atherosclerotic and control mice obtained at rest and under pharmacologically induced stress condition were considered. TAC from each mouse was modelled by partitioning time into intervals and fitting using straight lines, where position of intervals boundaries were optimized numerically. The best fit model was selected from several candidate models (with different numbers of segments) according to Akaike Information Criterion. PLR has been validated as the promising method for tracing qualitative and quantitative changes in TAC shapes showing different paths of stress response. The method is time saving, gives possibly objective results, extends analysis protocol and allows semi-automatic assessment of the TAC individual systolic and diastolic phases.