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. 2022 May 16;87:e263–e270. doi: 10.5114/pjr.2022.116548

Table 3.

Multivariate regression models of global strains predicting total enhanced mass

Adjusted R2 AIC AICC BIC Fitness (df) p-value
Model 1 GCS 0.1830 476.60 477.11 480.47 12.20 (1.49) 0.001
GCS
GLS
0.1972 476.66 477.53 482.45 7.14 (2.48) 0.001
GCS
GLS
GRS
0.1892 478.09 479.42 485.82 4.89 (3.47) 0.004
Model 2 GCS 0.1830 476.60 477.11 480.47 12.20 (1.49) 0.001
GCS
GCS R systolic
0.1686 478.44 479.31 484.24 6.07 (2.48) 0.004
GCS
GCS R systolic
GCS R diastolic
0.1516 480.40 481.73 488.13 3.98 (3.47) 0.013
Model 3 GCS
LV EDVI
0.2392 437.91 474.78 479.71 8.86 (2.48) 0.005
GCS
GLS
GRS
LV CI
LV EF
LV ESVI
LV EDVI
0.2638 475.51 478.12 478.11 3.99 (6.44) 0.002

AIC – Akaike information criterion, BIC – Bayesian information criterion, AICC – AIC for small sample size, GLS – global longitudinal strain, GCS – global circumferential strain, GRS – global radial strain, R – rate, LV – left ventricular, EDVI – end-diastolic volume index, CI – cardiac index, EF – ejection fraction, ESVI – end-systolic volume index