Table 4.
Multivariable analysis for the ΔEcc and “definite” myocardial functional deterioration with CRP.
| Linear Regression | Logistic Regression | Linear Regression (after excluding subjects with interim events) | |||||||
|---|---|---|---|---|---|---|---|---|---|
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|
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| B* | 95% CI | P | OR | 95% CI | P | B* | 95% CI | P | |
|
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| Model 1 | 0.081 | .036, .126 | <0.001 | 1.074 | 1.035, 1.114 | <0.001 | 0.084 | .039, .129 | <0.001 |
| Model 2 | 0.099 | .052, .145 | <0.001 | 1.092 | 1.046, 1.139 | <0.001 | 0.100 | .054, .147 | <0.001 |
| Model 3 | 0.098 | .049, .147 | <0.001 | 1.102 | 1.051, 1.156 | <0.001 | 0.102 | .053, .152 | <0.001 |
| Model 4 | 0.061 | .022, .099 | 0.002 | 1.078 | 1.026, 1.132 | 0.003 | 0.061 | .023, .100 | 0.002 |
*
The differences in ΔEcc (%) per 1 mg/L change in CRP.
Model 1: age, gender, and ethnicity as covariates
Model 2 : Model 1 + systolic blood pressure, heart rate, diabetes, smoking status, body mass index, current medications and estimated glomerular filtration rate
Model 3 : Model 2 + LV mass, presence of coronary calcium , interim myocardial infarction or coronary revascularization
Model 4 : Model 3 + baseline Ecc