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Table 3 Regression analysis to predict viral load and CD4 count

From: Treatment-associated polymorphisms in protease are significantly associated with higher viral load and lower CD4 count in newly diagnosed drug-naive HIV-1 infected patients

  Model 1 Model 2
Factors log VL (95% CI) p-value log VL (95% CI) p-value
TDR PI -0.073 (-0.287 – 0.140) 0.500 -0.080 (-0.290 – 0.129) 0.451
TDR RTI -0.009 (-0.157 – 0.138) 0.903 -0.003 (-0.147 – 0.142) 0.971
log10F PI 0.271 (0.122 – 0.420) 0.000 0.251 (0.104 – 0.398) 0.001
log10F RTI -0.090 (-0.324 – 0.144) 0.449 -0.133 (-0.363 – 0.097) 0.258
log10FPIm 0.314 (0.156 – 0.472) 0.000 0.294 (0.137 – 0.450) 0.000
log10FRTIm -0.117 (-0.459 – 0.223) 0.498 -0.180 (-0.516 – 0.155) 0.292
Factors sqrt CD4 (95% CI) p-value sqrt CD4 (95% CI) p-value
TDR PI -0.357 (-2.313 – 1.600) 0.721 -0.501 (-2.417 – 1.416) 0.609
TDR RTI 0.507 ( -0.844 – 1.859) 0.462 0.362 (-0.963 – 1.688) 0.592
log10F PI -1.628 (-2.999 – 0.257) 0.020 -1.540 (-2.891 – -0.189) 0.025
log10F RTI 1.465 (-0.683 – 3.613) 0.181 1.583 (-0.527 – 3.692) 0.141
log10FPIm -1.861 (-3.314 – -0.407) 0.012 -1.779 (-3.213 – -0.346) 0.015
log10FRTIm 1.400 (-1.733 – 4.532) 0.381 1.356 (-1.727 – 4.439) 0.389
  1. Linear regression analyses of the association between genotypic predictors and clinical parameters. Viral load values were log transformed and CD4 counts were square root transformed to approximate the normal distribution. For each of the genotypic predictors, two models including different sets of potential confounders were investigated. Model 1 included genotypic predictors for PR and RT, and estimated duration of infection (recent vs unknown duration). Model 2 additionally included age, gender, risk group and area of origin.