Retrovirology

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
log₁₀F_PI−m	0.314 (0.156 – 0.472)	0.000	0.294 (0.137 – 0.450)	0.000
log₁₀F_RTI−m	-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
log₁₀F_PI−m	-1.861 (-3.314 – -0.407)	0.012	-1.779 (-3.213 – -0.346)	0.015
log₁₀F_RTI−m	1.400 (-1.733 – 4.532)	0.381	1.356 (-1.727 – 4.439)	0.389

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.

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ISSN: 1742-4690

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