For each TCE we recorded: all drugs initiated together with LPV/r, all drugs currently received as part of cART, the viral load and the results of a genotypic resistance test performed in the 6 months preceding the initiation of LPV/r and a follow-up viral load measured over the first 4 months from starting LPV/r. When multiple baseline or follow up data were available, the value closest to month 3 after initiation of LPV/r was used. Because only a minority of patients contributed more than one TCE no attempt in the analysis was made to correct for violation of MEDChem Express 17650-98-5 independence of individual observations. TCE including drugs not belonging to the 3 original major drug classes were not considered because only RT and PR regions of HIV were sequenced. Using the results of the genotypic tests we could generate a genotypic susceptibility score for all antiretrovirals started together with LPV/r using the rules of ANRS IS . We also derived the lopinavir predictions using the 3 most common expert-opinion base IS. To make predictions of Stanford comparable to the other 2 webbased IS we grouped “potential low-level resistance”with “susceptible”and “low-level resistance”with “intermediate”. For this analysis, the UK CHIC/UK HDRD databases provided the training/validation datasets and EuroSIDA provided the test dataset. November 2011 | Volume 6 | Issue 11 | e25665 Derivation of a LPV/r Score with Linear Regression Statistical analysis The main characteristics of the TCE were described after stratification by cohort study. Because it is generally impossible to know, a priori, which off-the-shelf machine learning/statistical approaches will perform best for a given prediction problem and data set, we used a standard linear regression model with interaction terms for simplicity. The outcome was the change in viral load from pre-TCE to post-TCE levels on the log10 scale. For patients whose viral load decreased to undetectable levels we used the naive approach of replacing the unobserved undetectable value “ 21526763 with the limit of detection of the assay used. In sensitivity analyses, we instead replaced the unobserved undetectable value with K of 
the limit or with the fixed value of 20 copies/mL. The basic set of covariates and the ANRS predictions for the other drugs started besides LPV/r were considered in all regression models but were not forced into a model unless they were found ” to improve the prediction performance according to the specific selection criterion used. We then constructed 4 separate models including: i) the LPV/r Rega GSS, ii) the LPV/r ANRS GSS, and iii) the LPV/r Stanford GSS. All web-based GSS were fitted as categorical variables with “susceptible”as the reference group. Model iv) included individual PI mutations and 2-way interactions between these rather than a specific susceptibility score. Candidate PI mutations to be included were those reported as being a major mutation with non-zero prevalence associated with PI-resistance in either the IAS-USA December 2010 list or any of the considered web-based IS and minor mutations which were detected with a prevalence.5%. Three different criteria for the selection of mutations and 2-way interactions between mutation terms were used -best subset least squared estimations, least absolute shrinkage and selection operator and a hybrid version of least angle regression and LASSO. The hybrid method is a modification of the LAR originally proposed by Efron et al.. In this approach, the sequence of mo