Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score

Abstract

Background: The question of whether a score for a specific antiretroviral (e.g. lopinavir/r in this analysis) that improves prediction of viral load response given by existing expert-based interpretation systems (IS) could be derived from analyzing the correlation between genotypic data and virological response using statistical methods remains largely unanswered.

Methods and findings: We used the data of the patients from the UK Collaborative HIV Cohort (UK CHIC) Study for whom genotypic data were stored in the UK HIV Drug Resistance Database (UK HDRD) to construct a training/validation dataset of treatment change episodes (TCE). We used the average square error (ASE) on a 10-fold cross-validation and on a test dataset (the EuroSIDA TCE database) to compare the performance of a newly derived lopinavir/r score with that of the 3 most widely used expert-based interpretation rules (ANRS, HIVDB and Rega). Our analysis identified mutations V82A, I54V, K20I and I62V, which were associated with reduced viral response and mutations I15V and V91S which determined lopinavir/r hypersensitivity. All models performed equally well (ASE on test ranging between 1.1 and 1.3, p = 0.34).

Conclusions: We fully explored the potential of linear regression to construct a simple predictive model for lopinavir/r-based TCE. Although, the performance of our proposed score was similar to that of already existing IS, previously unrecognized lopinavir/r-associated mutations were identified. The analysis illustrates an approach of validation of expert-based IS that could be used in the future for other antiretrovirals and in other settings outside HIV research.

Conflict of interest statement

Competing Interests: No member of the The United Kingdom Collaborative Group on HIV Drug Resistance, United Kingdom CHIC Study Group and EuroSIDA has any financial or personal relationships with people or organizations that could inappropriately influence this work, although most members of the group have, at some stage in the past, received funding from a variety of pharmaceutical companies for research, travel grants, speaking engagements or consultancy fees. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Figure 1. Description of a lopinavir-based TCE.

Figure 2. Plot of the standardized coefficients of all the factors selected at each step (from step 1 to final step 7) of the best subset (LSE) method are plotted as a function of the step number.

This enables to assess the relative importance of each factor selected at any step of the selection process as well as provides information as to when effects entered the model. The lower plot in the panel shows how CV PRESS (the criterion used to choose the selected model) changes as factors enter or leave the model. Selection was halted at step 7 when the “one-standard error” rule was achieved.