title: 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.
  
  
  RESULTS
  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.
authors: Department of Infection and Population Health, Division of Population Health, UCL Medical School, Royal Free Campus, London, United Kingdom. aclepri@ccbr.umn.edu
authors: Alessandro
authors: A
authors: Cozzi-Lepri
authors: Mattia C F
authors: MC
authors: Prosperi
authors: Jesper
authors: J
authors: Kjær
authors: David
authors: D
authors: Dunn
authors: Roger
authors: R
authors: Paredes
authors: Caroline A
authors: CA
authors: Sabin
authors: Jens D
authors: JD
authors: Lundgren
authors: Andrew N
authors: AN
authors: Phillips
authors: Deenan
authors: D
authors: Pillay
mesh_terms: D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D024882:Drug Resistance, Viral / Q000235:genetics*; D005838:Genotype; D006678:HIV / Q000187:drug effects / Q000235:genetics; D015658:HIV Infections / Q000188:drug therapy; D006282:Health Personnel; D006801:Humans; D020407:Internet; D016014:Linear Models; D061466:Lopinavir / Q000494:pharmacology* / Q000627:therapeutic use; D009154:Mutation
chemical_list: D061466:Lopinavir