Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes

• Published in: Antiviral therapy 2004-08, Vol.9 (4), p.583-593
• Main Authors: DE LUCA, Andrea, VENDITTELLI, Marilena, ULIVI, Giovanni, BALDINI, Francesco, DI GIAMBENEDETTO, Simona, TROTTA, Maria Paola, CINGOLANI, Antonella, BACARELLI, Alessandra, GORI, Caterina, PERNO, Carlo Federico, ANTINORI, Andrea
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Algorithms; Anti-Retroviral Agents - therapeutic use; Antibiotics. Antiinfectious agents. Antiparasitic agents; Antiretroviral Therapy, Highly Active; Antiviral agents; Biological and medical sciences; Clinical Trials as Topic; Databases, Genetic; Drug Resistance, Viral - genetics; Drug Therapy, Combination; Female; Fuzzy Logic; Genotype; HIV Infections - drug therapy; HIV Infections - virology; HIV Protease - genetics; HIV Reverse Transcriptase - genetics; HIV-1 - drug effects; HIV-1 - genetics; HIV-1 - isolation & purification; Humans; Italy; Male; Medical sciences; Middle Aged; Mutation; Pharmacology. Drug treatments; Reproducibility of Results; Salvage Therapy
• ISSN: 1359-6535; 2040-2058
• DOI: 10.1177/135965350400900406

To evaluate whether fuzzy operators can be usefully applied to the interpretation of genotypic HIV-1 drug resistance by experts, and to improve the prediction of salvage therapy outcome by adapting interpretation rules of genotypic resistance on the basis of their association with virological response data. We used a clinical dataset of 231 patients failing highly active antiretroviral therapy (HAART) and starting salvage therapy with baseline resistance genotyping and virological outcomes after 3 and 6 months. A set of rules predicting genotypic resistance was initially derived from an expert (ADL). Rules were implemented using a fuzzy logic approach and the virological outcomes dataset used for the training phase. The resulting algorithm was validated using a separate set of 184 selected patients by correlating the resulting predicted activity with observed virological response at 3 months. For comparison, the expert systems from the drug resistance group of the Agence Nationale de Recherches sur le SIDA (ANRS-AC11) and the algorithm from the Stanford's HIV drug resistance database (Stanford HIVdb) were evaluated on the same set. The starting algorithm had a correlation with virological outcomes of R2=0.06 (P=0.0001). After the training phase the correlation with virological outcomes increased to R2=0.19 (P