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RESEARCH PRODUCT
A Prognostic Model for Estimating the Time to Virologic Failure in HIV-1 Infected Patients Undergoing a New Combination Antiretroviral Therapy Regimen
Pprosperi McDi Giambenedetto SFanti IMeini GBruzzone BCallegaro APenco GBagnarelli PMicheli VPaolini EDi Biagio AGhisetti VDi Pietro MZazzi MDe Luca AGiacometti AButini LDel Gobbo RMenzo STacconi DCorbelli GZanussi SMonno LPunzi GMaggiolo FLeonardo CalzaMaria Carla RePristerà RTurconi PMandas ATini SCarnevale GAmadio GSighinolfi LZuccati GMorfini MManetti RGalli LBartalesi FColao GTosti ASetti MTrezzi MOrani APardelli RDe Gennaro MChiodera AScalzini APalvarini LAlmi PTodaro GGianotti NCicconi PRusconi SGismondo MrBiondi MlCapetti AMeraviglia PBoeri EPecorari MMussini CSantirocchi MBrustia DRavanini PDal Bello FRomano NMancuso SCalzetti CMaserati RBaldanti FFrancisci DParruti GPolilli ESacchini DMartinelli CConsolini RVatteroni LVivarelli ANerli ALenzi LMagnani GOrtolani PAndreoni MPalamara GFimiani CPalmisano LAntinori AVullo VTurriziani OPerno CfMontano MCenderello GGonnelli ARomano LPalumbo MBonora SDelle Foglie PRossi CPoletti FMondino VMalena MLattuada E.subject
OncologyMaleAdult; Anti-HIV Agents; Cohort Studies; Drug Therapy Combination; Female; HIV Infections; HIV-1; Humans; Male; Middle Aged; Proportional Hazards Models; Treatment Failure; Viral LoadHIV InfectionsCohort Studies0302 clinical medicineANTIRETROVIRAL THERAPYMedicineHIV Infection030212 general & internal medicineTreatment Failure0303 health sciencesHealth PolicyMiddle AgedViral Load3. Good healthComputer Science ApplicationsCensoring (clinical trials)CohortCombinationlcsh:R858-859.7Drug Therapy CombinationFemaleViral loadHumanResearch ArticleCartAdultmedicine.medical_specialtyAnti-HIV AgentsHIV-1; antiretroviral therapyHealth InformaticsSettore MED/17 - MALATTIE INFETTIVElcsh:Computer applications to medicine. Medical informatics03 medical and health sciencesDrug TherapyInternal medicineHumansSurvival analysisProportional Hazards Models030306 microbiologybusiness.industryProportional hazards modelAdult; Anti-HIV Agents; Cohort Studies; Drug Therapy Combination; Female; HIV Infections; HIV-1; Humans; Male; Middle Aged; Proportional Hazards Models; Treatment Failure; Viral Load; Health Informatics; Health PolicyANTIRETROVIRAL DRUGSAnti-HIV AgentHIVGENOTYPESDiscontinuationRegimenImmunologyProportional Hazards ModelHIV-1Cohort Studiebusinessdescription
Abstract Background HIV-1 genotypic susceptibility scores (GSSs) were proven to be significant prognostic factors of fixed time-point virologic outcomes after combination antiretroviral therapy (cART) switch/initiation. However, their relative-hazard for the time to virologic failure has not been thoroughly investigated, and an expert system that is able to predict how long a new cART regimen will remain effective has never been designed. Methods We analyzed patients of the Italian ARCA cohort starting a new cART from 1999 onwards either after virologic failure or as treatment-naïve. The time to virologic failure was the endpoint, from the 90th day after treatment start, defined as the first HIV-1 RNA > 400 copies/ml, censoring at last available HIV-1 RNA before treatment discontinuation. We assessed the relative hazard/importance of GSSs according to distinct interpretation systems (Rega, ANRS and HIVdb) and other covariates by means of Cox regression and random survival forests (RSF). Prediction models were validated via the bootstrap and c-index measure. Results The dataset included 2337 regimens from 2182 patients, of which 733 were previously treatment-naïve. We observed 1067 virologic failures over 2820 persons-years. Multivariable analysis revealed that low GSSs of cART were independently associated with the hazard of a virologic failure, along with several other covariates. Evaluation of predictive performance yielded a modest ability of the Cox regression to predict the virologic endpoint (c-index≈0.70), while RSF showed a better performance (c-index≈0.73, p Conclusions GSSs of cART and several other covariates were investigated using linear and non-linear survival analysis. RSF models are a promising approach for the development of a reliable system that predicts time to virologic failure better than Cox regression. Such models might represent a significant improvement over the current methods for monitoring and optimization of cART.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-06-01 | BMC Medical Informatics and Decision Making |