A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy.

OBJECTIVES: We compared the use of computational models developed with and without HIV genotype vs. genotyping itself to predict effective regimens for patients experiencing first-line virological failure. METHODS: Two sets of models predicted virological response for 99 three-drug regimens for patients on a failing regimen of two nucleoside/nucleotide reverse transcriptase inhibitors and one nonnucleoside reverse transcriptase inhibitor in the Second-Line study. One set used viral load, CD4 count, genotype, plus treatment history and time to follow-up to make its predictions; the second set did not include genotype. Genotypic sensitivity scores were derived and the ranking of the alternative regimens compared with those of the models. The accuracy of the models and that of genotyping as predictors of the virological responses to second-line regimens were compared. RESULTS: The rankings of alternative regimens by the two sets of models were significantly correlated in 60-69% of cases, and the rankings by the models that use a genotype and genotyping itself were significantly correlated in 60% of cases. The two sets of models identified alternative regimens that were predicted to be effective in 97% and 100% of cases, respectively. The area under the receiver-operating curve was 0.72 and 0.74 for the two sets of models, respectively, and significantly lower at 0.55 for genotyping. CONCLUSIONS: The two sets of models performed comparably well and significantly outperformed genotyping as predictors of response. The models identified alternative regimens predicted to be effective in almost all cases. It is encouraging that models that do not require a genotype were able to predict responses to common second-line therapies in settings where genotyping is unavailable.

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

OBJECTIVES: Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs. METHODS: Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure. RESULTS: The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania. CONCLUSIONS: We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings.

In the absence of widespread access to individualized laboratory monitoring, which forms an integral part of HIV patient management in resource-rich settings, the roll-out of highly active antiretroviral therapy (HAART) in resource-limited settings has adopted a public health approach based on standard HAART protocols and clinical/immunological definitions of therapy failure. The cost-effectiveness of HIV-1 viral load monitoring at the individual level in such settings has been debated, and questions remain over the long-term and population-level impact of managing HAART without it. Computational models that accurately predict virological response to HAART using baseline data including CD4 count, viral load and genotypic resistance profile, as developed by the Resistance Database Initiative, have significant potential as an aid to treatment selection and optimization. Recently developed models have shown good predictive performance without the need for genotypic data, with viral load emerging as by far the most important variable. This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings.

Smoking and performance--a puff-by-puff analysis.

A new version of the Rapid Visual Information Processing (RVIP) task has been developed in this laboratory to examine the effects of each puff from a cigarette upon continuous performance. Two cigarettes were tested and compared with a not-smoking and a sham-smoking control. The results show improvements resulting from just two puffs both in terms of correct detections and reaction times. Both measures of performance were maintained at a higher level during and after smoking, compared with not-smoking and sham-smoking.

Smoking as a coping strategy.

People in modern society use an ever increasing variety of psychoactive substances to help them cope with the increasing amount of stress they experience in the course of their lives. A survey was undertaken to investigate the role of smoking as a coping strategy and the relationship between smoking, the other coping strategies available, and personality in an undergraduate student population. There seemed to be three distinct subgroups in this population as defined by their preferred coping strategy: those who seek help from their friends when faced by problems, those who seek "expert advice," and those who attempt to solve their problems alone; often with the use of drugs. Those falling in the third category, that of self-help, were more likely to self-medicate with a wide variety of psychoactive substances. If they were smokers then they smoked more cigarettes and chose their brand on the basis of strength of a cigarette. There was no evidence for different personality types tending to smoke in different situations and no evidence for any link between extraversion or neuroticism and substance use or coping.

Smokers of the future.

Teenagers continue to be initiated into smoking, especially young women. A predictor of smoking is the existence of stressors in their life. The major components of smoking motivation have been found to be similar in young smokers to those found in older smokers, except for the appearance of a group who are resisting the pressures against smoking. There is no evidence that the smokers of the future will be more extreme personality types than the smokers of 10 or more years ago. Changes in demographic characteristics of smokers in the United Kingdom and the United States in favour of relatively more women and more smokers from the poorer socio-economic groups requires investigation into whether this is attributable to different coping requirements in these groups.