The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool

Revell, Andrew D.; Wang, Dechao; Boyd, Mark; Emery, Sean; Pozniak, Anton; Wolf, Frank de; Harrigan, Richard; Montaner, Julio S. G.; Lane, Clifford; Larder, Brendan A.

doi:10.1097/qad.0b013e328349a9c2

Cited by 25 publications

(27 citation statements)

References 14 publications

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“…We have trained computational models to predict virological response to treatment from genotype, viral load, CD4 cell count, and treatment history with 80% accuracy. 14,15 This compares favourably with a 50-70% predictive accuracy typically achieved with genotypic sensitivity scores. 15,16 This approach could be of particular clinical utility in resource-limited settings (RLS).…”

Section: Introductionsupporting

confidence: 51%

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The use of computational models to predict response to HIV therapy for clinical cases in Romania

Revell¹,

Ene²,

Duiculescu³

et al. 2012

GERMS

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Introduction A major challenge in Romania is the optimisation of antiretroviral therapy for the many HIV-infected adults with, on average, a decade of treatment experience. The RDI has developed computational models that predict virological response to therapy but these require a genotype, which is not routinely available in Romania. Moreover the models, which were trained without any Romanian data, have proved most accurate for patients from the healthcare settings that contributed the training data. Here we develop and test a novel model that does not require a genotype, with test data from Romania.Methods A random forest (RF) model was developed to predict the probability of the HIV viral load (VL) being reduced to <50 copies/ml following therapy change. The input variables were baseline VL, CD4 count, treatment history and time to follow-up. The model was developed with 3188 treatment changes episodes (TCEs) from North America, Western Europe and Australia. The model's predictions for 100 independent TCEs from the RDI database were compared to those of a model trained with the same data plus genotypes and then tested using 39 TCEs from Romania in terms of the area under the ROC curve (AUC).Results When tested with the 100 independent RDI TCEs, the AUC values for the models with and without genotypes were 0.88 and 0.86 respectively. For the 39 Romanian TCEs the AUC was 0.60. However, when 14 cases with viral loads that may have been between 50 and 400 copies were removed, the AUC increased to 0.83. Discussion Despite having been trained without data from Romania, the model predicted treatment responses in treatment-experienced Romanian patients with clade F virus accurately without the need for a genotype. The results suggest that this approach might be generalisable and useful in helping design optimal salvage regimens for treatment-experienced patients in countries with limited resources where genotyping is not always available.

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Section: Introductionsupporting

confidence: 51%

“…14,15 This compares favourably with a 50-70% predictive accuracy typically achieved with genotypic sensitivity scores. 15,16 This approach could be of particular clinical utility in resource-limited settings (RLS). However, the models described above require genotyping, which is not generally available in RLS.…”

Section: Introductionsupporting

confidence: 51%

The use of computational models to predict response to HIV therapy for clinical cases in Romania

Revell¹,

Ene²,

Duiculescu³

et al. 2012

GERMS

Self Cite

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show abstract

“…Both sets of models were significantly more accurate predictors of virological response to second‐line therapy comprising ritonavir‐boosted lopinavir plus two or three NtRTIs than genotyping with Stanford rules‐based interpretation. It is encouraging that the NG models were able to achieve this level of performance and this is consistent with the results of other studies . It should be acknowledged that treatment decisions are not made on the basis of a genotype with rules‐based interpretation alone; physicians include other information in their decisions, such as the genetic barrier associated with different inhibitors.…”

Section: Discussionsupporting

confidence: 84%

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

Revell¹,

Boyd

Wang³

et al. 2014

HIV Medicine

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ObjectivesWe 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. MethodsTwo 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. ResultsThe 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. ConclusionsThe 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.

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“…A group intervention targeting PLWH in China was able to increase social support (Yu et al, 2009). Other effective interventions include peer support groups (Cully et al, 2012) and online treatment support (Revell et al, 2011). It is also important to note that perceived social support may be as useful as enacted social support (Xia et al, 2012).…”

Section: Discussionmentioning

confidence: 97%

Prevalence and associated factors of depression among people living with HIV in two cities in China

Lau

Mak

et al. 2013

Journal of Affective Disorders

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The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool

Abstract: The models achieved a consistent, high level of accuracy in predicting treatment responses, which was markedly superior to that of genotypic sensitivity scores. The models are being used to power an experimental system now available via the Internet.

Cited by 25 publications

References 14 publications

The use of computational models to predict response to HIV therapy for clinical cases in Romania

The use of computational models to predict response to HIV therapy for clinical cases in Romania

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

Prevalence and associated factors of depression among people living with HIV in two cities in China

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