Poster presentation at:
12th International Workshop on HIV Drug Resistance
Cabo, Mexico; 10-13 June 2003
A Neural Network Model Using Clinical Cohort Data Accurately
Predicts Virological Response and Identifies Regimens with Increased
Probability of Success in Treatment Failures.
D. Wang, B.A. Larder, A. Revell, R. Harrigan, J. Montaner.
On behalf of the HIV Resistance Response Database Initiative (RDI).
Background: We have previously demonstrated the utility
of neural networks (NN) in
predicting viral load (VL) response using a clinical trial dataset.
Here we compare the reliability of using ‘real life’ clinical
cohort data to model treatment response.
Methods: 351 patients from the BC HIV treatment cohort
were analysed. Each had multiple genotypes and irregular VL measurements.
‘Treatment change episodes’ (TCEs) were selected from patients
having a genotype up to 12 weeks before treatment change and baseline
VL up to 16, 12 or 8 weeks before treatment change. Follow-up VL was
within 4-40 weeks after treatment change. 652, 602 and 539 TCEs respectively,
obtained using the 3 VL windows, were used for NN training (10% of each
was partitioned for independent testing). Results were compared to those
previously obtained using clinical trial data comprising 700 TCEs, with
baseline VL and genotype at Week 0 (time of treatment change), and follow-up
VL at 8, 16, or 24 weeks post treatment change.
Results: The correlation between the predicted and
actual VL change for the 16-, 12- and 8-week baseline VL models gave
the following values: 0.42, 0.45 and 0.55 respectively. The model
with the narrowest baseline VL window (8 weeks) most accurately predicted
VL response. The value for this model was significantly different
to those for the other models (p<0.05) and also to that of a previous
model derived using clinical trial data ( =0.50, p<0.05). The NN
models were also used to predict VL trajectory. The rates of correct
trajectory prediction were: 74%, 72% and 87% (16-, 12- and 8-week models
respectively). Again, the 8-week model was significantly more accurate
than the two other models (p<0.05) and comparable to that obtained
using the clinical trial model (75%, ns). We utilized the 8-week baseline
VL model to evaluate alternative regimens in patients who had experienced
treatment failure. The model was used to predict response to 30 common
HAART regimens for each of 118 TCEs where VL increased after a change
to a regimen of =3 antiretroviral drugs (3,540 evaluations). In 116
cases (98%), one or more regimens were identified that the model predicted
would cause a reduction in VL. This could translate into an additional
101 cases where patients would have benefited from treatment selected
using this model (based on the 87% accuracy of trajectory prediction
Conclusions: NN modeling using the narrowest baseline
VL window gave the most accurate prediction of VL response, underlining
the importance of the quality of the dataset. This model also showed
superior predictive accuracy compared to data obtained from a NN model
trained with clinical trial data, establishing the feasibility of using
treatment cohort data to model VL response. Furthermore, the model identified
potentially beneficial treatment regimens in almost all patients who
experienced treatment failure, demonstrating the utility of using this
approach to perform evaluations of multiple treatment permutations.