Dataset Information

Pharmacogenomic study on anti-VEGF medicine in treatment of macular Neovascular diseases: a study protocol for a prospective observational study.

ABSTRACT: Macular neovascular diseases can cause severe vision loss. A newly approved anti-VEGF drug Conbercept has shown good efficacy and safety in rigorous random controlled trials (RCT), however, it cannot fully reflect the clinical application of Conbercept in real world clinical practice. Moreover, anti-VEGF drugs are expensive and often require multiple treatments, and some patients have poor or even no response to the drugs,this resulted enormous waste of medical resources. Therefore, how to find out those patients who have good response, and how to develop individualized therapeutic regimen in real world need to be urgently investigated in the aspect of pharmacogenomics and pharmacometabolomics.This study is a multicenter, prospective, observational study of Conbecept treating macular neovascular diseases in China. Patients suffered from age-related macular degeneration, polypoidal choroidal vasculopathy, and pathological myopia who already planned to receive Conbercept treatment will be recruited. We aimed to enroll more than 5000 patients from 43 ophthalmic centers in China. Patients' clinical data and blood samples will be collected during the one-year follow-up period. Finally, the safety and efficacy of Conbercept, and the potential predictors of patients' response to Conbercept will be investigated by pharmacogenomics and pharmacometabolomics analysis.This study will provide important data of Conbercept in treating macular neovascular diseases in real world. Besides, finding the predictor of patients' response will help doctor make more precise individualized therapeutic regimens.ClinicalTrials.gov, NCT03128463 . Registered on 9 March 2017.

SUBMITTER: Jing J

PROVIDER: S-EPMC6057085 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Pharmacogenomic study on anti-VEGF medicine in treatment of macular Neovascular diseases: a study protocol for a prospective observational study.

Jing Jin J Yinchen Shen S Xia Chen C Jing Wang W Chong Chen C Xun Xu X Hengye Huang H Kun Liu L

BMC ophthalmology 20180724 1

<h4>Background</h4>Macular neovascular diseases can cause severe vision loss. A newly approved anti-VEGF drug Conbercept has shown good efficacy and safety in rigorous random controlled trials (RCT), however, it cannot fully reflect the clinical application of Conbercept in real world clinical practice. Moreover, anti-VEGF drugs are expensive and often require multiple treatments, and some patients have poor or even no response to the drugs,this resulted enormous waste of medical resources. Ther ...[more]

PMID: 30041608

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Project description:PurposeTo probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain-optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings.MethodsSD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure.ResultsIn a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25-2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31-3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models.ConclusionsThe proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings.Translational relevancePrediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Project description:PurposeNo established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability.DesignAssessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.ParticipantsEighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).MethodsA total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.Main outcome measuresThe cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.ResultsThe cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.ConclusionsRadiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Dataset Information

Pharmacogenomic study on anti-VEGF medicine in treatment of macular Neovascular diseases: a study protocol for a prospective observational study.

Publications

Pharmacogenomic study on anti-VEGF medicine in treatment of macular Neovascular diseases: a study protocol for a prospective observational study.

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