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Baseline prediction of combination therapy outcome in hepatitis C virus 1b infected patients by discriminant analysis using viral and host factors.

ABSTRACT: Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy -especially among genotype 1 infected patients-, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline.Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1-E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A "leave-one-out" cross-validation method was used to assess the reliability of the discriminant models.Lower alanine transaminase levels (ALT, p=0.009), a higher number of quasispecies variants in the E1-E2 region (number of haplotypes, nHap_E1-E2) (p=0.003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p=0.039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90.5% sensitivity and 95.5% specificity, 85.7% sensitivity and 81.8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1-E2, and gamma-glutamyl transferase and ALT levels.Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this study led to accurate predictions in our population of Spanish HCV-1b treatment naïve patients.

SUBMITTER: Saludes V

PROVIDER: S-EPMC2994723 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Baseline prediction of combination therapy outcome in hepatitis C virus 1b infected patients by discriminant analysis using viral and host factors.

Saludes Verónica V Bracho Maria Alma MA Valero Oliver O Ardèvol Mercè M Planas Ramón R González-Candelas Fernando F Ausina Vicente V Martró Elisa E

PloS one 20101130 11

<h4>Background</h4>Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy -especially among genotype 1 infected patients-, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment fail ...[more]

PMID: 21152430

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Project description:BackgroundOnly about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters.MethodologySeventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively -training group-, and 21 prospectively -validation group-). Host and viral-related factors (viral load, and genetic variability in the E1-E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group.Principal findingsA multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1-E2 region, an amino acid substitution pattern in the viral core region, the IL28B polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 vs. 0.7361, respectively).Conclusions and significanceThe baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.

Project description:BackgroundAccurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline.MethodsWe included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process.ResultsFrom 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) ≤ 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) ≥ 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach.ConclusionsThe combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values.

Project description:BackgroundCOVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding time and disease progression, then develop and validate two prognostic discriminant models.MethodsThis study included 125 hospitalized patients with COVID-19, for whom 44 parameters were recorded, including age, gender, underlying comorbidities, epidemiological features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. Fisher's exact test and Mann-Whitney test were used for feature selection. All models were developed with fourfold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L2 regularization, prognostic discriminant models were built to predict postponed viral shedding time and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models' sensitivity and specificity.ResultsSixty-nine patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Six and eleven demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding time and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y1 (postponed viral shedding time) = - 0.244 + 0.2829x1 (the interval from the onset of symptoms to antiviral treatment) + 0.2306x4 (age) + 0.234x28 (Urea) - 0.2847x34 (Dual-antiviral therapy) + 0.3084x38 (Treatment with antibiotics) + 0.3025x21 (Treatment with Methylprednisolone); y2 (disease progression) = - 0.348-0.099x2 (interval from Jan 1st,2020 to individualized onset of symptoms) + 0.0945x4 (age) + 0.1176x5 (imaging characteristics) + 0.0398x8 (short-term exposure to Wuhan) - 0.1646x19 (lymphocyte counts) + 0.0914x20 (Neutrophil counts) + 0.1254x21 (Neutrphil/lymphocyte ratio) + 0.1397x22 (C-Reactive Protein) + 0.0814x23 (Procalcitonin) + 0.1294x24 (Lactic dehydrogenase) + 0.1099x29 (Creatine kinase).The output ≥ 0 predicted postponed viral shedding time or disease progressing to severe/critical state. These two models yielded the maximum AUROC and faired best in terms of prognostic performance (sensitivity of78.6%, 75%, and specificity of 66.7%, 88.9% for prediction of postponed viral shedding time and disease severity, respectively).ConclusionThe two discriminant models could effectively predict the postponed viral shedding time and disease severity and could be used as early-warning tools for COVID-19.

Dataset Information

Baseline prediction of combination therapy outcome in hepatitis C virus 1b infected patients by discriminant analysis using viral and host factors.

Publications

Baseline prediction of combination therapy outcome in hepatitis C virus 1b infected patients by discriminant analysis using viral and host factors.

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