236 Novel Guidelines to Avoid Routine Blood Tests After Robotic Assisted Radical Prostatectomy (RARP)

Introduction

Patients undergoing RARP commonly require routine post-operative blood tests. This practice dates from an era of open surgery, with increased blood loss and complications. We aim to improve specificity of blood test requests with novel guidelines.

Method

1039 consecutive RARP patients at two tertiary urology centres in the UK were audited. Novel guidelines constructed based on risk stratified evidence from the initial audit were used to prospectively audit 133 patients.

Results

16% had clinical concerns post-operatively. 1% and 4% had an intra- and post-operative complication. Intra- or post-operative clinical judgement flagged post-operative complications in 99.9%. 80% had routine blood tests with no clinical concerns. 6% had delayed discharge due to delayed processing of blood tests. 0.9% received a peri-operative transfusion.

Re-Audit Novel guidelines reduced the number of blood tests requested from 100% to 36%. Specificity in diagnosing a complication improved from 0% to 67%. Discharge delays reduced from 6% to 0% and no post-operative complications were missed (sensitivity 100%).

Conclusions

Routine blood tests, without an indication, did not flag any additional post-operative complications. Blood transfusion is rare for RARP. Novel guidelines to request post-operative blood tests will reduce costs and discharge delays whilst maintaining appropriate patient safety and care.

Identifying Biomarkers from Mass Spectrometry Data with Ordinal Outcome

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale. Ignoring this natural ordering results in some loss of information. In this paper, we propose a Bayesian model for the analysis of mass spectrometry data with ordered outcome. The method provides a unified approach for identifying relevant markers and predicting class membership. This is accomplished by building a stochastic search variable selection method within an ordinal outcome model. We apply the methodology to mass spectrometry data on ovarian cancer cases and healthy individuals. We also utilize wavelet-based techniques to remove noise from the mass spectra prior to analysis. We identify protein markers associated with being healthy, having low grade ovarian cancer, or being a high grade case. For comparison, we repeated the analysis using conventional classification procedures and found improved predictive accuracy with our method.

Open-label study of faldaprevir plus peginterferon and ribavirin in hepatitis C virus genotype 1-infected patients who failed placebo plus peginterferon and ribavirin

Faldaprevir, a hepatitis C virus (HCV) NS3/4A protease inhibitor, was evaluated in HCV genotype 1-infected patients who failed peginterferon and ribavirin (PegIFN/RBV) treatment during one of three prior faldaprevir trials. Patients who received placebo plus PegIFN/RBV and had virological failure during a prior trial were enrolled and treated in two cohorts: prior relapsers (n = 43) and prior nonresponders (null responders, partial responders and patients with breakthrough; n = 75). Both cohorts received faldaprevir 240 mg once daily plus PegIFN/RBV for 24 weeks. Prior relapsers with early treatment success (ETS; HCV RNA <25 IU/mL detectable or undetectable at week 4 and <25 IU/mL undetectable at week 8) stopped treatment at week 24. Others received PegIFN/RBV through week 48. The primary efficacy endpoint was sustained virological response (HCV RNA <25 IU/mL undetectable) 12 weeks post treatment (SVR12). More prior nonresponders than prior relapsers had baseline HCV RNA ≥ 800,000 IU/mL (80% vs 58%) and a non-CC IL28B genotype (91% vs 70%). Rates of SVR12 (95% CI) were 95.3% (89.1, 100.0) among prior relapsers and 54.7% (43.4, 65.9) among prior nonresponders; corresponding ETS rates were 97.7% and 65.3%. Adverse events led to faldaprevir discontinuations in 3% of patients. The most common Division of AIDS Grade ≥ 2 adverse events were anaemia (13%), nausea (10%) and hyperbilirubinaemia (9%). In conclusion, faldaprevir plus PegIFN/RBV achieved clinically meaningful SVR12 rates in patients who failed PegIFN/RBV in a prior trial, with response rates higher among prior relapsers than among prior nonresponders. The adverse event profile was consistent with the known safety profile of faldaprevir.

Baseline Polymorphisms and Emergence of Drug Resistance in the NS3/4A Protease of Hepatitis C Virus Genotype 1 following Treatment with Faldaprevir and Pegylated Interferon Alpha 2a/Ribavirin in Phase 2 and Phase 3 Studies

Analysis of data pooled from multiple phase 2 (SILEN-C1 to 3) and phase 3 studies (STARTVerso1 to 4) of the hepatitis C virus (HCV) nonstructural protein 3/4A (NS3/4A) protease inhibitor faldaprevir plus pegylated interferon alpha/ribavirin (PR) provides a comprehensive evaluation of baseline and treatment-emergent NS3/4A amino acid variants among HCV genotype-1 (GT-1)-infected patients. Pooled analyses of GT-1a and GT-1b NS3 population-based pretreatment sequences (n = 3,124) showed that faldaprevir resistance-associated variants (RAVs) at NS3 R155 and D168 were rare (<1%). No single, noncanonical NS3 protease or NS4A cofactor baseline polymorphism was associated with a reduced sustained virologic response (SVR) to faldaprevir plus PR, including Q80K. The GT-1b NS3 helicase polymorphism T344I was associated with reduced SVR to faldaprevir plus PR (P < 0.0001) but was not faldaprevir specific, as reduced SVR was also observed with placebo plus PR. Among patients who did not achieve SVR and had available NS3 population sequences (n = 507 GT-1a; n = 349 GT-1b), 94% of GT-1a and 83% of GT-1b encoded faldaprevir treatment-emergent RAVs. The predominant GT-1a RAV was R155K (88%), whereas GT-1b encoded D168 substitutions (78%) in which D168V was predominant (67%). The novel GT-1b NS3 S61L substitution emerged in 7% of virologic failures as a covariant with D168V, most often among the faldaprevir breakthroughs; S61L in combination with D168V had a minimal impact on faldaprevir susceptibility compared with that for D168V alone (1.5-fold difference in vitro). The median time to loss of D168 RAVs among GT-1b-infected patients who did not have a sustained virologic response at 12 weeks posttreatment (non-SVR12) after virologic failure was 5 months, which was shorter than the 14 months for R155 RAVs among GT-1a-infected non-SVR12 patients, suggesting that D168V is less fit than R155K in the absence of faldaprevir selective pressure.

Gene selection in arthritis classification with large-scale microarray expression profiles

The use of large-scale microarray expression profiling to identify predictors of disease class has become of major interest. Beyond their impact in the clinical setting (i.e. improving diagnosis and treatment), these markers are also likely to provide clues on the molecular mechanisms underlining the diseases. In this paper we describe a new method for the identification of multiple gene predictors of disease class. The method is applied to the classification of two forms of arthritis that have a similar clinical endpoint but different underlying molecular mechanisms: rheumatoid arthritis (RA) and osteoarthritis (OA). We aim at both the classification of samples and the location of genes characterizing the different classes. We achieve both goals simultaneously by combining a binary probit model for classification with Bayesian variable selection methods to identify important genes.We find very small sets of genes that lead to good classification results. Some of the selected genes are clearly correlated with known aspects of the biology of arthritis and, in some cases, reflect already known differences between RA and OA.

Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies

This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data. Our specific interest is in the analysis of data sets with predictors that have an a priori unknown form of possibly nonlinear associations to the response. The modeling approach we describe incorporates Gaussian processes in a generalized linear model framework to obtain a class of nonparametric regression models where the covariance matrix depends on the predictors. We consider, in particular, continuous, categorical and count responses. We also look into models that account for survival outcomes. We explore alternative covariance formulations for the Gaussian process prior and demonstrate the flexibility of the construction. Next, we focus on the important problem of selecting variables from the set of possible predictors and describe a general framework that employs mixture priors. We compare alternative MCMC strategies for posterior inference and achieve a computationally efficient and practical approach. We demonstrate performances on simulated and benchmark data sets.

Early response to antidepressant treatment in bulimia nervosa

BACKGROUND

Bulimia nervosa (BN) is a serious psychiatric disorder characterized by frequent episodes of binge eating and inappropriate compensatory behavior. Numerous trials have found that antidepressant medications are efficacious for the treatment of BN. Early response to antidepressant treatment, in the first few weeks after medication is initiated, may provide clinically useful information about an individual's likelihood of ultimately benefitting or not responding to such treatment. The purpose of this study was to examine the relationship between initial and later response to fluoxetine, the only antidepressant medication approved by the US Food and Drug Administration (FDA) for the treatment of BN, with the goal of developing guidelines to aid clinicians in deciding when to alter the course of treatment.

METHOD

Data from the two largest medication trials conducted in BN (n=785) were used. Receiver operating characteristic (ROC) curves were constructed to assess whether symptom change during the first several weeks of treatment was associated with eventual non-response to fluoxetine at the end of the trial.

RESULTS

Eventual non-responders to fluoxetine could be reliably identified by the third week of treatment.

CONCLUSIONS

Patients with BN who fail to report a 60% decrease in the frequency of binge eating or vomiting at week 3 are unlikely to respond to fluoxetine. As no reliable relationships between pretreatment characteristics and eventual response to pharmacotherapy have been identified for BN, early response is one of the only available indicators to guide clinical management.

Identifying biomarkers from mass spectrometry data with ordinal outcome

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale. Ignoring this natural ordering results in some loss of information. In this paper, we propose a Bayesian model for the analysis of mass spectrometry data with ordered outcome. The method provides a unified approach for identifying relevant markers and predicting class membership. This is accomplished by building a stochastic search variable selection method within an ordinal outcome model. We apply the methodology to mass spectrometry data on ovarian cancer cases and healthy individuals. We also utilize wavelet-based techniques to remove noise from the mass spectra prior to analysis. We identify protein markers associated with being healthy, having low grade ovarian cancer, or being a high grade case. For comparison, we repeated the analysis using conventional classification procedures and found improved predictive accuracy with our method.

Bayesian variable selection for the analysis of microarray data with censored outcomes

MOTIVATION

A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes. We consider accelerated failure time (AFT) models with log-normal and log-t distributional assumptions. A data augmentation approach is used to impute the failure times of censored observations and mixture priors are used for the regression coefficients to identify promising subsets of variables. The proposed method provides a unified procedure for the selection of relevant genes and the prediction of survivor functions.

RESULTS

We demonstrate the performance of the method on simulated examples and on several microarray datasets. For the simulation study, we consider scenarios with large number of noisy variables and different degrees of correlation between the relevant and non-relevant (noisy) variables. We are able to identify the correct covariates and obtain good prediction of the survivor functions. For the microarray applications, some of our selected genes are known to be related to the diseases under study and a few are in agreement with findings from other researchers.

AVAILABILITY

The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author.

CONTACT

mvannucci@stat.tamu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.

Modeling antitumor activity by using a non-linear mixed-effects model

The response of solid tumors to antitumor treatment generally declines markedly with treatment time. Sometimes, a tumor regrows (rebounds) before the end of the treatment period. Studies of the patterns of tumor response to treatment are important, because they may provide useful information for clinical decision-making. We have investigated patterns of tumor response in mouse xenograft tumors by using data from a study conducted at St. Jude Children's Research Hospital. We applied a biexponential non-linear mixed-effects model to an analysis of changes in tumor volume over a given period of treatment. The model gives a good fit to the data, even for small sample sizes. We addressed the relation between the baseline tumor volumes and the decay rates of the first and second stages of the tumor's response to treatment, and we applied sensitive analysis to determine the effect of using different imputed values for missing data. We also proposed a novel approach to a comparison of the antitumor effects of three different treatments, and we used the data from a St. Jude study to demonstrate the potential of this comparison approach in cancer clinical decision-making.

Gene selection: a Bayesian variable selection approach

Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data.

SUPPLEMENTARY INFORMATION

http://stat.tamu.edu/people/faculty/bmallick.html.