Echocardiography and Management for Cardiac Trauma.

Trauma is the number one cause of death among Americans between the ages of 1 and 46, costing >$670 billion a year. Blunt and penetrating trauma can lead to cardiac and aortic injuries, with the incidence of death varying upon the location of the damage. Among those who reach the hospital alive, many may survive if the hemorrhage and cardiovascular injuries are diagnosed and treated adequately in a timely fashion. Although echocardiography often is underused in the setting of cardiac trauma, it offers significant diagnosis and treatment potential because it is accessible in most settings, safe, relatively noninvasive, and can provide rapid and accurate trauma assessment in the hands of trained providers. This review article aims to analyze the pathophysiology of cardiac injuries in patients with trauma and the role of echocardiography for the accurate diagnosis of cardiac injury in trauma. This review, additionally, will offer a patient-centered, team-based, early management plan with a treatment algorithm to help improve the quality of care among these patients with cardiac trauma.

Prognostic and predictive signatures for treatment decisions.

AIM: We develop a subgroup selection procedure using both prognostic and predictive biomarkers to identify four patient subpopulations: low- and high-risk responders, and low- and high-risk nonresponders. METHODS: We utilize three regression models to identify three sets of biomarkers: S, prognostic biomarkers; T, predictive biomarkers; and U, prognostic and predictive biomarkers. The prognostic signature C(S) combines with a predictive signature, either C(T) or C(U), to develop two procedures C(S,T) and C(S,U) for identification of four subgroups. RESULTS: Simulation experiment showed that proposed models for identifying the biomarker sets S and U performed well, as did the procedure C(S,U) for subgroup identification. CONCLUSION: The proposed model provides more comprehensive characterization of patient subpopulations, and better accuracy in patient treatment assignment.

Difficult Retrieval of a Broken Catheter From a Total Implantable Venous Access Device.

A broken TIVAD catheter segment was discovered lodged within the right ventricle of a man presenting with chest pain. The catheter removal is detailed.

Development of predictive signatures for treatment selection in precision medicine with survival outcomes.

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker-positive and biomarker-negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood-based change-point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change-point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.

A novel procedure on next generation sequencing data analysis using text mining algorithm.

BACKGROUND: Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. METHODS: We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. RESULTS: The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. CONCLUSION: The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

Ensemble survival trees for identifying subpopulations in personalized medicine.

Recently, personalized medicine has received great attention to improve safety and effectiveness in drug development. Personalized medicine aims to provide medical treatment that is tailored to the patient's characteristics such as genomic biomarkers, disease history, etc., so that the benefit of treatment can be optimized. Subpopulations identification is to divide patients into several different subgroups where each subgroup corresponds to an optimal treatment. For two subgroups, traditionally the multivariate Cox proportional hazards model is fitted and used to calculate the risk score when outcome is survival time endpoint. Median is commonly chosen as the cutoff value to separate patients. However, using median as the cutoff value is quite subjective and sometimes may be inappropriate in situations where data are imbalanced. Here, we propose a novel tree-based method that adopts the algorithm of relative risk trees to identify subgroup patients. After growing a relative risk tree, we apply k-means clustering to group the terminal nodes based on the averaged covariates. We adopt an ensemble Bagging method to improve the performance of a single tree since it is well known that the performance of a single tree is quite unstable. A simulation study is conducted to compare the performance between our proposed method and the multivariate Cox model. The applications of our proposed method to two public cancer data sets are also conducted for illustration.

Text mining for identifying topics in the literatures about adolescent substance use and depression.

BACKGROUND: Both adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents. METHODS: In this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA) was applied to perform text mining on the dataset. Word clouds were used to visually display the content of topics and demonstrate the distribution of vocabularies over each topic. RESULTS: The LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test. CONCLUSIONS: Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.

Use of data mining at the Food and Drug Administration.

OBJECTIVES: This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA). TARGET AUDIENCE: We address data miners in all sectors, anyone interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities. SCOPE: Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

Subgroup identification for treatment selection in biomarker adaptive design.

BACKGROUND: Advances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects. METHODS: The diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets. RESULTS: The DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error. CONCLUSION: Accuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis.

Biomarker identification from next-generation sequencing data for pathogen bacteria characterization and surveillance.

AIM: The purpose was to develop an analytical pipeline for specific gene analysis and biomarker discovery from next generation sequencing (NGS) data. MATERIALS & METHODS: As a test case, the fliC gene reference sequences of 24 Salmonella enterica strains of 13 serotypes and NGS reads of 32 serovar Newport, 48 Montevideo and 115 Enteritidis outbreak isolates were retrieved from the National Center for Biotechnology Information database. RESULTS: Establishment of an analytical pipeline consisting of four steps: reference sequences retrieval and template sequence determination; NGS sequence reads retrieval; multiple sequence alignments and phylogenetic analysis; data mining and biomarker discovery. CONCLUSION: The pipeline developed provides an effective bioinformatics tool for genetic diversity clarification and marker sequences discovery for pathogen characterization and surveillance.

A heuristic approach to determine an appropriate number of topics in topic modeling.

BACKGROUND: Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics data represent words. Latent Dirichlet Allocation (LDA) is the most commonly used topic modelling method across a wide number of technical fields. However, model development can be arduous and tedious, and requires burdensome and systematic sensitivity studies in order to find the best set of model parameters. Often, time-consuming subjective evaluations are needed to compare models. Currently, research has yielded no easy way to choose the proper number of topics in a model beyond a major iterative approach. METHODS AND RESULTS: Based on analysis of variation of statistical perplexity during topic modelling, a heuristic approach is proposed in this study to estimate the most appropriate number of topics. Specifically, the rate of perplexity change (RPC) as a function of numbers of topics is proposed as a suitable selector. We test the stability and effectiveness of the proposed method for three markedly different types of grounded-truth datasets: Salmonella next generation sequencing, pharmacological side effects, and textual abstracts on computational biology and bioinformatics (TCBB) from PubMed. CONCLUSION: The proposed RPC-based method is demonstrated to choose the best number of topics in three numerical experiments of widely different data types, and for databases of very different sizes. The work required was markedly less arduous than if full systematic sensitivity studies had been carried out with number of topics as a parameter. We understand that additional investigation is needed to substantiate the method's theoretical basis, and to establish its generalizability in terms of dataset characteristics.

Predictive biomarkers for treatment selection: statistical considerations.

Predictive biomarkers are developed for treatment selection to identify patients who are likely to benefit from a particular therapy. This review describes statistical methods and discusses issues in the development of predictive biomarkers to enhance study efficiency for detection of treatment effect on the selected responder patients in clinical studies. The statistical procedure for treatment selection consists of three components: biomarker identification, subgroup selection and clinical utility assessment. Major statistical issues discussed include biomarker designs, procedures to identify predictive biomarkers, classification models for subgroup selection, subgroup analysis and multiple testing for clinical utility assessment and evaluation.

Identification of drug-induced toxicity biomarkers for treatment determination.

Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.

Topic modeling for cluster analysis of large biological and medical datasets.

BACKGROUND: The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. RESULTS: In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. CONCLUSION: Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

A composite model for subgroup identification and prediction via bicluster analysis.

BACKGROUND: A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen serotypes of microorganisms, different tumor subtypes in cancer patients, or different genetic makeups of patients related to treatment response. METHODS: This paper proposes a composite model for subgroup identification and prediction using biclusters. A biclustering technique is first used to identify a set of biclusters from the sampled data. For each bicluster, a subgroup-specific binary classifier is built to determine if a particular sample is either inside or outside the bicluster. A composite model, which consists of all binary classifiers, is constructed to classify samples into several disjoint subgroups. The proposed composite model neither depends on any specific biclustering algorithm or patterns of biclusters, nor on any classification algorithms. RESULTS: The composite model was shown to have an overall accuracy of 97.4% for a synthetic dataset consisting of four subgroups. The model was applied to two datasets where the sample's subgroup memberships were known. The procedure showed 83.7% accuracy in discriminating lung cancer adenocarcinoma and squamous carcinoma subtypes, and was able to identify 5 serotypes and several subtypes with about 94% accuracy in a pathogen dataset. CONCLUSION: The composite model presents a novel approach to developing a biclustering-based classification model from unlabeled sampled data. The proposed approach combines unsupervised biclustering and supervised classification techniques to classify samples into disjoint subgroups based on their associated attributes, such as genotypic factors, phenotypic outcomes, efficacy/safety measures, or responses to treatments. The procedure is useful for identification of unknown species or new biomarkers for targeted therapy.

MAVTgsa: an R package for gene set (enrichment) analysis.

Gene set analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes the P values and FDR (false discovery rate) q-value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics.

BACKGROUND: Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL). RESULTS: In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations. CONCLUSIONS: In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

Benchmark dose calculation for ordered categorical responses.

The use of benchmark dose (BMD) calculations for dichotomous or continuous responses is well established in the risk assessment of cancer and noncancer endpoints. In some cases, responses to exposure are categorized in terms of ordinal severity effects such as none, mild, adverse, and severe. Such responses can be assessed using categorical regression (CATREG) analysis. However, while CATREG has been employed to compare the benchmark approach and the no-adverse-effect-level (NOAEL) approach in determining a reference dose, the utility of CATREG for risk assessment remains unclear. This study proposes a CATREG model to extend the BMD approach to ordered categorical responses by modeling severity levels as censored interval limits of a standard normal distribution. The BMD is calculated as a weighted average of the BMDs obtained at dichotomous cutoffs for each adverse severity level above the critical effect, with the weights being proportional to the reciprocal of the expected loss at the cutoff under the normal probability model. This approach provides a link between the current BMD procedures for dichotomous and continuous data. We estimate the CATREG parameters using a Markov chain Monte Carlo simulation procedure. The proposed method is demonstrated using examples of aldicarb and urethane, each with several categories of severity levels. Simulation studies comparing the BMD and BMDL (lower confidence bound on the BMD) using the proposed method to the correspondent estimates using the existing methods for dichotomous and continuous data are quite compatible; the difference is mainly dependent on the choice of cutoffs for the severity levels.

Sex-specific genomic biomarkers for individualized treatment of life-threatening diseases.

Numerous studies have demonstrated sex differences in drug reactions to the same drug treatment, steering away from the traditional view of one-size-fits-all medicine. A premise of this study is that the sex of a patient influences difference in disease characteristics and risk factors. In this study, we intend to exploit and to obtain better sex-specific biomarkers from gene-expression data. We propose a procedure to isolate a set of important genes as sex-specific genomic biomarkers, which may enable more effective patient treatment. A set of sex-specific genes is obtained by a variable importance ranking using a combination of cross-validation methods. The proposed procedure is applied to three gene-expression datasets.