Analysis of Phenotypic Variation in Childhood Wheezing Disorders

Recurrent wheezing or asthma is a common problem in children that has increased considerably in prevalence in the past few decades. The causes and underlying mechanisms are poorly understood and it is thought that a numb er of distinct diseases causing similar symptoms are involved. Due to the lack of a biologically founded classification system, children are classified according to their observed disease related features (symptoms, signs, measurements) into phenotypes. The objectives of this PhD project were a) to develop tools for analysing phenotypic variation of a disease, and b) to examine phenotypic variability of wheezing among children by applying these tools to existing epidemiological data. A combination of graphical methods (multivariate co rrespondence analysis) and statistical models (latent variables models) was used. In a first phase, a model for discrete variability (latent class model) was applied to data on symptoms and measurements from an epidemiological study to identify distinct phenotypes of wheezing. In a second phase, the modelling framework was expanded to include continuous variability (e.g. along a severity gradient) and combinations of discrete and continuo us variability (factor models and factor mixture models). The third phase focused on validating the methods using simulation studies. The main body of this thesis consists of 5 articles (3 published, 1 submitted and 1 to be submitted) including applications, methodological contributions and a review. The main findings and contributions were: 1) The application of a latent class model to epidemiological data (symptoms and physiological measurements) yielded plausible pheno types of wheezing with distinguishing characteristics that have previously been used as phenotype defining characteristics. 2) A method was proposed for including responses to conditional questions (e.g. questions on severity or triggers of wheezing are asked only to children with wheeze) in multivariate modelling.ii 3) A panel of clinicians was set up to agree on a plausible model for wheezing diseases. The model can be used to generate datasets for testing the modelling approach. 4) A critical review of methods for defining and validating phenotypes of wheeze in children was conducted. 5) The simulation studies showed that a parsimonious parameterisation of the models is required to identify the true underlying structure of the data. The developed approach can deal with some challenges of real-life cohort data such as variables of mixed mode (continuous and categorical), missing data and conditional questions. If carefully applied, the approach can be used to identify whether the underlying phenotypic variation is discrete (classes), continuous (factors) or a combination of these. These methods could help improve precision of research into causes and mechanisms and contribute to the development of a new classification of wheezing disorders in children and other diseases which are difficult to classify.

The Impact of Pre-Operative Malperfusion on Outcome in Acute Type A Aortic Dissection: Results From the GERAADA Registry

BACKGROUND Malperfusion adversely affects outcomes in patients with acute type A aortic dissection, but reliable quantitative data are lacking. OBJECTIVES The aim of this study was to analyze the impact of various forms of malperfusion on early outcome. METHODS A total of 2,137 consecutive patients enrolled in GERAADA (German Registry for Acute Aortic Dissection Type A) who underwent surgery between 2006 and 2010, of whom 717 (33.6%) had any kind of pre-operative malperfusion, were retrospectively analyzed. RESULTS All-cause 30-day mortality was 16.9% and varied substantially according to the number of organ systems affected by malperfusion (none, 12.6%; 1 system, 21.3%; 2 systems, 30.9%; 3 systems, 43.4%; p < 0.001). Pre-operative cerebral malperfusion, comatose state, peripheral malperfusion, visceral malperfusion, involvement of supra-aortic branches, coronary malperfusion, and renal malperfusion were all independent predictors of developing any post-operative malperfusion syndrome. When survival was considered, age, peripheral malperfusion, involvement of supra-aortic branches, coronary malperfusion, spinal malperfusion, a primary entry in the descending aorta, and pre-operative comatose state were independent predictors, again with increasing significance. CONCLUSIONS Malperfusion remains a severe clinical condition with strong potential for adverse outcomes in patients undergoing surgery for acute type A aortic dissection. The GERAADA registry suggests that the impact of the number of organs involved and the type of malperfusion on outcome differs substantially. Introducing an appropriate classification system, such as "complicated" and uncomplicated" acute type A aortic dissection, might help predict individual risk as well as select a surgical strategy that may quickly resolve malperfusion.

Towards mapping the late Quaternary vegetation change of Europe

The number of well-dated pollen diagrams in Europe has increased considerably over the last 30 years and many of them have been submitted to the European Pollen Database (EPD). This allows for the construction of increasingly precise maps of Holocene vegetation change across the continent. Chronological information in the EPD has been expressed in uncalibrated radiocarbon years, and most chronologies to date are based on this time scale. Here we present new chronologies for most of the datasets stored in the EPD based on calibrated radiocarbon years. Age information associated with pollen diagrams is often derived from the pollen stratigraphy itself or from other sedimentological information. We reviewed these chronological tie points and assigned uncertainties to them. The steps taken to generate the new chronologies are described and the rationale for a new classification system for age uncertainties is introduced. The resulting chronologies are fit for most continental-scale questions. They may not provide the best age model for particular sites, but may be viewed as general purpose chronologies. Taxonomic particularities of the data stored in the EPD are explained. An example is given of how the database can be queried to select samples with appropriate age control as well as the suitable taxonomic level to answer a specific research question.

Hysteroscopic outpatient metroplasty to expand dysmorphic uteri (HOME-DU technique): a pilot study.

The new classification system of uterine anomalies from the European Society of Human Reproduction and Embryology and the European Society for Gynaecological Endoscopy defines T-shaped and tubular-shaped infantilis uteri as 'dysmorphic'. Such malformations have been proven to be associated with poor reproductive performance. A prospective observational study was conducted with 30 infertile women with dysmorphic uterus who underwent the novel Hysteroscopic Outpatient Metroplasty to Expand Dysmorphic Uteri (HOME-DU ) technique. Incisions are made on the uterine walls with a 5 Fr bipolar electrode. The procedure was conducted in outpatients under conscious sedation, using a 5-mm office hysteroscope. The technique was successful in all cases without complications. A net increase of uterine volume was found, as measured at hysteroscopy and three-dimensional transvaginal ultrasound (P < 0.001). Uterine morphology improved in all patients but one. At mean follow-up of 15 months, clinical pregnancy rate was 57% and term delivery rate 65%. These early data support HOME-DU as safe and effective in expanding the volume and normalizing the appearance of the uterine cavity of dysmorphic uteri. Although the cohort was small, pregnancy and live births outcomes were favourable in this poor-prognosis group, implying desirable benefits, which should be compared with other techniques.

Percutaneous embolization of arteriovenous malformations at the plantar aspect of the foot

Peripheral arteriovenous malformations (AVM) remain most challenging among various congenital vascular malformations to be treated. Here we present three illustrative patients with Yakes type IIIb and type IV AVM at the plantar aspect of the foot who were successfully treated by minimally invasive embolization. The value of the Yakes AVM classification system to guide the therapeutic decision making by directing specific therapeutic procedures to specific AVM types defined by their angioarchitecture is demonstrated. Direct percutaneous AVM puncture with coiling of aneurysmal outflow vein and subsequent ethanol embolization is shown. Finally, the report illustrates that several AVM types can coexist.

Classification of acute and delayed contrast media-induced reactions: proposal of a three-step system

Physicians and scientists use a broad spectrum of terms to classify contrast media (CM)-induced adverse reactions. In particular, the designation of hypersensitivity reactions is quite varied. Consequently, comparisons of different papers dealing with this subject are difficult or even impossible. Moreover, general descriptions may lead to problems in understanding reactions in patients with a history of adverse CM-reactions, and in efficiently managing these patients. Therefore, the goal of this paper is to suggest an easy system to clearly classify these reactions. The proposed three-step systems (3SS) is built up as follows: step 1 exactly describes the clinical features, including their severity; step 2 categorizes the time point of the onset (immediate or nonimmediate); and step 3 generally classifies the reaction (hypersensitivity or nonhypersensitivity reaction). The 3SS may facilitate better understanding of the clinical manifestations of adverse CM reactions and may support the prevention of these reactions on the basis of personalized medicine approaches.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Cancer classification using the Immunoscore: a worldwide task force

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Content comparison of health status measures for obesity based on the international classification of functioning, disability and health

OBJECTIVE: To compare the content covered by twelve obesity-specific health status measures using the International Classification of Functioning, Disability and Health (ICF). DESIGN: Obesity-specific health status measures were identified and then linked to the ICF separately by two trained health professionals according to standardized guidelines. The degree of agreement between health professionals was calculated by means of the kappa (kappa) statistic. Bootstrapped confidence intervals (CI) were calculated. The obesity-specific health-status measures were compared on the component and category level of the ICF. MEASUREMENTS: welve condition-specific health-status measures were identified and included in this study, namely the obesity-related problem scale, the obesity eating problems scale, the obesity-related coping and obesity-related distress questionnaire, the impact of weight on quality of life questionnaire (short version), the health-related quality of life questionnaire, the obesity adjustment survey (short form), the short specific quality of life scale, the obesity-related well-being questionnaire, the bariatric analysis and reporting outcome system, the bariatric quality of life index, the obesity and weight loss quality of life questionnaire and the weight-related symptom measure. RESULTS: In the 280 items of the eight measures, a total of 413 concepts were identified and linked to the 87 different ICF categories. The measures varied strongly in the number of concepts contained and the number of ICF categories used to map these concepts. Items on body functions varied form 12% in the obesity-related problem scale to 95% in the weight-related symptom measure. The estimated kappa coefficients ranged between 0.79 (CI: 0.72, 0.86) at the component ICFs level and 0.97 (CI: 0.93, 1.0) at the third ICF's level. CONCLUSION: The ICF proved highly useful for the content comparison of obesity-specific health-status measures. The results may provide clinicians and researchers with new insights when selecting health-status measures for clinical studies in obesity.

Comparison of different radiography systems in an experimental study for detection of forearm fractures and evaluation of the Müller-AO and Frykman classification for distal radius fractures

OBJECTIVES: We sought to compare the diagnostic performance of screen-film radiography, storage-phosphor radiography, and a flat-panel detector system in detecting forearm fractures and to classify distal radius fractures according to the Müller-AO and Frykman classifications compared with the true extent, depicted by anatomic preparation. MATERIALS AND METHODS: A total of 71 cadaver arms were fractured in a material testing machine creating different fractures of the radius and ulna as well as of the carpal bones. Radiographs of the complete forearm were evaluated by 3 radiologists, and anatomic preparation was used as standard of reference in a receiver operating curve analysis. RESULTS: The highest diagnostic performance was obtained for the detection of distal radius fractures with area under the receiver operating curve (AUC) values of 0.959 for screen-film radiography, 0.966 for storage-phosphor radiography, and 0.971 for the flat-panel detector system (P > 0.05). Exact classification was slightly better for the Frykman (kappa values of 0.457-0.478) compared with the Müller-AO classification (kappa values of 0.404-0.447), but agreement can be considered as moderate for both classifications. CONCLUSIONS: The 3 imaging systems showed a comparable diagnostic performance in detecting forearm fractures. A high diagnostic performance was demonstrated for distal radius fractures and conventional radiography can be routinely performed for fracture detection. However, compared with anatomic preparation, depiction of the true extent of distal radius fractures was limited and the severity of distal radius fractures tends to be underestimated.

Developing ICF Core Sets for persons with sleep disorders based on the International Classification of Functioning, Disability and Health

BACKGROUND: With the International Classification of Functioning, Disability and Health (ICF), we can now rely on a globally agreed-upon framework and system for classifying the typical spectrum of problems in the functioning of persons given the environmental context in which they live. ICF Core Sets are subgroups of ICF items selected to capture those aspects of functioning that are most likely to be affected by sleep disorders. OBJECTIVE: The objective of this paper is to outline the developmental process for the ICF Core Sets for Sleep. METHODS: The ICF Core Sets for Sleep will be defined at an ICF Core Sets Consensus Conference, which will integrate evidence from preliminary studies, namely (a) a systematic literature review regarding the outcomes used in clinical trials and observational studies, (b) focus groups with people in different regions of the world who have sleep disorders, (c) an expert survey with the involvement of international clinical experts, and (d) a cross-sectional study of people with sleep disorders in different regions of the world. CONCLUSION: The ICF Core Sets for Sleep are being designed with the goal of providing useful standards for research, clinical practice and teaching. It is hypothesized that the ICF Core Sets for Sleep will stimulate research that leads to an improved understanding of functioning, disability, and health in sleep medicine. It is of further hope that such research will lead to interventions and accommodations that improve the restoration and maintenance of functioning and minimize disability among people with sleep disorders throughout the world.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

A fuzzy risk attitude classification based on prospect theory

Traditional methods do not actually measure peoples’ risk attitude naturally and precisely. Therefore, a fuzzy risk attitude classification method is developed. Since the prospect theory is usually considered as an effective model of decision making, the personalized parameters in prospect theory are firstly fuzzified to distinguish people with different risk attitudes, and then a fuzzy classification database schema is applied to calculate the exact value of risk value attitude and risk be- havior attitude. Finally, by applying a two-hierarchical clas- sification model, the precise value of synthetical risk attitude can be acquired.