Problems in functioning from the patient perspective using the International Classification of Functioning, Disability and Health (ICF) as a reference

We conducted a qualitative, multicenter study using a focus group design to explore the lived experiences of persons with any kind of primary sleep disorder with regard to functioning and contextual factors using six open-ended questions related to the International Classification of Functioning, Disability and Health (ICF) components. We classified the results using the ICF as a frame of reference. We identified the meaningful concepts within the transcribed data and then linked them to ICF categories according to established linking rules. The six focus groups with 27 participants yielded a total of 6986 relevant concepts, which were linked to a total of 168 different second-level ICF categories. From the patient perspective, the ICF components: (1) Body Functions; (2) Activities & Participation; and (3) Environmental Factors were equally represented; while (4) Body Structures appeared poignantly less frequently. Out of the total number of concepts, 1843 concepts (26%) were assigned to the ICF component Personal Factors, which is not yet classified but could indicate important aspects of resource management and strategy development of those who have a sleep disorder. Therefore, treatment of patients with sleep disorders must not be limited to anatomical and (patho-)physiological changes, but should also consider a more comprehensive view that includes patient's demands, strategies and resources in daily life and the contextual circumstances surrounding the individual.

Identification of problems in functioning of people with sleep disorders in a clinical setting using the International Classification of Functioning Disability and Health (ICF) Checklist

We conducted an explorative, cross-sectional, multi-centre study in order to identify the most common problems of people with any kind of (primary) sleep disorder in a clinical setting using the International Classification of Functioning, Disability and Health (ICF) as a frame of reference. Data were collected from patients using a structured face-to-face interview of 45-60 min duration. A case record form for health professionals containing the extended ICF Checklist, sociodemographic variables and disease-specific variables was used. The study centres collected data of 99 individuals with sleep disorders. The identified categories include 48 (32%) for body functions, 13 (9%) body structures, 55 (37%) activities and participation and 32 (22%) for environmental factors. 'Sleep functions' (100%) and 'energy and drive functions', respectively, (85%) were the most severely impaired second-level categories of body functions followed by 'attention functions' (78%) and 'temperament and personality functions' (77%). With regard to the component activities and participation, patients felt most restricted in the categories of 'watching' (e.g. TV) (82%), 'recreation and leisure' (75%) and 'carrying out daily routine' (74%). Within the component environmental factors the categories 'support of immediate family', 'health services, systems and policies' and 'products or substances for personal consumption [medication]' were the most important facilitators; 'time-related changes', 'light' and 'climate' were the most important barriers. The study identified a large variety of functional problems reflecting the complexity of sleep disorders. The ICF has the potential to provide a comprehensive framework for the description of functional health in individuals with sleep disorders in a clinical setting.

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.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Towards the introduction of the 'Immunoscore' in the classification of malignant tumours.

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence of cancer cells in draining and regional lymph nodes (N) and evidence for distant metastases (M). However, it is now recognized that the clinical outcome can vary significantly among patients within the same stage. The current classification provides limited prognostic information and does not predict response to therapy. Multiple ways to classify cancer and to distinguish different subtypes of colorectal cancer have been proposed, including morphology, cell origin, molecular pathways, mutation status and gene expression-based stratification. These parameters rely on tumour-cell characteristics. Extensive literature has investigated the host immune response against cancer and demonstrated the prognostic impact of the in situ immune cell infiltrate in tumours. A methodology named 'Immunoscore' has been defined to quantify the in situ immune infiltrate. In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC/UICC TNM classification. An international consortium has been initiated to validate and promote the Immunoscore in routine clinical settings. The results of this international consortium may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.