Badih Ghattas

A noninvasive investigation of muscle energetics supports similarities between exertional heat stroke and malignant hyperthermia.

Exertional heat stroke (EHS) is usually triggered by strenuous exercise performed under hot and humid environmental conditions. Although the pathogenesis of an EHS episode differs from that of a clinical malignant hyperthermia (MH) crisis, both conditions share some similarities in symptoms, such as the abnormal increase in core temperature. By use of 31P magnetic resonance spectroscopy, we analyzed the muscle energetics of 26 post-EHS subjects for whom in vitro halothane/caffeine contracture tests were abnormal and investigated possible similarities with subjects susceptible to MH. An early decrease of pH was noted during the first minute of exercise in EHS subjects as compared with controls. EHS subjects were divided into two subgroups according to the diagnostic score previously developed for MH subjects. The 19 subjects (73%) with a score higher than 2 displayed significantly larger caffeine-induced and earlier ryanodine-induced contractures on muscle biopsies as compared with the rest of the group (7 subjects). The results demonstrate that muscle energetics are abnormal in subjects who have experienced EHS and suggest a possible link between MH and EH, although all EHS cannot be considered as MH.

Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study

BackgroundMultiple sclerosis (MS), a common neurodegenerative disease, has well-described associations with quality of life (QoL) impairment. QoL changes found in longitudinal studies are difficult to interpret due to the potential response shift (RS) corresponding to respondents’ changing standards, values, and conceptualization of QoL. This study proposes to test the capacity of Random Forest (RF) for detecting RS reprioritization as the relative importance of QoL domains’ changes over time.MethodsThis was a longitudinal observational study. The main inclusion criteria were patients 18 years old or more with relapsing-remitting multiple sclerosis. Every 6 months up to month 24, QoL was recorded using generic and MS-specific questionnaires (MusiQoL and SF-36). At 24 months, individuals were divided into two ‘disability change’ groups: worsened and not-worsened patients. The RF method was performed based on Breiman’s description. Analyses were performed to determine which QoL scores of SF-36 predicted the MusiQoL index. The average variable importance (AVI) was estimated.ResultsA total of 417 (79.6%) patients were defined as not-worsened and 107 (20.4%) as worsened. A clear RS was identified in worsened patients. While the mental score AVI was almost one third higher than the physical score AVI at 12 months, it was 1.5 times lower at 24 months.ConclusionThis work confirms that the RF method offers a useful statistical approach for RS detection. How to integrate the RS in the interpretation of QoL scores remains a challenge for future research.Trial registrationClinicalTrials.gov identifier: NCT00702065

Muscle Quantitative MR Imaging and Clustering Analysis in Patients with Facioscapulohumeral Muscular Dystrophy Type 1

Background Facioscapulohumeral muscular dystrophy type 1 (FSHD1) is the third most common inherited muscular dystrophy. Considering the highly variable clinical expression and the slow disease progression, sensitive outcome measures would be of interest. Methods and Findings Using muscle MRI, we assessed muscular fatty infiltration in the lower limbs of 35 FSHD1 patients and 22 healthy volunteers by two methods: a quantitative imaging (qMRI) combined with a dedicated automated segmentation method performed on both thighs and a standard T1-weighted four-point visual scale (visual score) on thighs and legs. Each patient had a clinical evaluation including manual muscular testing, Clinical Severity Score (CSS) scale and MFM scale. The intramuscular fat fraction measured using qMRI in the thighs was significantly higher in patients (21.9 ± 20.4%) than in volunteers (3.6 ± 2.8%) (p<0.001). In patients, the intramuscular fat fraction was significantly correlated with the muscular fatty infiltration in the thighs evaluated by the mean visual score (p<0.001). However, we observed a ceiling effect of the visual score for patients with a severe fatty infiltration clearly indicating the larger accuracy of the qMRI approach. Mean intramuscular fat fraction was significantly correlated with CSS scale (p≤0.01) and was inversely correlated with MMT score, MFM subscore D1 (p≤0.01) further illustrating the sensitivity of the qMRI approach. Overall, a clustering analysis disclosed three different imaging patterns of muscle involvement for the thighs and the legs which could be related to different stages of the disease and put forth muscles which could be of interest for a subtle investigation of the disease progression and/or the efficiency of any therapeutic strategy. Conclusion The qMRI provides a sensitive measurement of fat fraction which should also be of high interest to assess disease progression and any therapeutic strategy in FSHD1 patients.

Exploring the Response Shift Effect on the Quality of Life of Patients with Schizophrenia An Application of the Random Forest Method

Background. Interpretation of quality of life (QoL) scores over time can be difficult because of possible changes in internal standards, values, and conceptualization of QoL by individuals. This effect is called a response shift (RS). The purpose of this study was to examine whether an RS effect occurred over a 24-mo period in patients who were suffering from schizophrenia. Methods. The random forest method was applied to detect any RS reprioritization in a multicenter cohort study. QoL was recorded using a generic questionnaire (SF36) at baseline (T0), 12 mo (T12), and 24 mo (T24). Patients were categorized into 3 groups based on psychotic symptoms and relapse (stable, improved, and worsened groups) from their clinical profiles. The random forest method was performed to predict the General Health score of the SF36 from the other QoL domain scores of the SF36. We estimated the average variable importance of the QoL domain for each of the 3 groups. Results. A total of 124 (53.2%) patients were defined as stable, 59 (25.3%) as improved, and 50 (21.5%) as worsened. Among the stable group, the Social Functioning domain became more important over time. Of those classified as improved, the Mental Health domain became more important over time, while the Vitality domain became less important. Among those in the group who worsened, the Mental Health domain became less important while the Vitality and Bodily Pain domains became more important. Conclusions. Our study identified differential RS reprioritization among patients with different clinical profiles. Further work is needed to determine whether RS should be interpreted as a measurement bias or as an effect integrated in a true change.

Defining Quality of Life Levels to Enhance Clinical Interpretation in Multiple Sclerosis: Application of a Novel Clustering Method

Background: To enhance the use of quality of life (QoL) measures in clinical practice, it is pertinent to help clinicians interpret QoL scores. Objective: The aim of this study was to define clusters of QoL levels from a specific questionnaire (MusiQoL) for multiple sclerosis (MS) patients using a new method of interpretable clustering based on unsupervised binary trees and to test the validity regarding clinical and functional outcomes. Methods: In this international, multicenter, cross-sectional study, patients with MS were classified using a hierarchical top-down method of Clustering using Unsupervised Binary Trees. The clustering tree was built using the 9 dimension scores of the MusiQoL in 2 stages, growing and tree reduction (pruning and joining). A 3-group structure was considered, as follows: “high,” “moderate,” and “low” QoL levels. Clinical and QoL data were compared between the 3 clusters. Results: A total of 1361 patients were analyzed: 87 were classified with “low,” 1173 with “moderate,” and 101 with “high” QoL levels. The clustering showed satisfactory properties, including repeatability (using bootstrap) and discriminancy (using factor analysis). The 3 clusters consistently differentiated patients based on sociodemographic and clinical characteristics, and the QoL scores were assessed using a generic questionnaire, ensuring the clinical validity of the clustering. Conclusions: The study suggests that Clustering using Unsupervised Binary Trees is an original, innovative, and relevant classification method to define clusters of QoL levels in MS patients.

Statistical challenges of quality of life and cancer: new avenues for future research

Statistical modeling conference on the quality of life measurements of the French National Platform of Quality of Life and Cancer Faculty of Science in Luminy, Marseille, France, 12–13 September 2013 The French National Platform of Quality of Life and Cancer and the statistical team of the Mathematical Institute of Luminy undertook a successful first conference addressing the statistical challenges of measuring the quality of life in the field of oncology. More than 15 presentations were made over a 2-day period by the Faculty of Sciences in Luminy. The conference managed to assemble participants from different disciplines, such as mathematics and statistics, public health, epidemiology and psychology, to debate the key statistical and methodological issues of quality of life measurement and analysis. Three main topics were covered in this conference: the treatment of missing data, the development of item banking and computerised adaptive testing and the detection/understanding of response shift.

Clustering nominal data using unsupervised binary decision trees

An extension of clustering using binary decision trees (CUBT) is presented for nominal data.New heuristics are given for tuning the parameters of CUBT.CUBT outperforms many of the existing approaches for nominal datasets.The tree structure helps for the interpretation of the obtained clusters.The method usable for direct prediction.The method may be used with parallel computing and thus for Big data. In this work, we propose an extension of CUBT (clustering using unsupervised binary trees) to nominal data. For this purpose, we primarily use heterogeneity criteria and dissimilarity measures based on mutual information, entropy and Hamming distance. We show that for this type of data, CUBT outperforms most of the existing methods. We also provide and justify some guidelines and heuristics to tune the parameters in CUBT. Extensive comparisons are done with other well known approaches using simulations, and two examples of real datasets applications are given.

Random average shifted histograms

A new density estimator called RASH, for Random Average Shifted Histogram, obtained by averaging several histograms as proposed in average shifted histograms, is presented. The principal difference between the two methods is that in RASH each histogram is built over a grid with random shifted breakpoints. The asymptotic behavior of this estimator is established for the one-dimensional case and its performance through several simulations is analyzed. RASH is compared to several classic density estimators and to some recent ensemble methods. Although RASH does not always outperform the other methods, it is very simple to implement, being also more intuitive. The two dimensional case is also analyzed empirically.

Modernizing quality of life assessment: development of a multidimensional computerized adaptive questionnaire for patients with schizophrenia

ObjectiveQuality of life (QoL) is still assessed using paper-based and fixed-length questionnaires, which is one reason why QoL measurements have not been routinely implemented in clinical practice. Providing new QoL measures that combine computer technology with modern measurement theory may enhance their clinical use. The aim of this study was to develop a QoL multidimensional computerized adaptive test (MCAT), the SQoL-MCAT, from the fixed-length SQoL questionnaire for patients with schizophrenia.MethodsIn this multicentre cross-sectional study, we collected sociodemographic information, clinical characteristics (i.e., duration of illness, the PANSS, and the Calgary Depression Scale), and quality of life (i.e., SQoL). The development of the SQoL-CAT was divided into three stages: (1) multidimensional item response theory (MIRT) analysis, (2) multidimensional computerized adaptive test (MCAT) simulations with analyses of accuracy and precision, and (3) external validity.ResultsFive hundred and seventeen patients participated in this study. The MIRT analysis found that all items displayed good fit with the multidimensional graded response model, with satisfactory reliability for each dimension. The SQoL-MCAT was 39% shorter than the fixed-length SQoL questionnaire and had satisfactory accuracy (levels of correlation >0.9) and precision (standard error of measurement <0.55 and root mean square error <0.3). External validity was confirmed via correlations between the SQoL-MCAT dimension scores and symptomatology scores.ConclusionThe SQoL-MCAT is the first computerized adaptive QoL questionnaire for patients with schizophrenia. Tailored for patient characteristics and significantly shorter than the paper-based version, the SQoL-MCAT may improve the feasibility of assessing QoL in clinical practice.

Nonparametric comparison of several transformations of distribution functions

This paper considers two random variables such that there exists a monotone transformation between their distribution functions. The problem is to test if there is a change in this transformation when these two variables are observed under K different conditions. The approach considered is a CUSUM test based on the cumulative sum of the residuals and a test statistic is proposed for testing the equality of the K transformations. The asymptotic distribution of the test statistic is derived and its finite sample properties are examined by simulation. As a further illustration, an analysis of a real data set concerning the impact of the financial crisis of September 2008 is given.