Left Ventricular Trabeculations at Cardiac MRI: Reference Ranges and Association with Cardiovascular Risk Factors in UK Biobank

Background The extent of left ventricular (LV) trabeculation and its relationship with cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK Biobank cardiac MRI scans to (a) assess the association between individual characteristics and CV risk factors and trabeculated LV mass (LVM) and (b) establish normal reference ranges in a selected group of healthy UK Biobank participants. Materials and Methods In this cross-sectional secondary analysis, prospectively collected data from the UK Biobank (2006 to 2010) were retrospectively analyzed. Automated segmentation of trabeculations was performed using a deep learning algorithm. After excluding individuals with known CV diseases, White adults without CV risk factors (reference group) and those with preexisting CV risk factors (hypertension, hyperlipidemia, diabetes mellitus, or smoking) (exposed group) were compared. Multivariable regression models, adjusted for potential confounders (age, sex, and height), were fitted to evaluate the associations between individual characteristics and CV risk factors and trabeculated LVM. Results Of 43 038 participants (mean age, 64 years ± 8 [SD]; 22 360 women), 28 672 individuals (mean age, 66 years ± 7; 14 918 men) were included in the exposed group, and 7384 individuals (mean age, 60 years ± 7; 4729 women) were included in the reference group. Higher body mass index (BMI) (β = 0.66 [95% CI: 0.63, 0.68]; P < .001), hypertension (β = 0.42 [95% CI: 0.36, 0.48]; P < .001), and higher physical activity level (β = 0.15 [95% CI: 0.12, 0.17]; P < .001) were associated with higher trabeculated LVM. In the reference group, the median trabeculated LVM was 6.3 g (IQR, 4.7-8.5 g) for men and 4.6 g (IQR, 3.4-6.0 g) for women. Median trabeculated LVM decreased with age for men from 6.5 g (IQR, 4.8-8.7 g) at age 45-50 years to 5.9 g (IQR, 4.3-7.8 g) at age 71-80 years (P = .03). Conclusion Higher trabeculated LVM was observed with hypertension, higher BMI, and higher physical activity level. Age- and sex-specific reference ranges of trabeculated LVM in a healthy middle-aged White population were established. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kawel-Boehm in this issue.

Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data

OBJECTIVE

This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data.

METHODS

The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors. The unstructured data was used to fine-tune a pre-trained Bio Clinical BERT Transformer Deep Learning model. The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dirichlet Allocation. The study used the free and publicly available Medical Information Mart for Intensive Care (MIMIC) III database, on the open AutoML Library AutoGluon. Performance is evaluated with respect to two types of random classifiers, used as baselines.

RESULTS

The best model from structured data demonstrates high performance (ROC AUC = 0.944, PRC AUC = 0.655) with limited interpretability, where the most important predictors of prolonged LOS are the level of blood urea nitrogen and of platelets. The Transformer model displays a good but lower performance (ROC AUC = 0.842, PRC AUC = 0.375) with a richer array of interpretability by providing more specific in-hospital factors including procedures, conditions, and medical history. The best model trained on mixed data satisfies both a high level of performance (ROC AUC = 0.963, PRC AUC = 0.746) and a much larger scope in interpretability including pathologies of the intestine, the colon, and the blood; infectious diseases, respiratory problems, procedures involving sedation and intubation, and vascular surgery.

CONCLUSIONS

Our results outperform most of the state-of-the-art models in LOS prediction both in terms of performance and of interpretability. Data fusion between structured and unstructured text data may significantly improve performance and interpretability.

Machine-learning prediction for hospital length of stay using a French medico-administrative database

INTRODUCTION

Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS.

METHODS

Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90^th percentile (14 days). Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the area under the ROC curve (AUC).

RESULTS

Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values <0.0001). The performance of the RF, GB and NN models (AUC ranged from 0.808 to 0.810) was superior to that of the LR model (AUC = 0.795); all p-values <0.0001. In contrast, LR was superior to CART (AUC = 0.786), p < 0.0001. The variable most predictive of the PLOS was the destination of the patient after hospitalization to other institutions. The typical clinical profile of these patients (17.5% of the sample) was the elderly patient, admitted in emergency, for a trauma, a neurological or a cardiovascular pathology, more often institutionalized, with more comorbidities notably mental health problems, dementia and hemiplegia.

DISCUSSION

The integration of ML, particularly the GB algorithm, may be useful for health-care professionals and bed managers to better identify patients at risk of PLOS. These findings underscore the need to strengthen hospitals through targeted allocation to meet the needs of an aging population.

Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19

Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed tomography (LDCT) and investigate the link between EAT and COVID-19 clinical outcomes. Methods: This monocentric retrospective study included 353 patients: 95 for training, 20 for testing, and 238 for prognosis evaluation. EAT segmentation was obtained after thresholding on a manually segmented pericardial volume. The model was evaluated with Dice coefficient (DSC), inter-and intraobserver reproducibility, and clinical measures. Uni-and multi-variate analyzes were conducted to assess the prognosis value of the EAT volume, EAT extent, and lung lesion extent on clinical outcomes, including hospitalization, oxygen therapy, intensive care unit admission and death. Results: The mean DSC for EAT volumes was 0.85 ± 0.05. For EAT volume, the mean absolute error was 11.7 ± 8.1 cm3 with a non-significant bias of −4.0 ± 13.9 cm3 and a correlation of 0.963 with the manual measures (p < 0.01). The multivariate model providing the higher AUC to predict adverse outcome include both EAT extent and lung lesion extent (AUC = 0.805). Conclusions: A DL algorithm was developed and evaluated to obtain reproducible and precise EAT segmentation on LDCT. EAT extent in association with lung lesion extent was associated with adverse clinical outcomes with an AUC = 0.805.

Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT

Objectives

1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.

Methods

This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy.

Results

The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001).

Conclusions

A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.

Medical image segmentation automatic quality control: A multi-dimensional approach

In clinical applications, using erroneous segmentations of medical images can have dramatic consequences. Current approaches dedicated to medical image segmentation automatic quality control do not predict segmentation quality at slice-level (2D), resulting in sub-optimal evaluations. Our 2D-based deep learning method simultaneously performs quality control at 2D-level and 3D-level for cardiovascular MR image segmentations. We compared it with 3D approaches by training both on 36,540 (2D) / 3842 (3D) samples to predict Dice Similarity Coefficients (DSC) for 4 different structures from the left ventricle, i.e., trabeculations (LVT), myocardium (LVM), papillary muscles (LVPM) and blood (LVC). The 2D-based method outperformed the 3D method. At the 2D-level, the mean absolute errors (MAEs) of the DSC predictions for 3823 samples, were 0.02, 0.02, 0.05 and 0.02 for LVM, LVC, LVT and LVPM, respectively. At the 3D-level, for 402 samples, the corresponding MAEs were 0.02, 0.01, 0.02 and 0.04. The method was validated in a clinical practice evaluation against semi-qualitative scores provided by expert cardiologists for 1016 subjects of the UK BioBank. Finally, we provided evidence that a multi-level QC could be used to enhance clinical measurements derived from image segmentations.

Deep Learning-based Automated Segmentation of Left Ventricular Trabeculations and Myocardium on Cardiac MR Images: A Feasibility Study

Purpose

To develop and evaluate a complete deep learning pipeline that allows fully automated end-diastolic left ventricle (LV) cardiac MRI segmentation, including trabeculations and automatic quality control of the predicted segmentation.

Materials and Methods

This multicenter retrospective study includes training, validation, and testing datasets of 272, 27, and 150 cardiac MR images, respectively, collected between 2012 and 2018. The reference standard was the manual segmentation of four LV anatomic structures performed on end-diastolic short-axis cine cardiac MRI: LV trabeculations, LV myocardium, LV papillary muscles, and the LV blood cavity. The automatic pipeline was composed of five steps with a DenseNet architecture. Intraobserver agreement, interobserver agreement, and interaction time were recorded. The analysis includes the correlation between the manual and automated segmentation, a reproducibility comparison, and Bland-Altman plots.

Results

The automated method achieved mean Dice coefficients of 0.96 ± 0.01 (standard deviation) for LV blood cavity, 0.89 ± 0.03 for LV myocardium, and 0.62 ± 0.08 for LV trabeculation (mean absolute error, 3.63 g ± 3.4). Automatic quantification of LV end-diastolic volume, LV myocardium mass, LV trabeculation, and trabeculation mass-to-total myocardial mass (TMM) ratio showed a significant correlation with the manual measures (r = 0.99, 0.99, 0.90, and 0.83, respectively; all P < .01). On a subset of 48 patients, the mean Dice value for LV trabeculation was 0.63 ± 0.10 or higher compared with the human interobserver (0.44 ± 0.09; P < .01) and intraobserver measures (0.58 ± 0.09; P < .01). Automatic quantification of the trabeculation mass-to-TMM ratio had a higher correlation (0.92) compared with the intra- and interobserver measures (0.74 and 0.39, respectively; both P < .01).

Conclusion

Automated deep learning framework can achieve reproducible and quality-controlled segmentation of cardiac trabeculations, outperforming inter- and intraobserver analyses.Supplemental material is available for this article.© RSNA, 2020.

Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database

Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work was to develop a ML model to predict 30-day all-cause rehospitalizations based on the French hospital medico-administrative database.This was a retrospective cohort study of all discharges in the year 2015 from acute-care inpatient hospitalizations in a tertiary-care university center comprising 4 French hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization. Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB), and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the H-measure and the area under the ROC curve (AUC).Our analysis included 118,650 hospitalizations, of which 4127 (3.5%) led to rehospitalizations via emergency departments. The RF model was the most performant model according to the H-measure (0.29) and the AUC (0.79). The performances of the RF, GB and NN models (H-measures ranged from 0.18 to 0. 29, AUC ranged from 0.74 to 0.79) were better than those of the LR model (H-measure = 0.18, AUC = 0.74); all P values <.001. In contrast, LR was superior to CART (H-measure = 0.16, AUC = 0.70), P < .0001.The use of ML may be an alternative to regression models to predict health outcomes. The integration of ML, particularly the RF algorithm, in the prediction of unplanned rehospitalization may help health service providers target patients at high risk of rehospitalizations and propose effective interventions at the hospital level.

OLOGRAM: Determining significance of total overlap length between genomic regions sets

MOTIVATION

Various bioinformatics analyses provide sets of genomic coordinates of interest. Whether two such sets possess a functional relation is a frequent question. This is often determined by interpreting the statistical significance of their overlaps. However, only few existing methods consider the lengths of the overlap, and they do not provide a resolutive p-value.

RESULTS

Here, we introduce OLOGRAM, which performs overlap statistics between sets of genomic regions described in BEDs or GTF. It uses Monte Carlo simulation, taking into account both the distributions of region and inter-region lengths, to fit a negative binomial model of the total overlap length. Exclusion of user-defined genomic areas during the shuffling is supported.

AVAILABILITY

This tool is available through the command line interface of the pygtftk toolkit. It has been tested on Linux and OSX and is available on Bioconda and from https://github.com/dputhier/pygtftk under the GNU GPL license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computerized adaptive testing with decision regression trees: an alternative to item response theory for quality of life measurement in multiple sclerosis

BACKGROUND

The aim of this study was to propose an alternative approach to item response theory (IRT) in the development of computerized adaptive testing (CAT) in quality of life (QoL) for patients with multiple sclerosis (MS). This approach relied on decision regression trees (DRTs). A comparison with IRT was undertaken based on precision and validity properties.

MATERIALS AND METHODS

DRT- and IRT-based CATs were applied on items from a unidi-mensional item bank measuring QoL related to mental health in MS. The DRT-based approach consisted of CAT simulations based on a minsplit parameter that defines the minimal size of nodes in a tree. The IRT-based approach consisted of CAT simulations based on a specified level of measurement precision. The best CAT simulation showed the lowest number of items and the best levels of precision. Validity of the CAT was examined using sociodemographic, clinical and QoL data.

RESULTS

CAT simulations were performed using the responses of 1,992 MS patients. The DRT-based CAT algorithm with minsplit = 10 was the most satisfactory model, superior to the best IRT-based CAT algorithm. This CAT administered an average of nine items and showed satisfactory precision indicators (R = 0.98, root mean square error [RMSE] = 0.18). The DRT-based CAT showed convergent validity as its score correlated significantly with other QoL scores and showed satisfactory discriminant validity.

CONCLUSION

We presented a new adaptive testing algorithm based on DRT, which has equivalent level of performance to IRT-based approach. The use of DRT is a natural and intuitive way to develop CAT, and this approach may be an alternative to IRT.

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

OBJECTIVE

Quality 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.

METHODS

In 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.

RESULTS

Five 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.

CONCLUSION

The 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.

A Multidimensional Computerized Adaptive Short-Form Quality of Life Questionnaire Developed and Validated for Multiple Sclerosis: The MusiQoL-MCAT

The aim was to develop a multidimensional computerized adaptive short-form questionnaire, the MusiQoL-MCAT, from a fixed-length QoL questionnaire for multiple sclerosis.A total of 1992 patients were enrolled in this international cross-sectional study. The development of the MusiQoL-MCAT was based on the assessment of between-items MIRT model fit followed by real-data simulations. The MCAT algorithm was based on Bayesian maximum a posteriori estimation of latent traits and Kullback-Leibler information item selection. We examined several simulations based on a fixed number of items. Accuracy was assessed using correlations (r) between initial IRT scores and MCAT scores. Precision was assessed using the standard error measurement (SEM) and the root mean square error (RMSE).The multidimensional graded response model was used to estimate item parameters and IRT scores. Among the MCAT simulations, the 16-item version of the MusiQoL-MCAT was selected because the accuracy and precision became stable with 16 items with satisfactory levels (r ≥ 0.9, SEM ≤ 0.55, and RMSE ≤ 0.3). External validity of the MusiQoL-MCAT was satisfactory.The MusiQoL-MCAT presents satisfactory properties and can individually tailor QoL assessment to each patient, making it less burdensome to patients and better adapted for use in clinical practice.

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.

How to interpret multidimensional quality of life questionnaires for patients with schizophrenia?

PURPOSE

The classification of patients into distinct categories of quality of life (QoL) levels may be useful for clinicians to interpret QoL scores from multidimensional questionnaires. The aim of this study had been to define clusters of QoL levels from a specific multidimensional questionnaire (SQoL18) for patients with schizophrenia by using a new method of interpretable clustering and to test its validity regarding socio-demographic, clinical, and QoL information.

METHODS

In this multicentre cross-sectional study, patients with schizophrenia have been classified using a hierarchical top-down method called clustering using unsupervised binary trees (CUBT). A three-group structure has been employed to define QoL levels as "high", "moderate", or "low". Socio-demographic, clinical, and QoL data have been compared between the three clusters to ensure their clinical relevance.

RESULTS

A total of 514 patients have been analysed: 78 are classified as "low", 265 as "moderate", and 171 as "high". The clustering shows satisfactory statistical properties, including reproducibility (using bootstrap analysis) and discriminancy (using factor analysis). The three clusters consistently differentiate patients. As expected, individuals in the "high" QoL level cluster report the lowest scores on the Positive and Negative Syndrome Scale (p = 0.01) and the Calgary Depression Scale (p < 0.01), and the highest scores on the Global Assessment of Functioning (p < 0.03), the SF36 (p < 0.01), the EuroQol (p < 0.01), and the Quality of Life Inventory (p < 0.01).

CONCLUSION

Given the ease with which this method can be applied, classification using CUBT may be useful for facilitating the interpretation of QoL scores in clinical practice.

Development of a cross-cultural item bank for measuring quality of life related to mental health in multiple sclerosis patients

OBJECTIVE

Quality of life (QoL) measurements are considered important outcome measures both for research on multiple sclerosis (MS) and in clinical practice. Computerized adaptive testing (CAT) can improve the precision of measurements made using QoL instruments while reducing the burden of testing on patients. Moreover, a cross-cultural approach is also necessary to guarantee the wide applicability of CAT. The aim of this preliminary study was to develop a calibrated item bank that is available in multiple languages and measures QoL related to mental health by combining one generic (SF-36) and one disease-specific questionnaire (MusiQoL).

METHODS

Patients with MS were enrolled in this international, multicenter, cross-sectional study. The psychometric properties of the item bank were based on classical test and item response theories and approaches, including the evaluation of unidimensionality, item response theory model fitting, and analyses of differential item functioning (DIF). Convergent and discriminant validities of the item bank were examined according to socio-demographic, clinical, and QoL features.

RESULTS

A total of 1992 patients with MS and from 15 countries were enrolled in this study to calibrate the 22-item bank developed in this study. The strict monotonicity of the Cronbach's alpha curve, the high eigenvalue ratio estimator (5.50), and the adequate CFA model fit (RMSEA = 0.07 and CFI = 0.95) indicated that a strong assumption of unidimensionality was warranted. The infit mean square statistic ranged from 0.76 to 1.27, indicating a satisfactory item fit. DIF analyses revealed no item biases across geographical areas, confirming the cross-cultural equivalence of the item bank. External validity testing revealed that the item bank scores correlated significantly with QoL scores but also showed discriminant validity for socio-demographic and clinical characteristics.

CONCLUSION

This work demonstrated satisfactory psychometric characteristics for a QoL item bank for MS in multiple languages. This work may offer a common measure for the assessment of QoL in different cultural contexts and for international studies conducted on MS.

Exploring the Response Shift Effect on the Quality of Life of Patients with Schizophrenia

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.

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

Background

Multiple 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.

Methods

This 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.

Results

A 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.

Conclusion

This 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 registration

ClinicalTrials.gov identifier: NCT00702065

Multimodal MR imaging (diffusion, perfusion, and spectroscopy): is it possible to distinguish oligodendroglial tumor grade and 1p/19q codeletion in the pretherapeutic diagnosis?

BACKGROUND AND PURPOSE

Pretherapeutic determination of tumor grade and genotype in grade II and III oligodendroglial tumors is clinically important but is still challenging. Tumor grade and 1p/19q status are currently the 2 most important factors in therapeutic decision making for patients with these tumors. Histopathology and cMRI studies are still limited in some cases. In the present study, we were interested in determining whether the combination of PWI, DWI, and MR spectroscopy could help distinguish oligodendroglial tumors according to their histopathologic grade and genotype.

MATERIALS AND METHODS

We retrospectively reviewed 50 adult patients with grade II and III oligodendrogliomas and oligoastrocytomas who had DWI, PWI, and MR spectroscopy at short and long TE data and known 1p/19q status. Univariate analyses and multivariate random forest models were performed to determine which criteria could differentiate between grades and genotypes.

RESULTS

ADC, rCBV, rCBF, and rK2 were significantly different between grade II and III oligodendroglial tumors. DWI, PWI, and MR spectroscopy showed no significant difference between tumors with and without 1p/19q loss. Separation between tumor grades and genotypes with cMRI alone showed 31% and 48% misclassification rates, respectively. Multimodal MR imaging helps to determine tumor grade and 1p/19q genotype more accurately (misclassification rates of 17% and 40%, respectively).

CONCLUSIONS

Although multimodal investigation of oligodendroglial tumors has a lower contribution to 1p/19q genotyping compared with cMRI alone, it greatly improves the accuracy of grading of these neoplasms. Use of multimodal MR imaging could thus provide valuable information that may assist clinicians in patient preoperative management and treatment decision making.

The disparate nature of "intergenic" polyadenylation sites

The termination of mature eukaryotic mRNAs occurs at specific polyadenylation sites located downstream from stop codons in the 3'-untranslated region (UTR). An accurate delineation of these sites is essential for the study of 3'-UTR-based gene regulation and for the design of pertinent probes for transcriptome analysis. Although typical poly(A) sites are located between 0 and 2 kb from the stop codon, EST sequence analyses have identified sites located at unexpectedly long ranges (5-10 kb) in a number of genes. Here we perform a complete mapping of EST and full-length cDNA sequences on the mouse and human genome to observe putative poly(A) sites extending beyond annotated 3'-ends and into the intergenic regions. We introduce several quality parameters for poly(A) site prediction and train a classification tree to associate P-values to predicted sites. We observe a higher than background level of high-scoring sites up to 12-15 kb past the stop codon, both in human and mouse. This leads to an estimate of about 5000 human genes having unreported 3'-end extensions and about 3500 novel polyadenylated transcripts lying in present "intergenic" regions. These high-scoring, long-range poly(A) sites corresponding to novel transcripts and gene extensions should be incorporated into current human and mouse gene repositories.

[Prediction of transcription and genomic sequences]

Technological developments have enhanced DNA sequencing at genomic scale. On the basis of the resulting sequences, computational biologists now attempt to localise the most important functional regions, starting with genes, but also importantly the regulatory motifs and conditions controlling their expression. In a recent paper published in Cell, M.A. Beer and S. Tavazoie report the results obtained by combining statistical classifications (clustering) of transcriptome data (DNA chips), software for the discovery of cis-regulatory patterns, together with a probabilistic learning method to infer regulatory rules tentatively accounting for the observed transcriptional profiles.

Comparative analysis of in vitro contracture tests with ryanodine and a combination of ryanodine with either halothane or caffeine: a comparative investigation in malignant hyperthermia

BACKGROUND

The diagnosis of susceptibility to malignant hyperthermia (MH) is currently performed on muscle biopsies subjected to halothane-caffeine in vitro contracture tests (IVCTs). There is a consensus on our need to improve the diagnostic potential of IVCTs if we are to maximize the information available for research and diagnosis in MH. This study was designed as a pilot comparative study and we aimed at comparing the ryanodine test and new tests using a combination of ryanodine, halothane and caffeine.

METHODS

One hundred and thirty-two subjects (52 MHS and 80 MHN) were included in this study and new IVCTs were performed in additional muscle biopsy specimens. The contracture time-course was compared considering the onset time of contracture (OT) and the time to reach a 10 mN contracture (10T). Cut-off values were determined using ROC analyses.

RESULTS

For the ryanodine test, sensitivity and specificity calculated for OT were 84.6% and 90.4%, respectively, and were better than those obtained using 10T. Combined tests using either caffeine and ryanodine or halothane and ryanodine did provide higher sensitivities (from 85.3 to 93.9%). A better specificity was only observed for the IVC tests combining halothane (cumulated) and caffeine both with ryanodine (93.9% for both). The largest sensitivity was observed when halothane was used as a bolus and combined with ryanodine. The specificity was always larger with the combined tests as compared to the test using ryanodine alone (from 79.1 to 90.9%). This superiority was confirmed, at least in part, when comparing genetic investigations and the results of new tests in a subgroup of subjects.

CONCLUSIONS

This pilot study showed a clear diagnostic potential for new IVC tests combining halothane, the triggering agent of MH, and ryanodine acting at the calcium release channel, and should be considered as a first step in the investigation of combined tests.

Metabolic determinants of the onset of acidosis in exercising human muscle: a 31P-MRS study

Onset of intracellular acidosis during muscular exercise has been generally attributed to activation or hyperactivation of nonoxidative ATP production but has not been analyzed quantitatively in terms of H(+) balance, i.e., production and removal mechanisms. To address this issue, we have analyzed the relation of intracellular acidosis to H(+) balance during exercise bouts in seven healthy subjects. Each subject performed a 6-min ramp rhythmic exercise (finger flexions) at low frequency (LF, 0.47 Hz), leading to slight acidosis, and at high frequency (HF, 0.85 Hz), inducing a larger acidosis. Metabolic changes were recorded using (31)P-magnetic resonance spectroscopy. Onset of intracellular acidosis was statistically identified after 3 and 4 min of exercise for HF and LF protocols, respectively. A detailed investigation of H(+) balance indicated that, for both protocols, nonoxidative ATP production preceded a change in pH. For HF and LF protocols, H(+) consumption through the creatine kinase equilibrium was constant in the face of increasing H(+) generation and efflux. For both protocols, changes in pH were not recorded as long as sources and sinks for H(+) approximately balanced. In contrast, a significant acidosis occurred after 4 min of LF exercise and 3 min of HF exercise, whereas the rise in H(+) generation exceeded the rise in H(+) efflux at a nearly constant H(+) uptake associated with phosphocreatine breakdown. We have clearly demonstrated that intracellular acidosis in exercising muscle does not occur exclusively as a result of nonoxidative ATP production but, rather, reflects changes in overall H(+) balance.

Intermittent sand filtration for wastewater treatment in rural areas of the Middle East--a pilot study

This paper concentrates on Intermittent Sand Filtration (ISF) as a polishing stage for effluent from a facultative pond. During the three-year research program, the system operated with an influent flow-rate of 500-1,000 L/day and an average BOD concentration of 200-400 hydraulic and BOD loadings of 110-200 L/m2/day and 20-40 gBOD/m2/day, respectively. Flow to the ISF was applied intermittently with a different number of doses in each run. In addition, the effects of the frequency and the duration of rest periods (no feeding) were studied. Removal of 90-95% of BOD and 75-90% of COD and TSS was achieved consistently throughout the study period. Elevated levels of nitrification were observed with 95-100% removal of NH3. The ISF performed best when fed with 5-10 doses/day. Reducing the daily number of doses to 3/day at the same hydraulic loading rate resulted in a 20-30% reduction in removal efficiency. The 2-4 week rest period had no effect on the biological activity in the subsequent run. However, rest periods of more than 30 days were found to negatively affect removal efficiency.

Citrulline/malate promotes aerobic energy production in human exercising muscle

BACKGROUND

Previous studies have shown an antiasthenic effect of citrulline/malate (CM) but the mechanism of action at the muscular level remains unknown.

OBJECTIVE

To investigate the effects of CM supplementation on muscle energetics.

METHODS

Eighteen men complaining of fatigue but with no documented disease were included in the study. A rest-exercise (finger flexions)-recovery protocol was performed twice before (D-7 and D0), three times during (D3, D8, D15), and once after (D22) 15 days of oral supplementation with 6 g/day CM. Metabolism of the flexor digitorum superficialis was analysed by (31)P magnetic resonance spectroscopy at 4.7 T.

RESULTS

Metabolic variables measured twice before CM ingestion showed no differences, indicating good reproducibility of measurements and no learning effect from repeating the exercise protocol. CM ingestion resulted in a significant reduction in the sensation of fatigue, a 34% increase in the rate of oxidative ATP production during exercise, and a 20% increase in the rate of phosphocreatine recovery after exercise, indicating a larger contribution of oxidative ATP synthesis to energy production. Considering subjects individually and variables characterising aerobic function, extrema were measured after either eight or 15 days of treatment, indicating chronological heterogeneity of treatment induced changes. One way analysis of variance confirmed improved aerobic function, which may be the result of an enhanced malate supply activating ATP production from the tricarboxylic acid cycle through anaplerotic reactions.

CONCLUSION

The changes in muscle metabolism produced by CM treatment indicate that CM may promote aerobic energy production.

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 (31)P 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.

Forecasting physicochemical variables by a classification tree method. Application to the Berre Lagoon (south France)

The dynamics of the "Etang de Berre", a brackish lagoon situated close to the French Mediterranean sea coast, is strongly disturbed by freshwater inputs coming from an hydroelectric power station. The system dynamics has been described as a sequence of daily typical states from a set of physicochemical variables such as temperature, salinity and dissolved oxygen rates collected over three years by an automatic sampling station. Each daily pattern summarizes the evolution, hour by hour of the physicochemical variables. This article presents results of forecasts of the states of the system subjected to the simultaneous effects of meteorological conditions and freshwater releases. We recall the main step of the classification tree method used to build up the predictive model (Classification and Regression Trees, Breiman et al., 1984) and we propose a transfer procedure in order to test the stability of the model. Results obtained on the Etang de Berre data set allow us to describe and predict the effects of the environmental variables on the system dynamics with a margin of error. The transfer procedure applied after the tree building process gives a maximum gain in prediction accuracy of about 15%.