Statistical tools for the analysis of event-related potentials in electroencephalograms

Since its first use in human in 1929, the electroencephalogram (EEG) has become one of the most important diagnostic tool in clinical neurophysiology. However, their use in clinical studies is limited because the huge quantity of collected information is complicated to treat. Indeed, it is very difficult to have an overall picture of this multivariate problem. In addition to the impressive quantity of data to be treated, an intrinsic problem with electroencephalograms is that the signals are "contaminated" by body signals not directly related to cerebral activity. However, these signals do not interest us directly to evaluate treatment effect on the brain. Removing these signals known as "parasitic noise" from electroencephalograms is a difficult task. We use clinical data kindly made available by the pharmaceutical company Eli Lilly (Lilly Clinical Operations S.A., Louvain-la-Neuve, Belgium). Particular types of analyses were already carried out on these data, most based on frequency bands. They mainly confirmed the enormous potential of EEG in clinical studies without much insight in the understanding of treatment effect on the brain. The aim of this thesis is to propose and evaluate a panel of statistical techniques to clean and to analyze electroencephalograms. The first presented tool enables to align curves such as selected parts of EEGs before any further statistical treatment. Indeed, when monitoring some continuous process on similar units (like patients in a clinical study), one often notices a typical pattern common to all curves but with variation both in amplitude and dynamics across curves. In particular, typical peaks could be shifted from unit to unit. This complicates the statistical analysis of sample of curves. For example, the cross-sectional average usually does not reflect a typical curve pattern: due to shifts, the signal structure is smeared or might even disappear. Another of the presented tools is based on the preliminary linear decomposition of EEGs into statistically independent signals. This decomposition provides on the one hand an effective cleaning method and on the other hand a considerable reduction of the quantity of data to be analyzed. The technique of decomposition of our signals in statistically independent signals is a well-known technique in physics primarily used to unmix sound signals. This technique is named Independent Component Analysis or ICA. The last studied tool is functional ANOVA. The analysis of longitudinal curve data is a methodological and computational challenge for statisticians. Such data are often generated in biomedical studies. Most of the time, the statistical analysis focuses on simple summary measures, thereby discarding potentially important information. We propose to model these curves using non parametric regression techniques based on splines.(STAT 3)--UCL, 200

[The epidemiology of occupational asthma in Belgium]

Introduction A national surveillance programme of occupational asthma was set up to estimate the incidence and identify the causes of this disorder in Belgium. Materials and methods The programme was based on the voluntary notification of new cases of occupational asthma by chest specialists and occupational physicians during the period 2000-2002. Results 92% of the reported cases included occupational asthma of an immunological type and 8% bronchial irritability. According to the opinion of the reporting physicians the diagnosis was considered certain in 39%, probable in 29% and possible in 32% of cases. On the basis of these notifications the mean annual incidence of occupational asthma is estimated as 23.5 cases per million workers (95% confidence interval 19.2-28.8). The most frequently incriminated substances were isocyanates (16%), cereals (12%) and latex (10%). At the time of diagnosis 38% of patients had not applied for compensation. Conclusion The results of this programme of notification of occupational asthma agree with the data available from other countries and provide information complementary to the med-ico-legal statistics

INSENODIAB Study : Determinants and characteristics of insulin dose requirements in children and adolescent with new-onset type 1 diabetes.

BEST ORAL PRESENTATION Aims In children with newly diagnosed type 1 diabetes (T1D), insulin dose regimens vary substantially. According to current ISPAD recommendations, the initial total daily dose should range from 0.7 to 1 UI/kg body weight/day. Adjusting and stabilizing this dose to achieve normal blood glucose concentration can take several days. The goals of this study were (1) to assess how patient characteristics influence insulin dose requirements and (2) to establish predictive models of those insulin requirements in newly diagnosed children with T1D. Methods INSENODIAB is a monocentric, retrospective observational study over a 7-year period from January 2013 to February 2020. Chart review was conducted for children (6 months-18 years) admitted in Cliniques universitaires Saint-Luc for a new diagnosis of T1D during the observational period. Demographics, clinical and laboratory data, including insulin dosage were collected for all patients. Univariate and multivariable linear regression models were used to examine the impact of patient variables on insulin total daily dose, using a nominal Type I error of 5% as threshold. Results Complete clinical records were available for 103 patients with median body weight of 27 kg (Q1-Q3: 18.2-39.1, Mean: 30.0 kg). Median Insulin total daily dose was 26.8 units (Q1-Q3: 18.8-48.3 units, n=103) during hospitalization and 24.5 units (Q1-Q3: 17.0-45.3 units, n=94) on the day of discharge. Median duration of hospitalization was 5 days (range 1-10 days). In multivariable analysis, the main variables found to impact optimal insulin total daily dose were age, veinous bicarbonates levels at admission, body mass index and percentage of weight loss at diagnosis. The same factors remained after adjusting the model to insulin dose per day per kilogram body weight. Conclusions In newly diagnosed children with T1D, percentage of weight loss and veinous bicarbonates levels at admission, in addition to age and body mass index, influence the Insulin total daily dose necessary to reach glycemic control. Those results helped us developing a dosing algorithm which could potentially reduce the number of days currently needed to stabilize glycemic control in children and adolescents with new-onset T1D

Epidémiologie de l'asthme professionnel en Belgique.

INTRODUCTION: A national surveillance programme of occupational asthma was set up to estimate the incidence and identify the causes of this disorder in Belgium. MATERIALS AND METHODS: The programme was based on the voluntary notification of new cases of occupational asthma by chest specialists and occupational physicians during the period 2000-2002. RESULTS: 92% of the reported cases included occupational asthma of an immunological type and 8% bronchial irritability. According to the opinion of the reporting physicians the diagnosis was considered certain in 39%, probable in 29% and possible in 32% of cases. On the basis of these notifications the mean annual incidence of occupational asthma is estimated as 23.5 cases per million workers (95% confidence interval 19.2-28.8). The most frequently incriminated substances were isocyanates (16%), cereals (12%) and latex (10%). At the time of diagnosis 38% of patients had not applied for compensation. CONCLUSION: The results of this programme of notification of occupational asthma agree with the data available from other countries and provide information complementary to the medico-legal statistics

Statistical analysis of electroencephalograms: independent component analysis of event-related potentials

Electroencephalogram (EEG) is an important diagnostic tool in clinical neurophysiology. However, EEGs are not often used in clinical studies because of intrinsic problem like the huge quantity of data or artifacts. In this paper, we shall describe statistical tools to detect and quantify the effect of drugs on the brain by the analysis of EEGs. We first use Independent Component Analysis (ICA) to detect and remove automatically artifacts from EEGs. In the second step, ICA reduces the dimension of the problem. Using data from a clinical trial, we show that eight ICA components can reconstruct more than 80 percents of the data from the twenty-eight electrodes. Some of these eight ICA components can reconstruct an interesting characteristic of the signals (an event-related potential named P300). Finally, we shall show how the analysis of these two components allow to detect and quantify a treatment effect. Lorazepam decreases the P300 peak amplitude and increases the time of occurrence of the P300 peak

Influence of the occurrence and duration of partial remission on short-term metabolic control in type 1 diabetes: the DIABHONEY pediatric study

Objective: To evaluate the residual effect of partial remission (PR) on immediate post-PR glycemic control according to its occurrence and duration in a cohort of children with type 1 diabetes mellitus (T1DM). Patients and Methods: Values of glycemic control parameters [i.e. HbA 1C , insulin dose–adjusted hemoglobin A 1C (IDAA 1C ), glycemic target–adjusted HbA 1C (GTAA 1C )] and data from glucose monitoring devices from 189 pediatric patients with new-onset type 1 diabetes were collected retrospectively from 24 months. Patients were characterized according to their remission status (PR + and PR − ). PR + patients were subdivided into three subgroups regarding PR duration [i.e. short (⩾3–⩽6 months), intermediate (>6–⩽12 months), and long PR (>12–⩽14 months)]. We compared glycemic control data from each PR + subgroup at +6 and +12 months post-PR with PR − patients at the same postdiagnosis time. Second, PR + subgroups were compared with each other. Results: PR + patients showed improved glycemic control (i.e. HbA 1C , IDAA 1C , and GTAA 1C ) at + 6 months post-PR when compared with nonremitters (PR − ), independently of the PR duration subgroups (p < 0.05). Interestingly, patients in long PR + subgroup exhibited higher positive residual effect than short PR + subgroup with lower GTAA 1C scores (p = 0.02), better time in range (TIR) (p = 0.003), less time in hypoglycemia (10.45 versus 16.13%, p = 0.03) and less glycemic variability (83.1 mg/dl versus 98.84 mg/dl, p = 0.03). No significant differences were found for glucose control between PR + and PR − patients at +12 months post-PR. Conclusion: This study supports the positive impact of PR occurrence and duration on short-term metabolic control (better HbA 1C levels, IDAA 1C and GTAA 1C scores, TIR, and less glycemic variability) with the residual effect increasing according to PR duration

The Clinical Significance of Heterogeneous Paramagnetic Rim Lesions

Multiple Sclerosis (MS) chronic active lesions (CAL), seen on susceptibility-based MRI as Paramagnetic Rim Lesions (PRL), are associated with increased clinical disability and tissue damage. However, MRI and histopathological studies reveal a substantial heterogeneity in terms of CAL associated tissue damage, with potential implications for predicting clinical outcomes