Language Regression Associated With Autistic Regression and Electroencephalographic (EEG) Abnormalities: A Prospective Study.

We report a boy, referred at 25 months following a dramatic isolated language regression antedating autistic-like symptomatology. His sleep electroencephalogram (EEG) showed persistent focal epileptiform activity over the left parietal and vertex areas never associated with clinical seizures. He was started on adrenocorticotropic hormone (ACTH) with a significant improvement in language, behavior, and in EEG discharges in rapid eye movement (REM) sleep. Later course was characterized by fluctuations/regressions in language and behavior abilities, in phase with recrudescence of EEG abnormalities prompting additional ACTH courses that led to remarkable decrease in EEG abnormalities, improvement in language, and to a lesser degree, in autistic behavior. The timely documentation of regression episodes suggesting an "atypical" autistic regression, striking therapy-induced improvement, fluctuation of symptomatology over time could be ascribed to recurrent and persisting EEG abnormalities.

Long-term outcome after cognitive and behavioral regression in non-lesional epilepsy with CSWS

Purpose: To present the long-term outcome (LTO) of 10 adolescents and young adults with documented cognitive and behavioral regression as children due to non-lesional focal, mainly frontal epilepsy with continuous spike-waves during slow wave sleep (CSWS). Method: Past medical and EEG data of all patients were reviewed and neuropsychological tests exploring main cognitive functions were administered. Result: After a mean duration of follow-up of 15.6 years (range 8-23 years), none of the 10 patients had recovered fully, but four regained borderline to normal intelligence and were almost independent. Patients with prolonged global intellectual regression had the worst outcome, whereas those with more specific and short-lived deficits recovered best. The marked behavioral disorders that were so disturbing during the active period (AP) resolved in all but one patient. Executive functions were neither severely nor homogenously affected. Three patients with a frontal syndrome during the AP disclosed only mild residual executive and social cognition deficits. The main cognitive gains occurred shortly after the AP, but qualitative improvements continued to occur. LTO correlated best with duration of CSWS. Conclusion: Our findings emphasize that cognitive recovery after cessation of CSWS depends on the severity and duration of the initial regression. None of our patients had major executive and social cognition deficits with preserved intelligence as reported in adults with destructive lesions of the frontal lobes during childhood. Early recognition of epilepsy with CSWS and rapid introduction of effective therapy are crucial for a best possible outcome.

Predictive factors of adrenal insufficiency in patients admitted to acute medical wards: a case control study.

BACKGROUND: Adrenal insufficiency is a rare and potentially lethal disease if untreated. Several clinical signs and biological markers are associated with glucocorticoid failure but the importance of these factors for diagnosing adrenal insufficiency is not known. In this study, we aimed to assess the prevalence of and the factors associated with adrenal insufficiency among patients admitted to an acute internal medicine ward. METHODS: Retrospective, case-control study including all patients with high-dose (250 μg) ACTH-stimulation tests for suspected adrenal insufficiency performed between 2008 and 2010 in an acute internal medicine ward (n = 281). Cortisol values <550 nmol/l upon ACTH-stimulation test were considered diagnostic for adrenal insufficiency. Area under the ROC curve (AROC), sensitivity, specificity, negative and positive predictive values for adrenal insufficiency were assessed for thirteen symptoms, signs and biological variables. RESULTS: 32 patients (11.4%) presented adrenal insufficiency; the others served as controls. Among all clinical and biological parameters studied, history of glucocorticoid withdrawal was the only independent factor significantly associated with patients with adrenal insufficiency (Odds Ratio: 6.71, 95% CI: 3.08 -14.62). Using a logistic regression, a model with four significant and independent variable was obtained, regrouping history of glucocorticoid withdrawal (OR 7.38, 95% CI [3.18 ; 17.11], p-value <0.001), nausea (OR 3.37, 95% CI [1.03 ; 11.00], p-value 0.044), eosinophilia (OR 17.6, 95% CI [1.02; 302.3], p-value 0.048) and hyperkalemia (OR 2.41, 95% CI [0.87; 6.69], p-value 0.092). The AROC (95% CI) was 0.75 (0.70; 0.80) for this model, with 6.3 (0.8 - 20.8) for sensitivity and 99.2 (97.1 - 99.9) for specificity. CONCLUSIONS: 11.4% of patients with suspected adrenal insufficient admitted to acute medical ward actually do present with adrenal insufficiency, defined by an abnormal response to high-dose (250 μg) ACTH-stimulation test. A history of glucocorticoid withdrawal was the strongest factor predicting the potential adrenal failure. The combination of a history of glucocorticoid withdrawal, nausea, eosinophilia and hyperkaliemia might be of interest to suspect adrenal insufficiency.

In vivo targeting of an anti-tumor antibody coupled to antigenic MHC class I complexes induces specific growth inhibition and regression of established syngeneic tumor grafts.

The concept of antibody-mediated targeting of antigenic MHC/peptide complexes on tumor cells in order to sensitize them to T-lymphocyte cytotoxicity represents an attractive new immunotherapy strategy. In vitro experiments have shown that an antibody chemically conjugated or fused to monomeric MHC/peptide can be oligomerized on the surface of tumor cells, rendering them susceptible to efficient lysis by MHC-peptide restricted specific T-cell clones. However, this strategy has not yet been tested entirely in vivo in immunocompetent animals. To this aim, we took advantage of OT-1 mice which have a transgenic T-cell receptor specific for the ovalbumin (ova) immunodominant peptide (257-264) expressed in the context of the MHC class I H-2K(b). We prepared and characterized conjugates between the Fab' fragment from a high-affinity monoclonal antibody to carcinoembryonic antigen (CEA) and the H-2K(b) /ova peptide complex. First, we showed in OT-1 mice that the grafting and growth of a syngeneic colon carcinoma line transfected with CEA could be specifically inhibited by systemic injections of the conjugate. Next, using CEA transgenic C57BL/6 mice adoptively transferred with OT-1 spleen cells and immunized with ovalbumin, we demonstrated that systemic injections of the anti-CEA-H-2K(b) /ova conjugate could induce specific growth inhibition and regression of well-established, palpable subcutaneous grafts from the syngeneic CEA-transfected colon carcinoma line. These results, obtained in a well-characterized syngeneic carcinoma model, demonstrate that the antibody-MHC/peptide strategy can function in vivo. Further preclinical experimental studies, using an anti-viral T-cell response, will be performed before this new form of immunotherapy can be considered for clinical use.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data.

BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.

Ruling out coronary heart disease in primary care patients with chest pain: a clinical prediction score.

ABSTRACT: BACKGROUND: Chest pain raises concern for the possibility of coronary heart disease. Scoring methods have been developed to identify coronary heart disease in emergency settings, but not in primary care. METHODS: Data were collected from a multicenter Swiss clinical cohort study including 672 consecutive patients with chest pain, who had visited one of 59 family practitioners' offices. Using delayed diagnosis we derived a prediction rule to rule out coronary heart disease by means of a logistic regression model. Known cardiovascular risk factors, pain characteristics, and physical signs associated with coronary heart disease were explored to develop a clinical score. Patients diagnosed with angina or acute myocardial infarction within the year following their initial visit comprised the coronary heart disease group. RESULTS: The coronary heart disease score was derived from eight variables: age, gender, duration of chest pain from 1 to 60 minutes, substernal chest pain location, pain increases with exertion, absence of tenderness point at palpation, cardiovascular risks factors, and personal history of cardiovascular disease. Area under the receiver operating characteristics curve was of 0.95 with a 95% confidence interval of 0.92; 0.97. From this score, 413 patients were considered as low risk for values of percentile 5 of the coronary heart disease patients. Internal validity was confirmed by bootstrapping. External validation using data from a German cohort (Marburg, n = 774) revealed a receiver operating characteristics curve of 0.75 (95% confidence interval, 0.72; 0.81) with a sensitivity of 85.6% and a specificity of 47.2%. CONCLUSIONS: This score, based only on history and physical examination, is a complementary tool for ruling out coronary heart disease in primary care patients complaining of chest pain.

Prediction of 18-month survival in patients with primary myelodysplastic syndrome. A regression model and scoring system based on the combination of chromosome findings and the Bournemouth score.

The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of myelodysplasia and which describe two distinct evolutional pathways.

Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.

BACKGROUND AND PURPOSE: Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors. METHODS: The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke. RESULTS: In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)). CONCLUSIONS: The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

INTERMED predicts non-return to work in an occupational rehabilitation setting for individuals with orthopaedic trauma-Part I

Introduction.- Knowledge of predictors of an unfavourable outcome, e.g. non-return to work after an injury enables to identify patients at risk and to target interventions for modifiable predictors. It has been recently shown that INTERMED; a tool to measure biopsychosocial complexity in four domains (biologic, psychologic, social and care, with a score between 0-60 points) can be useful in this context. The aim of this study was to set up a predictive model for non-return to work using INTERMED in patients in vocational rehabilitation after orthopaedic injury.Patients and methods.- In this longitudinal prospective study, the cohort consisted of 2156 consecutively included inpatients with orthopaedic trauma attending a rehabilitation hospital after a work, traffic or sport related injury. Two years after discharge, a questionnaire regarding return to work was sent (1502 returned their questionnaires). In addition to INTERMED, 18 predictors known at baseline of the rehabilitation were selected based on previous research. A multivariable logistic regression was performed.Results.- In the multivariate model, not-returning to work at 2 years was significantly predicted by the INTERMED: odds-ratio (OR) 1.08 (95% confidence interval, CI [1.06; 1.11]) for a one point increase in scale; by qualified work-status before the injury OR = 0.74, CI (0.54; 0.99), by using French as preferred language OR = 0.60, CI (0.45; 0.80), by upper-extremity injury OR = 1.37, CI (1.03; 1.81), by higher education (> 9 years) OR = 0.74, CI (0.55; 1.00), and by a 10 year increase in age OR = 1.15, CI (1.02; 1.29). The area under the receiver-operator-characteristics curve (ROC)-curve was 0.733 for the full model (INTERMED plus 18 variables).Discussion.- These results confirm that the total score of the INTERMED is a significant predictor for return to work. The full model with 18 predictors combined with the total score of INTERMED has good predictive value. However, the number of variables (19) to measure is high for the use as screening tool in a clinic.

Glomerular filtration rate: measure creatinine and height rather than cystatin C!

AIM: Inulin clearance (Cin) is the gold standard for assessing glomerular filtration rate (GFR). Other methods are based on the plasma creatinine concentration (Pcreat), creatinine clearance (Ccreat), the Haycock-Schwartz formula and the plasma concentration of cystatin C (PcysC), a 13 kDa basic protein produced at a constant rate by all nucleated cells. The present prospective study was thus designed to evaluate the reliability of PcysC as a marker of GFR in comparison with that of Pcreat, Ccreat and the Haycock-Schwartz formula, using Cin as the gold standard. METHODS: Ninety-nine children (51 m/48 f), with a median age of 8.3 y (1.0-17.9) were studied. Using a cut-off for Cin of 100 ml/min per 1.73 m2, 54 children (54.5%) had impaired GFR. Those with normal GFR were comparable for age, height, weight and body mass index. RESULTS: Logistic regression, ROC analysis and linear regression all showed that Ccreat was the best parameter to discriminate between impaired and normal GFR, followed by the Haycock-Schwartz formula, PcysC, and finally Pcreat, each one being significantly more predictive than the next. CONCLUSION: GFR is better assessed by the Haycock-Schwartz formula than by PcysC or Pcreat alone. It is therefore concluded that when urine collection is not possible, simply measuring the child's Pcreat and height is the best, easiest and cheapest way to assess GFR.

Diagnostic value of MRI for the detection of suspected placental invasion: Does it depend on observers' experience?

Purpose: To evaluate the diagnostic value of specific MR features for detection of suspected placental invasion according to observers' experience.Methods and Materials: Our study population included 25 pregnant women (mean age 35.16) investigated by prenatal MRI. In twelve out of them placental invasion was histopathologically proven, the 13 other women (52%) without placental invasion served as control group. Multiplanar T1- and T2-weighted sequences had been performed mostly without IV contrast injection (1.5 T). MR examinations of the two groups were rendered anonymous, mixed, then independently and retrospectively reviewed by two senior and two junior radiologists in view of 8 MR features indicating placentar invasion including the degree. Results were compared with surgical diagnosis (placenta normal/increta/accreta/percreta). Interobserver agrement between senior and junior readers were calculated. Stepwise logistic regression and receiver operating (ROC) curvers were performed.Results: Demographics between the two groups were not statistically different. Overall sensitivity and specificity for detecting placentar invasion was 90.9% and 75.0% for senior readers, and 81.8% and 61.8% for junior readers respectively. The most significant MR features indicating placentar invasion were T2 hypointense placental bands, followed by placenta praevia, focally interrupted myometrial border, posterior placental insertion, and heterogeneous placental signal. For each of the evaluated MR features the interobserver agreement kappa between the two senior readers was superior than that between the junior readers, ranging from bad (<0.4) to good (0.4-0.75).Conclusions: MRI can be a reliable and reproducible tool for detection of suspected placentar invasion, however very variable according to the observers' experience.