Lung real time three-dimensional imaging in critically ill ventilated patients: a global diagnosis concordance study.

Lung ultrasound (LUS) increases clinical diagnosis performance in intensive care unit (ICU). Real-time three-dimensional (3-D) imaging was compared with two-dimensional (2-D) LUS by assessing the global diagnosis concordance. In this single center, prospective, observational, pilot study, one trained operator performed a 3-D LUS immediately after a 2-D LUS in eight areas of interest on the same areas in 16 ventilated critically ill patients. All cine loops were recorded on a computer without visible link between 2-D and 3-D exams. Two experts blindly reviewed cine loops. Four main diagnoses were proposed: normal lung, consolidation, pleural effusion and interstitial syndrome. Fleiss κ and Cohen's κ values were calculated. In 252 LUS cine loops, the concordance between 2-D and 3-D exams was 83.3% (105/126), 77.6% (99/126) and 80.2% (101/126) for the trained operator and the experts respectively. The Cohen's κ coefficient value was 0.69 [95% Confidence Interval (CI) 0.58-0.80] for expert 1 meaning a substantial agreement. The inter-rater reliability was very good (Fleiss' κ value = 0.94 [95% CI 0.87-1.0]) for 3-D exams. The Cohen's κ was excellent for pleural effusion (κ= 0.93 [95% CI 0.76-1.0]), substantial for normal lung diagnosis (κ = 0.68 [95% CI 0.51-0.86]) and interstitial syndrome (κ = 0.62 [95% CI 0.45-0.80]) and fair for consolidation diagnoses (κ = 0.47 [95% CI 0.30-0.64]). In ICU ventilated patients, there was a substantial concordance between 2-D and 3-D LUS with a good inter-rater reliability. However, the diagnosis concordance for lung consolidation is poor.

Artificial neural networks for assessing the risk of urinary calcium stone among men.

The pathophysiology of idiopathic calcium oxalate nephrolithiasis involves metabolic abnormalities. Previous studies gave conflicting results about the metabolic factors in stone formers. Artificial neural networks (ANN) are new methods based on computer programming that have outperformed conventional methods in prediction of outcomes in different medical applications. The aim of our study was to compare with ANN the clinical and biochemical parameters implicated in urinary calcium stone between stone formers (SF) and controls (C). We compared 11 clinical and biochemical variables among 119 male idiopathic calcium oxalate SF and 96 C by univariate and multivariate statistical analyses. Univariate analyses included discriminant analysis, logistic regression analysis, and ANN. For multivariate analyses, stepwise discriminant analysis and ANN were performed. Variables included age, body mass index (BMI), family history of nephrolithiasis, supersaturation with respect to calcium oxalate, calcemia, and 24-h urinary calcium, oxalate, uric acid, urea, sodium, and citrate. With univariate and multivariate analyses, ANN were as efficient as classical statistical analyses in discriminating the different parameters. The sensitivity, the specificity, and the percentage of correctly classified patients were similar in all analyses. With ANN, supersaturation (receiver operating characteristic, ROC curves index 0.73) and urea (ROC 0.72) were the most discriminant followed by family history and urinary calcium (ROC 0.67). ROC index was 0.63 for citrate, 0.61 for oxalate and urate, 0.60 for sodium and calcemia, 0.58 for age, and 0.56 for BMI, but these parameters were not statistically different between SF and C. ANN gave additional information since they made it possible to determine the cut-off values of the parameters and their predictive power. Cut-off values for being a stone former were 8.9 for supersaturation and 363 mmol/day for urinary urea with a predictive power of 0.69 and 0.70, respectively. Univariate and multivariate analysis evidenced supersaturation and 24-h urinary urea excretion as the most discriminant parameters between the two populations. In addition to high supersaturation, the negative impact of protein intake was confirmed.

The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

MOTIVATION: Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining as an intuitive and computationally efficient procedure. RESULTS: The present study describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray datasets, and was developed using an in vitro experiment that compared control and interferon-gamma treated skin cells. In this experiment, up to nine replicates were used to confidently estimate error, thereby enabling methods of different statistical power to be compared. Gene expression results of a similar absolute expression are binned, so as to enable a highly accurate local estimate of the mean squared error within conditions. The model then relates variability of gene expression in each bin to absolute expression levels and uses this in a test derived from the classical ANOVA. The GEA selection method is compared with both the classical and permutational ANOVA tests, and demonstrates an increased stability, robustness and confidence in gene selection. A subset of the selected genes were validated by real-time reverse transcription-polymerase chain reaction (RT-PCR). All these results suggest that GEA methodology is (i) suitable for selection of differentially expressed genes in microarray data, (ii) intuitive and computationally efficient and (iii) especially advantageous under conditions of low k. AVAILABILITY: The GEA code for R software is freely available upon request to authors.