Determination of respiratory gas flow by electrical impedance tomography in an animal model of mechanical ventilation

Background A recent method determines regional gas flow of the lung by electrical impedance tomography (EIT). The aim of this study is to show the applicability of this method in a porcine model of mechanical ventilation in healthy and diseased lungs. Our primary hypothesis is that global gas flow measured by EIT can be correlated with spirometry. Our secondary hypothesis is that regional analysis of respiratory gas flow delivers physiologically meaningful results. Methods In two sets of experiments n = 7 healthy pigs and n = 6 pigs before and after induction of lavage lung injury were investigated. EIT of the lung and spirometry were registered synchronously during ongoing mechanical ventilation. In-vivo aeration of the lung was analysed in four regions-of-interest (ROI) by EIT: 1) global, 2) ventral (non-dependent), 3) middle and 4) dorsal (dependent) ROI. Respiratory gas flow was calculated by the first derivative of the regional aeration curve. Four phases of the respiratory cycle were discriminated. They delivered peak and late inspiratory and expiratory gas flow (PIF, LIF, PEF, LEF) characterizing early or late inspiration or expiration. Results Linear regression analysis of EIT and spirometry in healthy pigs revealed a very good correlation measuring peak flow and a good correlation detecting late flow. PIFEIT = 0.702 · PIFspiro + 117.4, r2 = 0.809; PEFEIT = 0.690 · PEFspiro-124.2, r2 = 0.760; LIFEIT = 0.909 · LIFspiro + 27.32, r2 = 0.572 and LEFEIT = 0.858 · LEFspiro-10.94, r2 = 0.647. EIT derived absolute gas flow was generally smaller than data from spirometry. Regional gas flow was distributed heterogeneously during different phases of the respiratory cycle. But, the regional distribution of gas flow stayed stable during different ventilator settings. Moderate lung injury changed the regional pattern of gas flow. Conclusions We conclude that the presented method is able to determine global respiratory gas flow of the lung in different phases of the respiratory cycle. Additionally, it delivers meaningful insight into regional pulmonary characteristics, i.e. the regional ability of the lung to take up and to release air.

A clinical prediction model to identify patients at high risk of death in the emergency department.

PURPOSE Rapid assessment and intervention is important for the prognosis of acutely ill patients admitted to the emergency department (ED). The aim of this study was to prospectively develop and validate a model predicting the risk of in-hospital death based on all available information available at the time of ED admission and to compare its discriminative performance with a non-systematic risk estimate by the triaging first health-care provider. METHODS Prospective cohort analysis based on a multivariable logistic regression for the probability of death. RESULTS A total of 8,607 consecutive admissions of 7,680 patients admitted to the ED of a tertiary care hospital were analysed. Most frequent APACHE II diagnostic categories at the time of admission were neurological (2,052, 24 %), trauma (1,522, 18 %), infection categories [1,328, 15 %; including sepsis (357, 4.1 %), severe sepsis (249, 2.9 %), septic shock (27, 0.3 %)], cardiovascular (1,022, 12 %), gastrointestinal (848, 10 %) and respiratory (449, 5 %). The predictors of the final model were age, prolonged capillary refill time, blood pressure, mechanical ventilation, oxygen saturation index, Glasgow coma score and APACHE II diagnostic category. The model showed good discriminative ability, with an area under the receiver operating characteristic curve of 0.92 and good internal validity. The model performed significantly better than non-systematic triaging of the patient. CONCLUSIONS The use of the prediction model can facilitate the identification of ED patients with higher mortality risk. The model performs better than a non-systematic assessment and may facilitate more rapid identification and commencement of treatment of patients at risk of an unfavourable outcome.

Reliable prediction of clinical outcome in patients with chronic HCV infection and compensated advanced hepatic fibrosis: a validated model using objective and readily available clinical parameters.

OBJECTIVE Reliable tools to predict long-term outcome among patients with well compensated advanced liver disease due to chronic HCV infection are lacking. DESIGN Risk scores for mortality and for cirrhosis-related complications were constructed with Cox regression analysis in a derivation cohort and evaluated in a validation cohort, both including patients with chronic HCV infection and advanced fibrosis. RESULTS In the derivation cohort, 100/405 patients died during a median 8.1 (IQR 5.7-11.1) years of follow-up. Multivariate Cox analyses showed age (HR=1.06, 95% CI 1.04 to 1.09, p<0.001), male sex (HR=1.91, 95% CI 1.10 to 3.29, p=0.021), platelet count (HR=0.91, 95% CI 0.87 to 0.95, p<0.001) and log10 aspartate aminotransferase/alanine aminotransferase ratio (HR=1.30, 95% CI 1.12 to 1.51, p=0.001) were independently associated with mortality (C statistic=0.78, 95% CI 0.72 to 0.83). In the validation cohort, 58/296 patients with cirrhosis died during a median of 6.6 (IQR 4.4-9.0) years. Among patients with estimated 5-year mortality risks <5%, 5-10% and >10%, the observed 5-year mortality rates in the derivation cohort and validation cohort were 0.9% (95% CI 0.0 to 2.7) and 2.6% (95% CI 0.0 to 6.1), 8.1% (95% CI 1.8 to 14.4) and 8.0% (95% CI 1.3 to 14.7), 21.8% (95% CI 13.2 to 30.4) and 20.9% (95% CI 13.6 to 28.1), respectively (C statistic in validation cohort = 0.76, 95% CI 0.69 to 0.83). The risk score for cirrhosis-related complications also incorporated HCV genotype (C statistic = 0.80, 95% CI 0.76 to 0.83 in the derivation cohort; and 0.74, 95% CI 0.68 to 0.79 in the validation cohort). CONCLUSIONS Prognosis of patients with chronic HCV infection and compensated advanced liver disease can be accurately assessed with risk scores including readily available objective clinical parameters.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach.

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.

Detecting outlying studies in meta-regression models using a forward search algorithm.

When considering data from many trials, it is likely that some of them present a markedly different intervention effect or exert an undue influence on the summary results. We develop a forward search algorithm for identifying outlying and influential studies in meta-analysis models. The forward search algorithm starts by fitting the hypothesized model to a small subset of likely outlier-free studies and proceeds by adding studies into the set one-by-one that are determined to be closest to the fitted model of the existing set. As each study is added to the set, plots of estimated parameters and measures of fit are monitored to identify outliers by sharp changes in the forward plots. We apply the proposed outlier detection method to two real data sets; a meta-analysis of 26 studies that examines the effect of writing-to-learn interventions on academic achievement adjusting for three possible effect modifiers, and a meta-analysis of 70 studies that compares a fluoride toothpaste treatment to placebo for preventing dental caries in children. A simple simulated example is used to illustrate the steps of the proposed methodology, and a small-scale simulation study is conducted to evaluate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Dimensional approaches to schizophrenia: A comparison of the Bern Psychopathology scale and the five-factor model of the Positive and Negative Syndrome Scale

The aim was to examine to what extent the dimensions of the BPS map the five factors derived from the PANSS in order to explore the level of agreement of these alternative dimensional approaches in patients with schizophrenia. 149 inpatients with schizophrenia spectrum disorders were recruited. Psychopathological symptoms were assessed with the Bern Psychopathology Scale (BPS) and the Positive and Negative Syndrome Scale (PANSS). Linear regression analyses were conducted to explore the association between the factors and the items of the BPS. The robustness of patterns was evaluated. An understandable overlap of both approaches was found for positive and negative symptoms and excitement. The PANSS positive factor was associated with symptoms of the affect domain in terms of both inhibition and disinhibition, the PANSS negative factor with symptoms of all three domains of the BPS as an inhibition and the PANSS excitement factor with an inhibition of the affect domain and a disinhibition of the language and motor domains. The results show that here is only a partial overlap between the system-specific approach of the BPS and the five-factor PANSS model. A longitudinal assessment of psychopathological symptoms would therefore be of interest.

A randomized trial of the effects of the noble gases helium and argon on neuroprotection in a rodent cardiac arrest model.

BACKGROUND The noble gas xenon is considered as a neuroprotective agent, but availability of the gas is limited. Studies on neuroprotection with the abundant noble gases helium and argon demonstrated mixed results, and data regarding neuroprotection after cardiac arrest are scant. We tested the hypothesis that administration of 50% helium or 50% argon for 24 h after resuscitation from cardiac arrest improves clinical and histological outcome in our 8 min rat cardiac arrest model. METHODS Forty animals had cardiac arrest induced with intravenous potassium/esmolol and were randomized to post-resuscitation ventilation with either helium/oxygen, argon/oxygen or air/oxygen for 24 h. Eight additional animals without cardiac arrest served as reference, these animals were not randomized and not included into the statistical analysis. Primary outcome was assessment of neuronal damage in histology of the region I of hippocampus proper (CA1) from those animals surviving until day 5. Secondary outcome was evaluation of neurobehavior by daily testing of a Neurodeficit Score (NDS), the Tape Removal Test (TRT), a simple vertical pole test (VPT) and the Open Field Test (OFT). Because of the non-parametric distribution of the data, the histological assessments were compared with the Kruskal-Wallis test. Treatment effect in repeated measured assessments was estimated with a linear regression with clustered robust standard errors (SE), where normality is less important. RESULTS Twenty-nine out of 40 rats survived until day 5 with significant initial deficits in neurobehavioral, but rapid improvement within all groups randomized to cardiac arrest. There were no statistical significant differences between groups neither in the histological nor in neurobehavioral assessment. CONCLUSIONS The replacement of air with either helium or argon in a 50:50 air/oxygen mixture for 24 h did not improve histological or clinical outcome in rats subjected to 8 min of cardiac arrest.

PSPLINE: Stata module providing a penalized spline scatterplot smoother based on linear mixed model technology

Pspline uses xtmixed to fit a penalized spline regression and plots the smoothed function. Additional covariates can be specified to adjust the smooth and plot partial residuals.

WGTTEST: Stata module to test the impact of sampling weights in regression analysis

wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.

The nasal microbiota in infants with cystic fibrosis in the first year of life: a prospective cohort study.

BACKGROUND Respiratory tract infections and subsequent airway inflammation occur early in the life of infants with cystic fibrosis. However, detailed information about the microbial composition of the respiratory tract in infants with this disorder is scarce. We aimed to undertake longitudinal in-depth characterisation of the upper respiratory tract microbiota in infants with cystic fibrosis during the first year of life. METHODS We did this prospective cohort study at seven cystic fibrosis centres in Switzerland. Between Feb 1, 2011, and May 31, 2014, we enrolled 30 infants with a diagnosis of cystic fibrosis. Microbiota characterisation was done with 16S rRNA gene pyrosequencing and oligotyping of nasal swabs collected every 2 weeks from the infants with cystic fibrosis. We compared these data with data for an age-matched cohort of 47 healthy infants. We additionally investigated the effect of antibiotic treatment on the microbiota of infants with cystic fibrosis. Statistical methods included regression analyses with a multivariable multilevel linear model with random effects to correct for clustering on the individual level. FINDINGS We analysed 461 nasal swabs taken from the infants with cystic fibrosis; the cohort of healthy infants comprised 872 samples. The microbiota of infants with cystic fibrosis differed compositionally from that of healthy infants (p=0·001). This difference was also found in exclusively antibiotic-naive samples (p=0·001). The disordering was mainly, but not solely, due to an overall increase in the mean relative abundance of Staphylococcaceae in infants with cystic fibrosis compared with healthy infants (multivariable linear regression model stratified by age and adjusted for season; second month: coefficient 16·2 [95% CI 0·6-31·9]; p=0·04; third month: 17·9 [3·3-32·5]; p=0·02; fourth month: 21·1 [7·8-34·3]; p=0·002). Oligotyping analysis enabled differentiation between Staphylococcus aureus and coagulase-negative Staphylococci. Whereas the analysis showed a decrease in S aureus at and after antibiotic treatment, coagulase-negative Staphylococci increased. INTERPRETATION Our study describes compositional differences in the microbiota of infants with cystic fibrosis compared with healthy controls, and disordering of the microbiota on antibiotic administration. Besides S aureus, coagulase-negative Staphylococci also contributed to the disordering identified in these infants. These findings are clinically important in view of the crucial role that bacterial pathogens have in the disease progression of cystic fibrosis in early life. Our findings could be used to inform future studies of the effect of antibiotic treatment on the microbiota in infants with cystic fibrosis, and could assist in the prevention of early disease progression in infants with this disorder. FUNDING Swiss National Science Foundation, Fondation Botnar, the Swiss Society for Cystic Fibrosis, and the Swiss Lung Association Bern.

A daily rainfall erosivity model for Western Amazonia

Rainfall erosivities as defined by the R factor from the universal soil loss equation were determined for all events during a two-year period at the station La Cuenca in western Amazonia. Three methods based on a power relationship between rainfall amount and erosivity were then applied to estimate event and daily rainfall erosivities from the respective rainfall amounts. A test of the resulting regression equations against an independent data set proved all three methods equally adequate in predicting rainfall erosivity from daily rainfall amount. We recommend the Richardson model for testing in the Amazon Basin, and its use with the coefficient from La Cuenca in western Amazonia.