Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

The frequency of Duchenne muscular dystrophy/Becker muscular dystrophy and Pompe disease in children with isolated transaminase elevation: results from the observational VICTORIA study

IntroductionElevated transaminases and/or creatine phosphokinase can indicate underlying muscle disease. Therefore, this study aims to determine the frequency of Duchenne muscular dystrophy/Becker muscular dystrophy (DMD/BMD) in male children and Pompe disease (PD) in male and female children with isolated hypertransaminasemia.MethodsThis multi-center, prospective study enrolled patients aged 3–216 months with serum alanine transaminase (ALT) and/or aspartate transaminase (AST) levels &gt;2× the upper limit of normal (ULN) for ≥3 months. Patients with a known history of liver or muscle disease or physical examination findings suggestive of liver disease were excluded. Patients were screened for creatinine phosphokinase (CPK) levels, and molecular genetic tests for DMD/BMD in male patients and enzyme analysis for PD in male and female patients with elevated CPK levels were performed. Genetic analyses confirmed PD. Demographic, clinical, and laboratory characteristics of the patients were analyzed.ResultsOverall, 589 patients [66.8% male, mean age of 63.4 months (standard deviation: 60.5)] were included. In total, 251 patients (188 male and 63 female) had CPK levels above the ULN. Of the patients assessed, 47% (85/182) of male patients were diagnosed with DMD/BMD and 1% (3/228) of male and female patients were diagnosed with PD. The median ALT, AST, and CPK levels were statistically significantly higher, and the questioned neurological symptoms and previously unnoticed examination findings were more common in DMD/BMD patients than those without DMD/BMD or PD (p &lt; 0.001).DiscussionQuestioning neurological symptoms, conducting a complete physical examination, and testing for CPK levels in patients with isolated hypertransaminasemia will prevent costly and time-consuming investigations for liver diseases and will lead to the diagnosis of occult neuromuscular diseases. Trial RegistrationClinicaltrials.gov NCT04120168

Comparison of homogeneity tests for temperature using a simulation study

Homogeneity testing is one of the most important analyses in climate-related studies as it underpins the reliability of any inferences. The effects not directly related with climate are identified and removed from the meteorological variables, and then the obtained homogeneous variables are used to present an enhanced picture of the current situation and produce realistic forecasts based upon the variables. In this study, we investigate the performances of well-known homogeneity tests and introduce some tests that are not usually used for testing homogeneity via Monte Carlo simulation. We generate data using a normally distributed temperature variable. We consider both absolute and relative homogeneity tests (RHTest). Although relative tests are the best performing homogeneity tests, their results highly depend on the quality of the reference series. Consequently, they have to be used with at least one of the absolute tests in order to detect possible inhomogeneities in the reference series. Our results show that the relative tests which are standard normal homogeneity test, F-test for structural breaks with reference series, multiple change point detection method, namely and the RHTest have the best detection rates whereas the absolute tests, namely, Kruskal-Wallis, Pettitt and Friedman tests have the worst performances. The best performing absolute test is F-test for structural breaks

Meteorolojik Verilerin Hesaplamalı İstatistiksel Yöntemlerle Türdeşlik Analizi

Türdeşlik analizi, zaman serisi verisi için önemli bir araştırma konusudur. Ortalamada değişim, varyantsa değişim, eğilim oluşumu, ani ya da kademeli artış veya azalma gibi birçok durum türdeşliği bozan sebeplerdir. Türdeşlik literatüründe kullanılan yöntemlerin birçoğunda güvenli olmayan sonuçlara yol açabilecek varsayımlar ya da yaklaşımlar bulunmaktadır. Bunlardan bazıları; bağımsızlık ve özdeşlik ya da normallik varsayımı gibi zaman serisi için gerçekçi olmayan durumlardır. Bu projede, bir hesaplamalı istatistik yaklaşımı olan koruyan halka yönteminin kullanımı ile literatürdeki yöntemlerin geliştirilmesi ya da yeni bir yöntem oluşturularak verinin türdeşlik analizinin yapılması planlanmaktadır

Detection performance of likelihood ratio test for change points based on bootstrap for AR 1 models

he detection of change-points in time series is an important issue especially in economics, finance, meteorology and energy. Change in mean, change in variance or any sudden increase or decrease in the series can cause breakpoints. In AR(1) models, the likelihood ratio test is conducted to test for a single breakpoint. However, if the sample size is small or the location of the breakpoint is close to the end or the beginning of the series, the detection performance becomes worse. In order to increase the correct detection percentage of the likelihood ratio test in these cases, a bootstrap method for dependent data is applied and its performance is investigated when the change is only in the mean under several breakpoint scenarios. The test is applied to simulated data and the results are compared with the results obtained from tests in the literature

Generation of monthly precipitation series and the performance of homogeneity tests

Climate studies have gained importance due to the significant effect of climate change. The extreme meteorological events can cause floods, droughts, sudden change in the temperature or change in the climate trends. Since they have an important effect on human beings and the environment, these meteorological variables should be predicted and some precautions should be taken if possible. In order to conduct any kind of statistical analysis, the nonclimatic effects should be determined and corrected or removed. Otherwise, any kind of meteorological effect should be kept to reflect the change in the variable. If there is any nonclimatic effect in the variable, the series is defined as inhomogeneous. The most important climatic variables are temperature and precipitation. The precipitation variable cannot be modeled efficiently due to its nonsymmetric shape and high variability. First, monthly total precipitation amount is simulated based on real station records. Then, in each simulation, artificial breakpoints are created and the homogeneity analysis are conducted to detect the inhomogeneity and the results are compared to decide on the best performed homogeneity test for precipitation