Patrick Schober

Bio: Patrick Schober is an academic researcher from VU University Amsterdam. The author has contributed to research in topics: Medicine & MEDLINE. The author has an hindex of 25, co-authored 154 publications receiving 3354 citations. Previous affiliations of Patrick Schober include Vanderbilt University Medical Center & VU University Medical Center.

Correlation Coefficients: Appropriate Use and Interpretation.

Abstract: Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.

Monitoring tissue oxygenation by near infrared spectroscopy (NIRS): background and current applications

Abstract: Conventional cardiovascular monitoring may not detect tissue hypoxia, and conventional cardiovascular support aiming at global hemodynamics may not restore tissue oxygenation. NIRS offers non-invasive online monitoring of tissue oxygenation in a wide range of clinical scenarios. NIRS monitoring is commonly used to measure cerebral oxygenation (rSO(2)), e.g. during cardiac surgery. In this review, we will show that tissue hypoxia occurs frequently in the perioperative setting, particularly in cardiac surgery. Therefore, measuring and obtaining adequate tissue oxygenation may prevent (postoperative) complications and may thus be cost-effective. NIRS monitoring may also be used to detect tissue hypoxia in (prehospital) emergency settings, where it has prognostic significance and enables monitoring of therapeutic interventions, particularly in patients with trauma. However, optimal therapeutic agents and strategies for augmenting tissue oxygenation have yet to be determined.

Cormack–Lehane classification revisited

Abstract: † The reproducibility of CL classification was limited, with a poor intraobserver reliability and a fair inter-observer reliability.

Survival analysis and interpretation of time-to-event data: The tortoise and the hare

Abstract: Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest A unique feature of survival data is that typically not all patients experience the event (eg, death) by the end of the observation period, so the actual survival times for some patients are unknown This phenomenon, referred to as censoring, must be accounted for in the analysis to allow for valid inferences Moreover, survival times are usually skewed, limiting the usefulness of analysis methods that assume a normal data distribution As part of the ongoing series in Anesthesia & Analgesia, this tutorial reviews statistical methods for the appropriate analysis of time-to-event data, including nonparametric and semiparametric methods-specifically the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model These methods are by far the most commonly used techniques for such data in medical literature Illustrative examples from studies published in Anesthesia & Analgesia demonstrate how these techniques are used in practice Full parametric models and models to deal with special circumstances, such as recurrent events models, competing risks models, and frailty models, are briefly discussed

Correlation Coefficients: Appropriate Use and Interpretation.

Abstract: Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.

Iconographies supplémentaires de l'article : Methodologic Standards for the Development of Clinical Decision Rules in Emergency Medicine

Abstract: The purpose of this review is to present a guide to help readers critically appraise the methodologic quality of an article or articles describing a clinical decision rule. This guide will also be useful to clinical researchers who wish to answer 1 or more questions detailed in this article. We consider the 6 major stages in the development and testing of a new clinical decision rule and discuss a number of standards within each stage. We use examples from emergency medicine and, in particular, examples from our own research on clinical decisions rules for radiography in trauma.