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      Reverse shock index (RSI) as a predictor of post-intubation cardiac arrest (PICA)

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          Abstract

          Background

          Endotracheal intubation (ETI) in critically ill patients is a high-risk procedure due to the increased risk of cardiac arrest, and several factors may predict poor outcomes in these patients. The aim of this study was to investigate the role of some factors, especially newly introduced vital signs such as the reverse shock index (RSI), in predicting post-intubation cardiac arrest (PICA) in critically ill adult patients.

          Methods

          This cross-sectional study was conducted on critically ill patients over 18 years of age who were admitted to the emergency department (ED) and underwent ETI within 1 year. Patients who developed PICA and those without this event were included in the study, and their features were compared. The primary outcome was cardiac arrest.

          Results

          Of 394 patients, 127 patients were included, of whom 95 (74.8%) developed PICA, and 32 (25.2%) did not experience cardiac arrest after intubation. In multivariate analysis, age, RSI, oxygen saturation, and total bilirubin were significantly associated with PICA. In addition, patients with RSI < 1 had a significantly higher risk of developing PICA (odds ratio = 5.22, 95% CI 1.83–14.86, p = 0.002). The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for predicting PICA were 51.11%, 83.33%, 90.2%, 36.23%, and 59.17%, respectively. The ROC curve for RSI showed an area under the curve (AUC) of 0.66.

          Conclusion

          RSI may be useful in predicting PICA with higher diagnostic accuracy compared to the shock index. Furthermore, advanced age, hypoxia, and hyperbilirubinemia may increase the risk of PICA in patients admitted to the ED.

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          Most cited references34

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          Purposeful selection of variables in logistic regression

          Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
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            Association Between Tracheal Intubation During Adult In-Hospital Cardiac Arrest and Survival.

            Tracheal intubation is common during adult in-hospital cardiac arrest, but little is known about the association between tracheal intubation and survival in this setting.
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              Cardiac Arrest and Mortality Related to Intubation Procedure in Critically Ill Adult Patients

              To determine the prevalence of and risk factors for cardiac arrest during intubation in ICU, as well as the association of ICU intubation-related cardiac arrest with 28-day mortality.
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                Author and article information

                Contributors
                mtorabi1390@yahoo.com , me_torabi@kmu.ac.ir
                Journal
                Int J Emerg Med
                Int J Emerg Med
                International Journal of Emergency Medicine
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1865-1372
                1865-1380
                7 December 2023
                7 December 2023
                2023
                : 16
                : 88
                Affiliations
                [1 ]Department of Emergency Medicine, Emergency Medicine, Kerman University of Medical Sciences, ( https://ror.org/02kxbqc24) Kerman, Iran
                [2 ]Faculty of Medicine, Kerman University of Medical Sciences, ( https://ror.org/02kxbqc24) Kerman, Iran
                [3 ]Biostatistics, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, ( https://ror.org/02kxbqc24) Kerman, Iran
                Article
                569
                10.1186/s12245-023-00569-y
                10702001
                38062359
                503c306c-4670-407b-9ddf-9c31f7904a1b
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 28 September 2023
                : 1 December 2023
                Categories
                Research
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                Emergency medicine & Trauma
                cardiac arrest,emergency department,intubation,vital signs
                Emergency medicine & Trauma
                cardiac arrest, emergency department, intubation, vital signs

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