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      Quadratic function between arterial partial oxygen pressure and mortality risk in sepsis patients: an interaction with simplified acute physiology score

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      Author(s): a , 1 , 2 , 2
      Publication date (Electronic):
      Journal: Scientific Reports
      Publisher: Nature Publishing Group

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          Abstract

          Oxygen therapy is widely used in emergency and critical care settings, while there is little evidence on its real therapeutic effect. The study aimed to explore the impact of arterial oxygen partial pressure (PaO 2) on clinical outcomes in patients with sepsis. A large clinical database was employed for the study. Subjects meeting the diagnostic criteria of sepsis were eligible for the study. All measurements of PaO 2 were extracted. The primary endpoint was death from any causes during hospital stay. Survey data analysis was performed by using individual ICU admission as the primary sampling unit. Quadratic function was assumed for PaO 2 and its interaction with other covariates were explored. A total of 199,125 PaO 2 samples were identified for 11,002 ICU admissions. Each ICU stay comprised 18 PaO 2 samples in average. The fitted multivariable model supported our hypothesis that the effect of PaO 2 on mortality risk was in quadratic form. There was significant interaction between PaO 2 and SAPS-I (p = 0.007). Furthermore, the main effect of PaO 2 on SOFA score was nonlinear. The study shows that the effect of PaO 2 on mortality risk is in quadratic function form, and there is significant interaction between PaO 2 and severity of illness.

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          Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

          Mohammed Saeed, Mauricio Villarroel, Andrew T Reisner … (2011)
          We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Data collection and retrospective analysis. All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. Adult patients admitted to intensive care units between 2001 and 2007. None. The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.
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            Association between arterial hyperoxia following resuscitation from cardiac arrest and in-hospital mortality.

            J Hope Kilgannon, Alan Jones, Nathan I. Shapiro … (2010)
            Laboratory investigations suggest that exposure to hyperoxia after resuscitation from cardiac arrest may worsen anoxic brain injury; however, clinical data are lacking. To test the hypothesis that postresuscitation hyperoxia is associated with increased mortality. Multicenter cohort study using the Project IMPACT critical care database of intensive care units (ICUs) at 120 US hospitals between 2001 and 2005. Patient inclusion criteria were age older than 17 years, nontraumatic cardiac arrest, cardiopulmonary resuscitation within 24 hours prior to ICU arrival, and arterial blood gas analysis performed within 24 hours following ICU arrival. Patients were divided into 3 groups defined a priori based on PaO(2) on the first arterial blood gas values obtained in the ICU. Hyperoxia was defined as PaO(2) of 300 mm Hg or greater; hypoxia, PaO(2) of less than 60 mm Hg (or ratio of PaO(2) to fraction of inspired oxygen <300); and normoxia, not classified as hyperoxia or hypoxia. In-hospital mortality. Of 6326 patients, 1156 had hyperoxia (18%), 3999 had hypoxia (63%), and 1171 had normoxia (19%). The hyperoxia group had significantly higher in-hospital mortality (732/1156 [63%; 95% confidence interval {CI}, 60%-66%]) compared with the normoxia group (532/1171 [45%; 95% CI, 43%-48%]; proportion difference, 18% [95% CI, 14%-22%]) and the hypoxia group (2297/3999 [57%; 95% CI, 56%-59%]; proportion difference, 6% [95% CI, 3%-9%]). In a model controlling for potential confounders (eg, age, preadmission functional status, comorbid conditions, vital signs, and other physiological indices), hyperoxia exposure had an odds ratio for death of 1.8 (95% CI, 1.5-2.2). Among patients admitted to the ICU following resuscitation from cardiac arrest, arterial hyperoxia was independently associated with increased in-hospital mortality compared with either hypoxia or normoxia.
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              Univariate description and bivariate statistical inference: the first step delving into data.

              Zhongheng Zhang (2016)
              In observational studies, the first step is usually to explore data distribution and the baseline differences between groups. Data description includes their central tendency (e.g., mean, median, and mode) and dispersion (e.g., standard deviation, range, interquartile range). There are varieties of bivariate statistical inference methods such as Student's t-test, Mann-Whitney U test and Chi-square test, for normal, skews and categorical data, respectively. The article shows how to perform these analyses with R codes. Furthermore, I believe that the automation of the whole workflow is of paramount importance in that (I) it allows for others to repeat your results; (II) you can easily find out how you performed analysis during revision; (III) it spares data input by hand and is less error-prone; and (IV) when you correct your original dataset, the final result can be automatically corrected by executing the codes. Therefore, the process of making a publication quality table incorporating all abovementioned statistics and P values is provided, allowing readers to customize these codes to their own needs.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 13 October 2016
                Publication date Collection: 2016
                Volume: 6
                Electronic Location Identifier: 35133
                Affiliations
                [1 ]Department of emergency medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine , Hangzhou, 310016, China
                [2 ]Department of critical care medicine, Jinhua municipal central hospital, Jinhua hospital of Zhejiang university , Zhejiang, P.R.China
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                Publisher Item ID: srep35133
                DOI: 10.1038/srep35133
                PMC ID: 5062070
                PubMed ID: 27734905
                SO-VID: 68171e8f-6415-40ff-a9a1-5a3ac1236f73
                Copyright © Copyright © 2016, The Author(s)
                License:

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                Date received : 03 December 2015
                Date accepted : 26 September 2016
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