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      • Record: found
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      Which score should be used in intubated patients’ Glasgow coma scale or full outline of unresponsiveness?

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          Abstract

          Background and Aims:

          Today Glasgow coma scale (GCS) is the most well-known and common score for evaluation of the level of consciousness and outcome predict after traumatic brain injuries in the world. Regarding to some advantages of the full outline of unresponsiveness (FOUR) score over GCS in intubated patients, we’re going to compare the precision of these two scores in predicting the outcome predict in intubated patients.

          Methods:

          This research was a diagnostic-based study, which was conducted prospectively on 80 patients with Traumatic brain injury who were intubated and admitted to Intensive Care Unit (ICU) of Educational Hospitals of Mazandaran University of Medical Science during February 2013 to August 2013. The scores of FOUR and GCS were measured by the researcher in the first 24 h of admission in ICU. The information's recorded in the check list including the mortality rate of early and late inside of the hospital interred to excel. The findings were analyzed using SPSS software, through descriptive statistics and regression logistic.

          Results:

          The results showed of 80 patients 21 patients (20%) were female and 59 patients (80%) were male. The age average of the samples was 33.80 ± 12.60 ranging from 16 to 60 years old. 21 patients (26.2%) died during treatment. Of 21 patients, 15 patients died during first 14 days (18.7%) and 6 patients died after 14 years (7.5%). The area under curve (AUC) of FOUR score in early mortality was 0.90 (C 1 = 0.95, 0.88–0.90). The amount AUC for GCS was 0.80 (C 1 = 0.95, 0.78–0.84), which in delayed mortality it was ordered as 0.86 (C 1 = 0.95, 0.84–0.90) and 0.89 (C 1 = 0.95, 0.78–0.88).

          Conclusion:

          The research results indicated that FOUR score is more exact and more practical in intubated patients regarding lack of verbal response factor in early mortality prediction in GCS. Hence, it is recommended for health professionals to use the FOUR score to predict the early outcome of intubated patients with traumatic brain injuries.

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          Variations in mortality and length of stay in intensive care units.

          Catherine Draper, Brandie Wagner, W Knaus … (1993)
          To evaluate the amount of variation in in-hospital mortality and length of intensive care unit (ICU) stay that can be accounted for by clinical data available at ICU admission. Inception cohort study. Forty-two ICUs in 40 hospitals, including 26 hospitals that were randomly selected and 14 large tertiary care hospitals that volunteered for the study. A consecutive sample of 16,622 patients and 17,440 ICU admissions. Data on selected demographic characteristics, comorbidity, and specific physiologic variables were recorded during the first ICU day for an average of 415 admissions at each ICU; hospital discharge status (dead or alive) and length of ICU stay were recorded for individual patients; and the ratio of actual to predicted in-hospital mortality, standardized mortality ratios, and the ratio of actual to predicted length of ICU stay were recorded for individual ICUs. Unadjusted in-hospital mortality rates for the 42 units varied from 6.4% to 40%, and 90% (R2 = 0.90) of this variation was attributable to patient characteristics at admission. The standard mortality ratio varied from 0.67 to 1.25. The mean unadjusted length of ICU stay varied from 3.3 to 7.3 days, and 78% of the variation (R2 = 0.78) was attributed to patient and selected institutional characteristics. The best performing unit had a length of stay ratio of 0.88, whereas the poorest performing unit had a ratio of 1.21. Clinicians can use readily available admission data to adjust for considerable variations in patient severity and type in different ICUs. Such data should permit precise evaluation and comparison of ICU effectiveness and efficiency, which varied substantially in this study, and result in improved methods of risk prediction and evaluation of new medical practices.
          Article 0 comments Cited 65 times – based on 0 reviews     Review now
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            The epidemiology of traumatic brain injury: a review.

            J Bruns, Andreas W Hauser (2002)
            Traumatic brain injury (TBI) not only has considerable morbidity and mortality, but it is a major cause of epilepsy. We wish to determine the frequency of TBI, special groups at risk for TBI, and mortality from TBI. We reviewed studies of TBI that are either population based or derived from definable catchment areas that allow determination of incidence, identification of risk groups, and mortality. We review methodology used in epidemiologic studies of TBI and try to distinguish this data from that of head injury not necessarily affecting the brain. We report epidemiologic characteristics of TBI, including incidence, differences by age, gender, race and ethnic group, and geographic variation, and mortality. Population-based studies in the United States suggest that the incidence of TBI is between 180 and 250 per 100,000 population per year. Incidence may be higher in Europe and South Africa. There are groups at high risk for TBI. This includes males and individuals living in regions characterized by socioeconomic deprivation. There are selective age groups at risk for TBI. This includes the very young, adolescents and young adults, and the elderly. Mortality varies by severity but is high in those with severe injury and in the elderly. TBI is a major public health problem as well as a major cause of epilepsy. If primary prevention is to be undertaken, we must understand the epidemiology of the condition. The primary causes of TBI vary by age, socioeconomic factors, and geographic region, so any planned interventions must be tailored accordingly.
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              Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: a comparison between decision tree analysis and logistic regression.

              Carol S A Macmillan, Calvyn Howells, Peter J D Andrews … (2002)
              Decision tree analysis highlights patient subgroups and critical values in variables assessed. Importantly, the results are visually informative and often present clear clinical interpretation about risk factors faced by patients in these subgroups. The aim of this prospective study was to compare results of logistic regression with those of decision tree analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A decision tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score). Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logistic regression to determine the relative influence of patient age, admission Glasgow Coma Scale score, Injury Severity Score (ISS), pupillary response on admission, and insult duration. The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxemic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant. Using decision tree analysis, the authors found that hypotension and low cerebral perfusion pressure (CPP) are the best predictors of death, with a 9.2% improvement in predictive accuracy (PA) over that obtained by simply predicting the largest outcome category as the outcome for each patient. Hypotension was a significant predictor of poor outcome (GOS Score 1-3). Low CPP, patient age, hypocarbia, and pupillary response were also good predictors of outcome (good/poor), with a 5.1% improvement in PA. In certain subgroups of patients pyrexia was a predictor of good outcome. Decision tree analysis confirmed some of the results of logistic regression and challenged others. This investigation shows that there is knowledge to be gained from analyzing observational data with the aid of decision tree analysis.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Appl Basic Med Res
                Journal ID (iso-abbrev): Int J Appl Basic Med Res
                Journal ID (publisher-id): IJABMR
                Title: International Journal of Applied and Basic Medical Research
                Publisher: Medknow Publications & Media Pvt Ltd (India )
                ISSN (Print): 2229-516X
                ISSN (Electronic): 2248-9606
                Publication date (Print): May-Aug 2015
                Volume: 5
                Issue: 2
                Pages: 92-95
                Affiliations
                [1]Department of Nursing and Midwiferi Nasibeh, Mazandaran University of Medical Sciences, Sari, Iran
                [1 ]Education and Development Center, Mazandaran University of Medical Sciences, Sari, Iran
                Author notes
                Address for correspondence: Mr. Seyed Hossin Hossini, Department of Nursing, Mazandaran University of Medical Sciences, Sari, Iran. E-mail: gorjim29@ 123456yahoo.com
                Article
                Publisher ID: IJABMR-5-92
                DOI: 10.4103/2229-516X.157152
                PMC ID: 4456901
                SO-VID: ceb5081f-65c4-41a6-b794-f1858d06d161
                Copyright © Copyright: © International Journal of Applied and Basic Medical Research
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 09 September 2014
                Date accepted : 27 January 2015
                Categories
                Subject: Original Article

                Keywords: full outline of unresponsiveness,glasgow coma scale,intensive care unit,mortality,traumatic brain injuries
                Data availability:
                Keywords: full outline of unresponsiveness, glasgow coma scale, intensive care unit, mortality, traumatic brain injuries

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