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  IN THIS Article
 »  Abstract
 » Introduction
 » Patients and Methods
 » Results
 » Discussion
 » Conclusion
 » Acknowledgments
 »  References
 »  Article Figures
 »  Article Tables

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 Table of Contents    
RESEARCH ARTICLE
Year : 2014  |  Volume : 18  |  Issue : 6  |  Page : 369-375

The ability of two scoring systems to predict in-hospital mortality of patients with moderate and severe traumatic brain injuries in a Moroccan intensive care unit


Department of Anaesthesia, Emergency and Intensive Care Medicine, University Hospital Mohammed VI, Medical School of Marrakech, Cadi Ayyad University, Marrakech, Morocco

Date of Web Publication4-Jun-2014

Correspondence Address:
Houssam Rebahi
Ibn Tofail Hospital, Almoustachfa Street, Gueliz, Marrakech 40000
Morocco
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0972-5229.133895

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 » Abstract 

Aim of Study: We aim to assess and to compare the predicting power for in-hospital mortality (IHM) of the Acute Physiology and Chronic Health Evaluation-II (APACHE-II) and the Simplified Acute Physiology Score-II (SAPS-II) for traumatic brain injury (TBI). Patients and Methods: This retrospective cohort study was conducted during a period of 2 years and 9 months in a Moroccan intensive care unit. Data were collected during the first 24 h of each admission. The clinical and laboratory parameters were analyzed and used as per each scoring system to calculate the scores. Univariate and multivariate analyses through regression logistic models were performed, to predict IHM after moderate and severe TBIs. Areas under the receiver operating characteristic curves (AUROC), specificities and sensitivities were determined and also compared. Results: A total of 225 patients were enrolled. The observed IHM was 51.5%. The univariate analysis showed that the initial Glasgow coma scale (GCS) was lower in nonsurviving patients (mean GCS = 6) than the survivors (mean GCS = 9) with a statistically significant difference (P = 0.0024). The APACHE-II and the SAPS-II of the nonsurviving patients were higher than those of the survivors (respectively 20.4 ± 6.8 and 31.2 ± 13.6 for nonsurvivors vs. 15.7 ± 5.4 and 22.7 ± 10.3 for survivors) with a statistically significant difference (P = 0.0032 for APACHE-II and P = 0.0045 for SAPS-II). Multivariate analysis: APACHE-II was superior for predicting IHM (AUROC = 0.92). Conclusion: The APACHE-II is an interesting tool to predict IHM of head injury patients. This is particularly relevant in Morocco, where TBI is a greater public health problem than in many other countries.


Keywords: In-hospital mortality, prediction, traumatic brain injury


How to cite this article:
Nejmi H, Rebahi H, Ejlaidi A, Abouelhassan T, Samkaoui MA. The ability of two scoring systems to predict in-hospital mortality of patients with moderate and severe traumatic brain injuries in a Moroccan intensive care unit. Indian J Crit Care Med 2014;18:369-75

How to cite this URL:
Nejmi H, Rebahi H, Ejlaidi A, Abouelhassan T, Samkaoui MA. The ability of two scoring systems to predict in-hospital mortality of patients with moderate and severe traumatic brain injuries in a Moroccan intensive care unit. Indian J Crit Care Med [serial online] 2014 [cited 2017 Sep 20];18:369-75. Available from: http://www.ijccm.org/text.asp?2014/18/6/369/133895



 » Introduction Top


Traumatic brain injury (TBI) remains a major health problem in Morocco. It is a leading cause of morbimortality in the young adult. [1]

Early prediction of mortality after TBI is essential and may be useful in several areas. For instance, it can support early clinical decision-making, facilitate reliable stratification of these patients based on their prognoses, allocate accurately the resources and also it can help in communicating with patients families. [2],[3],[4],[5]

To reach this purpose, many studies have identified various prognostic factors that affect the outcome after head injury with varying degrees of correlation. [6],[7] Furthermore, a multiple prognostic models and scales, which vary in complexity and accuracy of prediction, [8],[9] have been created in order to predict clinical outcome for TBI, but they are not widely used, [3] because they were developed from relatively small samples of patients originating from a single center or region, their methodological quality is generally poor (with the exception of those developed by Hukkelhoven et al. and Signorini et al.) [10],[11] with lack of external validation, [12] and only a few are developed using populations from low and middle income countries, where most of trauma occurs. [3] Furthermore, they are not presented to physicians in a user-friendly way as they are not easy or simple to use and they are time-consuming to make.

Prognostic models such as Corticosteroid Randomization after Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) were found as useful tools, based on admission characteristics, to predict the risk of 6 months mortality and unfavorable outcomes in individual patients after moderate or severe TBI. [4] However, Medical Research Council CRASH Trial Collaborators found that the strength of predictors of outcomes in TBI varies according to whether patients are from high or low-middle income countries. [13]

We attempt to evaluate the discriminating capability of two scores daily used and more familiar in our practice, Acute Physiology and Chronic Health Evaluation-II (APACHE-II) and Simplified Acute Physiology Score-II (SAPS-II), [14],[15] to predict mortality in specific patient populations such those with TBI. The potential validities of these predicting systems can allow their application before in-hospital therapeutic interventions, especially in low- and middle-income countries where the resources are limited and where other prognostic models haven't been generalized yet.


 » Patients and Methods Top


Data collection

Our hospital (one of the three sites of our university teaching hospital) is a 409-bed tertiary care center in Marrakech, Morocco. It offers care to the population of the South. The 10-bed medical-surgical intensive care unit (ICU) has approximately 570 admissions a year. The hospital also has a coronary care unit and a cardiac surgical ICU. The ICU is run by full-time intensivists and has 24 h immediate access to other medical and surgical specialties. The nurse-to-patient ratio is approximately 1:2.

Records of 431 consecutive patients, who were admitted to our adult medical and surgical ICU during a period of 2 years and 9 months, were retrospectively examined, and 225 of these were included in this study. Inclusion criteria were: Patients admitted with a diagnosis of moderate or severe TBI (Glasgow coma scale [GCS] ≤12) at the time of hospital admission; patients aged 16 years or more; and patients admitted to the emergency room no more than 2 h after their injuries. Exclusion criteria were: Polytrauma (n = 109) and incomplete data gathering (n = 17). Furthermore, readmissions to ICU (n = 40), patients who died within the first 24 h of admission and who were transferred from a different hospital were excluded from the study (n = 40).

A careful review of all medical charts including laboratory results was carried out. Patients data observed during the first 24 h of their hospital stays were collected by experienced doctors to obtain the following variables: Demographics, neurological injury, temperature (°C), systolic and mean arterial blood pressure (mmHg), heart rate, respiratory rate, PaO 2 (mmHg) or FiO 2 , arterial pH and bicarbonate, serum sodium, potassium, urea and creatinine, urine output, serum white blood cell count, hematocrit, platelet count and bilirubin, age, type of admission, GCS score, presence of chronic diseases (chronic organ insufficiency) or immunocompromised state. Consequently, we selected the most abnormal value of each variable during the period between admission and the hour 24 of hospitalization. [14],[15] Assessment of the severity using GCS is problematic when ongoing sedation is needed. As poor GCS carries a heavy weight in both the APACHE-II and SAPS-II scores, the lowest initial recording of GCS before sedation was used.

For all patients, APACHE-II and SAPS-II scores were calculated. The risk of death was calculated as described in the original literature. [14],[15] The associated risks of in-hospital mortality (IHM) were derived using data from each patient's ICU stay and predictive equations of the respective scoring system.

The endpoint for the prognostic analysis was IHM in connection with the predicted mortalities of the scores.

Statistical analysis

Continuous variables were expressed as mean ± standard deviation and were compared using standard t-test. Categorical values were expressed in absolute and relative frequencies, and were analyzed using commercially available statistical software (Epi Info™ 6 developed by Centers for Disease Control and Prevention (CDC), USA). P > 0.05 were considered as nonstatistically significant. Predicted mortality was calculated using logistic regression formulae described in the original articles. [14],[15]

A receiver operating characteristic (ROC) curve was built for each severity index, and area under the ROC curve (AUROC) was used to test the ability of models to discriminate between patients who survived or patients who did not. [16],[17]


 » Results Top


Traumatic brain injury accounted for 27.5% of all admissions to ICU in the study period (431 out of 15.64 of total admissions). Study population demographics are shown in [Table 1]. 4.4% of all patients had one or more severe chronic illnesses. Cardiovascular disease was the leading chronic illness, followed by metabolic illness (diabetes mellitus) and immunosuppression [Table 1].
Table 1: General characteristics of 225 patients (distributed in two groups: Survivors and nonsurvivors) including
demographic data, comorbidity, scoring system data, ICU length of stay and ICU mortality


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Mean APACHE-II and SAPS-II values were 20.56 (range: 2-36) and 28.12 (range: 5-60), respectively. In comparison with survivors, nonsurvivors were older, had a longer lead time, ICU and hospital lengths of stay. They had higher APACHE-II, SAPS-II scores, and they had also lower GCS score. Univariate analysis [Table 1] showed that age, APACHE-II, SAPS-II and GCS scores were predictors for IHM in TBI patients.

Observed mortality during the hospital stay was 51.55% (116/225) with 40% mortality in ICU (90/225 patients). Both APACHE-II and SAPAS-II underestimated mortality for our patients' sample. APACHE-II system strongly correlated (Spearman's rank correlation coefficient, 0.98, P < 0.01). The mean values for the overall patients population, survivors and nonsurvivors, resulting from multivariate analysis, are listed in [Table 2].
Table 2: Univariate and multivariate analysis of the three severity scoring systems and comparison of their results

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Both APACHE-II and SAPS-II systems were accurate for predicting mortality with statistical significance. Furthermore, the higher score the greater risk of mortality. [Figure 1] and [Figure 2] show the ROC curves for the two scoring systems. APACHE-II showed increased total adjustment, with the highest AUROC (0.92), compared to 0.843 for the SAPS-II, sensitivity and specificity (respectively 76.4%; 82.7% against 69.8%; 78.5% for the SAPS-II). These data reflect the better discriminative power of the first system.
Figure 1: Discriminative ability of clinical prediction rules (outcome = death) derived from Acute Physiology and Chronic Health Evaluation-II scoring system

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Figure 2: Receiver operating characteristic curve for the Simplified Acute Physiology Score-II scoring system

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 » Discussion Top


Several scoring systems and prognostic models have been developed in the field of intensive care and emergency medicine over the last two decades to enable caregivers to quantify early the probability of survival of trauma patients and estimate the patients' severity illness. Furthermore, outcome prediction systems have become key tools to evaluate the care quality and the ICU performance. [16],[18]

Acute Physiology and Chronic Health Evaluation II and SAPS-II are two general scoring systems currently in common use for measuring the condition of individual ICU patients by numeric scores including multiple physiologic variables selected because of their impact on mortality: The sicker the patient, the more deranged the values and the higher the score. [14],[15]

Since its creation, APACHE-II has been designated as an accurate and exact predictor of mortality across a wide range of diagnostic populations, particularly in the ICU settings.

While APACHE-II score has been considered "invalid" in the trauma population, we hypothesized that APACHE-II would more accurately predict outcomes in critically brain injured patients in whom commonly used specific trauma scores have inherent limitations.

Due to the presence of cerebral insults, patients admitted to ICU tended to show more unfavorable outcomes compared with non-ICU patients and many studies have identified various prognostic factors that affect the outcome after head injury with varying degrees of correlation. [19]

The main findings in this study are that patient age and presedation GCS score were individually related to hospital death. Many previous studies have demonstrated that older patients have generally poor outcomes, and a low GCS score was associated with a more severe TBI and higher mortality, which were consistent with our results. [20],[21],[22],[23],[24],[25] Comorbidity has also been found to be a predictor of mortality in earlier studies. [20],[26],[27]

The preresuscitation GCS is still one of the important determinants in predicting mortality of TBI patients. The presedation GCS, is a clinical, simple, and practical tool to predict mortality in neurosurgical and head trauma patients. [28],[29],[30],[31] In fact, it has been the gold standard for effective assessment in these patients against, which the other grading systems are usually compared. [30] However, TBI remain an extremely heterogeneous disorder with unpredictable evaluative patterns. [20],[21],[22] Numerous studies documented the superiority of APACHE-II compared with GCS in predicting mortality due to its recognition of age and significant underlying problems. [33],[34],[35] In addition to that, APACHE-II and SAPS-II scores include this important factor (GCS) in their automated calculation tables. For those reasons, Zali et al. found that APACHE-II was better than GCS for mortality prediction in neurosurgical ICU patients. [36] That was true in two works conducted in Turkey and Korea. [37],[38]

Cho and Wang concluded that APACHE-III was better in assessing early outcomes than either the GCS or APACHE-II in neurosurgical intensive care: The AUROC curves of these scores were, respectively, 0.892, 0.868 et 0.826. [34]

In this paper, we investigated the discriminative power of APACHE-II and SAPS-II in predicting hospital mortality of moderate and severe TBI patients. In both systems, predicted mortality (the average of APACHE-II and SAPS-II were respectively 20.56 and 28.12) was much lower than actual mortality (51.55%). There are many reasons that could explain this result: On the one hand, most scoring systems have been constructed in general ICU populations and were therefore not validated for specific patients or groups. This has been especially true for TBI patients, who are younger and have fewer chronic health problems frequently seen in older patients, resulting in underestimated predicted mortality. [39] On the other hand, the higher rate of actual as compared to predicted mortality is attributable to the severity of intracranial injury on initial computed tomography scan, the longer mean admission time (1 h 48 min after the accident), and the poor quality of management on the field (sub optimal prehospital management) .

Simplified Acute Physiology Score II was the weakest predicting system with an underestimation of mortality and a low number of correctly categorized patients [Table 2]. In fact, GCS alone had a better predicting ability than SAPS-II. This may be considered surprising, since the impact of the GCS score is included in the SAPS-II system. We suggest that the SAPS-II system assigns more points to emergency surgical patients than to medical patients, supposing that the surgical patients are at a higher risk of death. However, for TBI patients the opposite is true: Patients managed surgically have better severity of illness-adjusted outcomes than patients medically managed.

In this study, the illness severity and death rate among medical patients were higher than those recorded for surgical patients. Despite the stratification ability of the APACHE-II system, it lacked accuracy in predicting death rates because the recorded death rate was higher than the predicted rate. Another multicenter study proved that for the overall estimation of aggregate ICU mortality of trauma patients, the APACHE-III system was the most reliable; however, performance was most accurate for subsets of patients with head trauma. [34],[35]

Wong et al. and Vassar et al. found that both the Trauma and Injury Severity Score (TRISS) and the APACHE-II systems were both poor predictors of the risk for hospital death among ICU trauma patients. [40],[41] On the other hand, in a recent work, the authors suggest that there is no difference between the sequential organ failure assessment (SOFA) score and APACHE-II and TRISS in predicting the outcomes of ICU trauma patients. Although their conclusion was limited by the fact that this work was a single center study; however, the method for calculating SOFA scores is easier and simpler than APACHE-II and TRISS. [42]

Overall, APACHE-II and TRISS did not meet acceptable thresholds of performance. [41] Furthermore, critically injured patients have physiologic derangements not accurately accounted for by commonly used trauma scores (TRISS, ISS etc.).

In this subset a more general ICU scoring system, for instance:APACHE-II is useful for risk adjustment, for research, administrative and quality improvement purposes. Similarly, a prospective cohort study of 1019 critically injured patients enrolled in the United States showed that APACHE-II was the best predictor of mortality (AUROC 0.77 vs. AUROC 0.54 for ISS and 0.64 for TRISS). [43]

Trauma and Injury Severity Score, as well as IMPACT and CRASH models, has shown a good discriminatory power in measuring outcomes in a Chinese work enrolling TBI patients in Hong Kong. [44]

Furthermore, the generalizability of the many published prognostic systems for early prediction of outcome in TBI requires external validation. [12],[45] Thus, the two prognostic models (IMPACT and CRASH) were recently cross-validated and externally validated in numerous trials where the samples were composed of patients from Western countries. [46],[47],[48],[49]

Our work was a contribution from a part of the world where TBI is a greater health problem than in many Western countries with high socioeconomic costs. However, there are some concerns. First the data came from a retrospective study, with its inherent biases. Second, our study was conducted in a single center. Third, we used admission parameters, which could vary in time. Furthermore, the main limitations were related to the high rates of patients' exclusion and death.


 » Conclusion Top


In our context, there is a need for a simple, easily applicable, objective bedside scoring system, based on well-established prognostic factors, to predict the short-term outcome of head injury. This will help in the efficient use of resources and in communicating with the families of the victims. This study demonstrated that the APACHE-II score had a better discriminative power for early accurate prediction of outcome after moderate and severe head injuries, especially in the Moroccan health system. However, due to the underestimation of mortality and to the extremely high death and exclusion rates, the results of this work cannot be generalized to any other populations. Thus, there is a need for further multicenter studies to allow trauma care providers to set uniform standards for predicting outcomes in traumatic brain injury particularly in low-and middle-income countries.


 » Acknowledgments Top


The authors would like to acknowledge the considerable help provided by the Laboratory of Epidemiology located in the Faculty of Medicine and Pharmacy of Marrakech. Therefore, we would like to thank Prof. M. AMINE for his contribution and his advice during the preparation of this manuscript.

 
 » References Top

1.Corrigan JD, Selassie AW, Orman JA. The epidemiology of traumatic brain injury. J Head Trauma Rehabil 2010;25:72-80.  Back to cited text no. 1
    
2.Young GB. Traumatic brain injury: The continued quest for early prognostic determination. Crit Care Med 2010;38:325-6.  Back to cited text no. 2
[PUBMED]    
3.Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak 2006;6:38.  Back to cited text no. 3
    
4.Steyerberg EW, Mushkudiani N, Perel P, Butcher I, Lu J, McHugh GS, et al. Predicting outcome after traumatic brain injury: Development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008;5:e165.  Back to cited text no. 4
    
5.Ramesh VG, Thirumaran KP, Raja MC. A new scale for prognostication in head injury. J Clin Neurosci 2008;15:1110-3.  Back to cited text no. 5
    
6.Jennett B, Teasdale G, Braakman R, Minderhoud J, Heiden J, Kurze T. Prognosis of patients with severe head injury. Neurosurgery 1979;4:283-9.  Back to cited text no. 6
[PUBMED]    
7.Marshall LF, Gautille T, Klauber MR, Eisenberg HM, Jane JA, Luerssen TG, et al. The outcome of severe closed head-injury. J Neurosurg 1991;75:S28-36.  Back to cited text no. 7
    
8.Narayan RK, Greenberg RP, Miller JD, Enas GG, Choi SC, Kishore PR, et al. Improved confidence of outcome prediction in severe head injury. A comparative analysis of the clinical examination, multimodality evoked potentials, CT scanning, and intracranial pressure. J Neurosurg 1981;54:751-62.  Back to cited text no. 8
[PUBMED]    
9.Pillai SV, Kolluri VR, Praharaj SS. Outcome prediction model for severe diffuse brain injuries: Development and evaluation. Neurol India 2003;51:345-9.  Back to cited text no. 9
[PUBMED]  Medknow Journal  
10.Hukkelhoven CW, Steyerberg EW, Habbema JD, Farace E, Marmarou A, Murray GD, et al. Predicting outcome after traumatic brain injury: Development and validation of a prognostic score based on admission characteristics. J Neurotrauma 2005;22:1025-39.  Back to cited text no. 10
    
11.Signorini DF, Andrews PJ, Jones PA, Wardlaw JM, Miller JD. Adding insult to injury: The prognostic value of early secondary insults for survival after traumatic brain injury. J Neurol Neurosurg Psychiatry 1999;66:26-31.  Back to cited text no. 11
    
12.Hukkelhoven CW, Rampen AJ, Maas AI, Farace E, Habbema JD, Marmarou A, et al. Some prognostic models for traumatic brain injury were not valid. J Clin Epidemiol 2006;59:132-43.  Back to cited text no. 12
    
13.MRC CRASH Trial Collaborators, Perel P, Arango M, Clayton T, Edwards P, Komolafe E, et al. Predicting outcome after traumatic brain injury: Practical prognostic models based on large cohort of international patients. BMJ 2008;336:425-9.  Back to cited text no. 13
    
14.Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: A severity of disease classification system. Crit Care Med 1985;13:818-29.  Back to cited text no. 14
[PUBMED]    
15.Le Gall JR, Lemeshow S, Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 1993;270:2957-63.  Back to cited text no. 15
    
16.Guidet B, Aegerter PH. Indices de gravité et applications en reanimation (severity scores in intensive care medicine). Le Praticien en Anesthesie Reanimation 2009;13:6-18.  Back to cited text no. 16
    
17.Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.  Back to cited text no. 17
[PUBMED]    
18.Zimmerman JE, Shortell SM, Rousseau DM, Duffy J, Gillies RR, Knaus WA, et al. Improving intensive care: Observations based on organizational case studies in nine intensive care units: A prospective, multicenter study. Crit Care Med 1993;21:1443-51.  Back to cited text no. 18
    
19.Yi HJ, Kim YS, Ko Y, Oh SJ, Kim KM, Oh SH. Factors associated with survival and neurological outcome after cardiopulmonary resuscitation of neurosurgical intensive care unit patients. Neurosurgery 2006;59:838-45.  Back to cited text no. 19
    
20.Lingsma HF, Roozenbeek B, Steyerberg EW, Murray GD, Maas AI. Early prognosis in traumatic brain injury: From prophecies to predictions. Lancet Neurol 2010;9:543-54.  Back to cited text no. 20
    
21.Menon DK, Zahed C. Prediction of outcome in severe traumatic brain injury. Curr Opin Crit Care 2009;15:437-41.  Back to cited text no. 21
    
22.Petroni G, Quaglino M, Lujan S, Kovalevski L, Rondina C, Videtta W, et al. Early prognosis of severe traumatic brain injury in an urban Argentinian trauma center. J Trauma 2010;68:564-70.  Back to cited text no. 22
    
23.Utomo WK, Gabbe BJ, Simpson PM, Cameron PA. Predictors of in-hospital mortality and 6-month functional outcomes in older adults after moderate to severe traumatic brain injury. Injury 2009;40:973-7.  Back to cited text no. 23
[PUBMED]    
24.Mitra B, Cameron PA, Gabbe BJ, Rosenfeld JV, Kavar B. Management and hospital outcome of the severely head injured elderly patient. ANZ J Surg 2008;78:588-92.  Back to cited text no. 24
    
25.Mosenthal AC, Lavery RF, Addis M, Kaul S, Ross S, Marburger R, et al. Isolated traumatic brain injury: Age is an independent predictor of mortality and early outcome. J Trauma 2002;52:907-11.  Back to cited text no. 25
    
26.Camilloni L, Farchi S, Giorgi Rossi P, Chini F, Borgia P. Mortality in elderly injured patients: The role of comorbidities. Int J Inj Contr Saf Promot 2008;15:25-31.  Back to cited text no. 26
    
27.Colantonio A, Escobar MD, Chipman M, McLellan B, Austin PC, Mirabella G, et al. Predictors of postacute mortality following traumatic brain injury in a seriously injured population. J Trauma 2008;64:876-82.  Back to cited text no. 27
    
28.Kung WM, Tsai SH, Chiu WT, Hung KS, Wang SP, Lin JW, et al. Correlation between Glasgow coma score components and survival in patients with traumatic brain injury. Injury 2011;42:940-4.  Back to cited text no. 28
    
29.Livingston BM, Mackenzie SJ, MacKirdy FN, Howie JC. Should the pre-sedation Glasgow Coma Scale value be used when calculating acute physiology and chronic health evaluation scores for sedated patients? Scottish Intensive Care Society Audit Group. Crit Care Med 2000;28:389-94.  Back to cited text no. 29
    
30.Ting HW, Chen MS, Hsieh YC, Chan CL. Good mortality prediction by Glasgow Coma Scale for neurosurgical patients. J Chin Med Assoc 2010;73:139-43.  Back to cited text no. 30
    
31.Healey C, Osler TM, Rogers FB, Healey MA, Glance LG, Kilgo PD, et al. Improving the Glasgow Coma Scale score: Motor score alone is a better predictor. J Trauma 2003;54:671-8.  Back to cited text no. 31
    
32.Foreman BP, Caesar RR, Parks J, Madden C, Gentilello LM, Shafi S, et al. Usefulness of the abbreviated injury score and the injury severity score in comparison to the Glasgow Coma Scale in predicting outcome after traumatic brain injury. J Trauma 2007;62:946-50.  Back to cited text no. 32
    
33.Alvarez M, Nava JM, Rué M, Quintana S. Mortality prediction in head trauma patients: Performance of Glasgow Coma Score and general severity systems. Crit Care Med 1998;26:142-8.  Back to cited text no. 33
    
34.Cho DY, Wang YC. Comparison of the APACHE III, APACHE II and Glasgow Coma Scale in acute head injury for prediction of mortality and functional outcome. Intensive Care Med 1997;23:77-84.  Back to cited text no. 34
    
35.Cho DY, Wang YC, Lee MJ. Comparison of APACHE III, II and the Glasgow Coma Scale for prediction of mortality in a neurosurgical intensive care unit. Clin Intensive Care 1995;6:9-14.  Back to cited text no. 35
    
36.Zali AR, Seddighi AS, Seddighi A, Ashrafi F. Comparison of the Acute Physiology and Chronic Health Evaluation Score (APACHE) II with GCS in predicting hospital mortality of neurosurgical intensive care unit patients. Glob J Health Sci 2012;4:179-84.  Back to cited text no. 36
    
37.Dalgiç A, Ergüngör FM, Becan T, Elhan A, Okay O, Yüksel BC. The revised Acute Physiology and Chronic Health Evaluation System (APACHE II) is more effective than the Glasgow Coma Scale for prediction of mortality in head-injured patients with systemic trauma. Ulus Travma Acil Cerrahi Derg 2009;15:453-8.  Back to cited text no. 37
    
38.Park SK, Chun HJ, Kim DW, Im TH, Hong HJ, Yi HJ. Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II in predicting hospital mortality of neurosurgical intensive care unit patients. J Korean Med Sci 2009;24:420-6.  Back to cited text no. 38
    
39.Vassar MJ, Wilkerson CL, Duran PJ, Perry CA, Holcroft JW. Comparison of APACHE II, TRISS, and a proposed 24-hour ICU point system for prediction of outcome in ICU trauma patients. J Trauma 1992;32:490-9.  Back to cited text no. 39
    
40.Wong DT, Barrow PM, Gomez M, McGuire GP. A comparison of the Acute Physiology and Chronic Health Evaluation (APACHE) II score and the Trauma-Injury Severity Score (TRISS) for outcome assessment in intensive care unit trauma patients. Crit Care Med 1996;24:1642-8.  Back to cited text no. 40
    
41.Vassar MJ, Lewis FR Jr, Chambers JA, Mullins RJ, O′Brien PE, Weigelt JA, et al. Prediction of outcome in intensive care unit trauma patients: A multicenter study of Acute Physiology and Chronic Health Evaluation (APACHE), Trauma and Injury Severity Score (TRISS), and a 24-hour intensive care unit (ICU) point system. J Trauma 1999;47:324-9.  Back to cited text no. 41
    
42.Hwang SY, Lee JH, Lee YH, Hong CK, Sung AJ, Choi YC. Comparison of the sequential organ failure assessment, Acute Physiology and Chronic Health Evaluation II scoring system, and Trauma and Injury Severity Score method for predicting the outcomes of intensive care unit trauma patients. Am J Emerg Med 2012;30:749-53.  Back to cited text no. 42
    
43.Dossett LA, Redhage LA, Sawyer RG, May AK. Revisiting the validity of APACHE II in the trauma ICU: Improved risk stratification in critically injured adults. Injury 2009;40:993-8.  Back to cited text no. 43
    
44.Wong GK, Teoh J, Yeung J, Chan E, Siu E, Woo P, et al. Outcomes of traumatic brain injury in Hong Kong: validation with the TRISS, CRASH, and IMPACT models. J Clin Neurosci 2013;20:1693-6.  Back to cited text no. 44
    
45.Mushkudiani NA, Hukkelhoven CW, Hernández AV, Murray GD, Choi SC, Maas AI, et al. A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes. J Clin Epidemiol 2008;61:331-43.  Back to cited text no. 45
    
46.Roozenbeek B, Lingsma HF, Lecky FE, Lu J, Weir J, Butcher I, et al. Prediction of outcome after moderate and severe traumatic brain injury: External validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models. Crit Care Med 2012;40:1609-17.  Back to cited text no. 46
    
47.Panczykowski DM, Puccio AM, Scruggs BJ, Bauer JS, Hricik AJ, Beers SR, et al. Prospective independent validation of IMPACT modeling as a prognostic tool in severe traumatic brain injury. J Neurotrauma 2012;29:47-52.  Back to cited text no. 47
    
48.Lingsma H, Andriessen TM, Haitsema I, Horn J, van der Naalt J, Franschman G, et al. Prognosis in moderate and severe traumatic brain injury: External validation of the IMPACT models and the role of extracranial injuries. J Trauma Acute Care Surg 2013;74:639-46.  Back to cited text no. 48
    
49.Yeoman P, Pattani H, Silcocks P, Owen V, Fuller G. Validation of the IMPACT outcome prediction score using the Nottingham Head Injury Register dataset. J Trauma 2011;71:387-92.  Back to cited text no. 49
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2]



 

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Online since 7th April '04
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