» » » √ Stress Berat Scoring Systems
,

√ Stress Berat Scoring Systems

Characterization of injury severity is crucial to the scientific study of trauma, yet the actual measurement of injury severity began only 50 years ago. In 1969, researchers developed the Abbreviated Injury Scale (AIS) to grade the severity of individual injuries. Since its introduction, by the Association for the Advancement of Automotive Medicine (AAAM) International Injury Scaling Committee (IISC), the parent organization of the AIS modified the AIS, most recently in 2005 (AIS-2005). The AIS is the basis for the Injury Severity Score (ISS), which is the most widely used measure of injury severity in patients with trauma. Attempting to summarize the severity of injury in a patient with multiple traumas with a single number is difficult at best; therefore, multiple alternative scoring systems have been proposed, each with its own problems and limitations.


This article reviews the conceptual and statistical background necessary to understand injury severity scoring, presents the most common scoring systems, and addresses new ideas and trends in syok scoring.


 



An accurate method for quantitatively summarizing injury severity has many potential applications. The ability to predict outcome from syok (ie, mortality) is perhaps the most mendasar use of injury severity scoring, a use that arises from the patient’s and the family’s desires to know the prognosis. More recently, physicians suggested that injury severity scoring can provide objective information for end-of-life decision-making and resource allocation. Trauma mortality prediction in individual patients by any scoring system is limited and is in general no better than good clinical judgment. Therefore, decisions for individual patients should never be based solely on a statistically derived injury severity score. However, scoring systems can serve to estimate quantitatively the level of acuity of injured patients that are applied to adjustments in hospital outcome assessments.


Field syok scoring also is used to facilitate rational prehospital triage decisions, thereby minimizing the time from injury occurrence to definitive management. Similarly, physicians suggest that it can enhance appropriate use of helicopters and timely transfer of severely injured patients to syok wards. Trauma scoring also is used for quality assurance by allowing evaluation of syok care both within and between syok centers, a contentious and controversial area that is likely to only increase in importance.


Perhaps the most important role for injury severity scoring is in syok care research. Scientific study of the epidemiology of syok and syok outcomes would not be possible otherwise. Injury severity scoring is indispensable in stratifying patients into comparable groups for prospective clinical trials. Similarly, this technique can be used retrospectively to identify and control for differences in baseline injury severity between patient populations.


Continuous quality improvement is an integral component of syok center care. Assessing outcomes objectively is challenging, but the evolution of injury severity scoring systems with sufficient precision and reproducibility now permits syok centers to compare their processes and outcomes, facilitating identification of best practices that form the foundations of quality improvement programs.1


 



Fundamentally, syok outcome prediction is a multivariate problem. Researchers use multiple independent variables (eg, age, injury severity) to predict the dependent variable (or outcome). Most physicians are familiar with the simplest form of regression analysis, simple linear regression, which describes the linear relationship between 2 variables. Multiple regression is an extension of this technique, in which more than one independent variable is used to describe a single, continuous dependent variable. Multiple regression is advantageous because it allows one to measure the association between a predictor variable and an outcome variable while controlling for other modifying factors. Researchers use multiple regression, therefore, to control for the effects of many variables and assess the independent effect of a single variable.


In syok severity scoring, mortality is the outcome that has elicited the most interest. Mortality is a dichotomous variable having only 2 possible values, death or survival. Although several methods are available, multiple logistic regression is the most popular approach when the outcome of interest is dichotomous because of some unique advantages of multiple logistic regression.


Odds is a ratio of the probability that a certain event under consideration will occur to the probability that it will not occur.


As an example, out of 100 patients aged 65-74 years who sustain blunt abdominal trauma, 10 will die, while out of 50 patients aged 75-84 years, 15 will die. The probability of death in the age group of 65-74 years is 20/100=0.2 (20%) and in the age group of 75-84 years is 15/50=0.3 (30%).


Therefore, the odds of a patient who is between the ages of 65-74 years dying after blunt syok is 0.2/1-0.2=0.25 (25%). In contrast, the odds of a patient who is between the ages of 75-84 years dying after blunt syok is 0.3/1-0.3=0.4 (40%).


If the probability of an event occurring is equal to the probability that it will not occur, then the odds that the event will occur is 0.5/1-0.5=1 (eg, the odds of “heads” appearing after tossing a coin is 1).


A ratio of two odds is called an odds ratio. A ratio of two odds greater than 1.0 indicates an increased risk for the outcome, whereas an odds ratio of less than 1.0 indicates a decreased risk or protective effect for the outcome. For each age group, one group is typically classified as the reference group and comparisons are made to this group.


For example, the age group of 65-74 years could be used as the reference group because it is expected that increasing age would be associated with a greater possibility of dying after blunt abdominal trauma. To compare the age group of 65-74 years with an older age group, odds ratios are calculated. The odds of the age group of 75-84 years divided by the odds of the age group of 65-74 years produces an odds ratio of 1.2 (0.3/0.25). With an odds ratio of greater than 1.0, this suggests that patients between the ages of 75-84 years are 1.2 times more likely to die after blunt abdominal syok when compared to patients between the ages of 65–74 years.


Logistic regression analysis is a statistical tool that uses these types of analyses to explore the relationship between multiple variables and outcomes. Mathematical models can then be constructed based on identification of clinical parameters that predict outcome. Most clinical scoring systems are based on these types of mathematical modeling. Any clinical variable that has been given a particular score will effect the determination of outcome (almost always either alive or dead) based on the influence of that clinical variable itself on mortality and the influence of other clinical variables.


Logistic regression is mathematically convenient in that one can easily convert the coefficients of the equation into estimates of the risk of developing a disease or outcome given the presence of a particular risk factor. Researchers adjust these risk estimates for the effects of other risk factors or covariates included in the logistic regression equation.


Outcome prediction never will be perfect, in part because injury severity is difficult to quantify. Perhaps more important is that the patient’s response to injury is complex and difficult to model adequately; therefore, multiple scoring systems emerged.


Practitioners should be able to assess the predictive performance of each system in order to compare them. Measures of predictive performance include explanatory power, discrimination, and calibration.


Explanatory power is that proportion of the prediction outcome that can be explained by the model rather than by variation. This is reflected by the coefficient of determination (r2).


Discrimination is the ability of the model to separate the patients into 2 groups; for example, those who survive and those who die. This involves sensitivity, specificity, and accuracy, which are concepts well understood by most physicians. However, when applied to predictive models, these concepts can be problematic. A syok survival predictive model yields a probability of survival; while in reality, patients can only live or die. Therefore, a prediction rule must be established; typically, researchers assign a cutoff point of 0.5. Patients with a probability of survival greater than 0.5, therefore, are predicted to have lived, while those with a probability of survival less than or equal to 0.5 are predicted to have died. The masalah is that sensitivity, specificity, and accuracy all vary depending on a prediction rule chosen.


Receiver operating characteristic (ROC) curve analysis can help evaluate the accuracy and discrimination of a predictive model over a wide range of cutoff points. The ROC curve is constructed by plotting the sensitivity on the y-axis and (1 – specificity) on the x-axis at different cutoff points. The area under the ROC curve measures the accuracy of the model. A straight line arising from the origin at a 45° angle has an area under the curve of 0.5 and represents accuracy no better than flipping a coin. A perfect predictive model has an area under the curve of 1.0. As accuracy and discrimination improve, the ROC curve moves upward and to the left. ROC curves allow one to compare different predictive models used in the same population of patients.


Calibration is the ability of the model to correctly predict outcome over the entire range of risk. Calibration can be assessed graphically by plotting the actual outcome against the predicted outcome. Calibration is assessed statistically by goodness-of-fit testing, most commonly the Hosmer-Lemeshow test. This test involves grouping patients into risk categories and using a modified chi-square analysis to compare the observed and predicted outcomes in each group. The hypothesis tested is that the model’s predictions are the same as the actual outcome; therefore, higher P values are desired and reflect a good fit.


 



The Revised Trauma Score (RTS) is one of the more common physiologic scores in use. It combines 3 specific, commonly assessed clinical parameters, as follows: (1) Glasgow Coma Scale (GCS), (2) systolic blood pressure (SBP), and (3) respiratory rate (RR).


The magnitude of derangement in each parameter is scored from 0-4. The RTS has 2 forms depending on its use. When used for field triage, the RTS is determined by adding each of the coded values together. Thus, the RTS ranges from 0-12 and is easily calculated. See Table 1.


 


Table 1. Revised Trauma Score












































Coded Value



GCS



SBP (mm Hg)



RR (breaths/min)



0



3



0



0



1



4-5



< 50



< 5



2



6-8



50-75



5-9



3



9-12



76-90



>30



4



13-15



>90



10-30



 


An RTS of less than 11 is used to indicate the need for transport to a designated syok center. The coded form of the RTS is used more frequently for quality assurance and outcome prediction. The coded RTS is calculated as follows, in which SBPc, RRc, and GCSc represent the coded values of each variable:


RTSc = 0.9368 GCSc + 0.7326 SBPc + 0.2908 RRc

Obviously, this value is more complicated to compute, which limits its usefulness in the field. The main advantage of the coded RTS is that the weighting of the individual components emphasizes the significant impact of traumatic brain injury on outcome.


The RTS has several limitations that affect its usefulness, and most of these limitations are related to the GCS. As originally described, the GCS was intended to measure the functional status of the central nervous system. Because of the importance of head injury in determining syok outcome, the GCS also is used by many as a component of syok severity scoring. Problems inherent to the GCS (and RTS) include the inability to accurately score patients who are intubated and mechanically ventilated (which can often happen prior to making a triage decision).


Moreover, patients who are pharmacologically paralyzed or who are under the influence of alcohol or illicit drugs also are difficult to score. Alternative approaches in this setting include using the best motor response and the eye-opening response to calculate or predict the mulut response. Research has shown that substitution of the best motor response for the GCS results in no loss of predictive capability. More recently, researchers have shown that the best motor response predicts syok mortality as well as or better than other syok severity scores.


 



The Acute Physiology & Chronic Health Evaluation (APACHE) was introduced in 1981. It is applied within 24 hours of a patient’s admission to the ICU. An integer score from 0-71 is computed based on several measurements. APACHE characterizes syok patients inadequately, although different versions of this scoring system are used widely for the assessment of illness severity in surgical intensive care units. This system has 2 components, as follows: (1) the chronic health evaluation, which incorporates the influence of comorbid conditions (eg, diabetes mellitus, cirrhosis, chronic renal failure, heart disease malignancy), and (2) the Acute Physiology Score (APS). The most recent versions of the major 4dukt ICU prognostic systems include APACHE III, APACHE IV, Simplified Acute Physiology Score (SAPS) 3, and the ICU Admission Mortality Probablility Model.2


The APS consists of weighted variables representing the major physiologic systems, including neurologic, cardiovascular, respiratory, renal, gastrointestinal, metabolic, and hematologic variables. In 1985, the APACHE system was revised (ie, APACHE II) by reducing the number of APS variables from 34 to 12, restricting the comorbid conditions, and deriving coefficients for specific diseases. A representative calculation for a hypothetical patient is shown in Table 2.


 


Table 2. Representative APACHE II Calculation for Hypothetical Patient





















































































Total



 



30



Parameter



Representative Measure



APACHE II Value



Glasgow Coma Scale



13



2



Age



56



3



Mean arterial pressure (mmHg)



57



2



PaO2 (FIO2< 0.5)



60



3



[K+] (mmol/L)



4.0



0



WBC x 1,000/cm3



20



2



Heart rate (beats/min)



140



3



Respiratory rate (breaths/min)



35



3



pH (arterial)



7.22



3



[creatinine] (mg/dL)



1.7



4



Core temperature (C°)



39.2



3



[Na+] (mmol/L)



148



0



Hematocrit (%)



28



2



 


The chronic health assessment is chronic obstructive pulmonary disease (score=5). The APACHE II total score is 35; this score is associated with a death rate of 83.1%. Therefore, approximately 8 out of 10 patients with this score do not survive.


APACHE II is the most widely applied APACHE system; however, it has several potential limitations. The computation of APACHE II scores requires large amounts of data to be reviewed and analyzed. However, it is possible to process this information accurately, portably, and reproducibly at the bedside with handheld smartphones or other portable computer devices with appropriate software. APACHE II calculators are now incoporated into some electronic medical record systems.


The GCS, which forms a powerful predictive component of the APS, was not intended to reflect extracranial injuries. Being a relatively younger population, comorbidity is unusual in these patients and the potential exists for lead-time bias. By using only ICU data and not accounting for prior treatment, APACHE II underestimates mortality in patients who are transferred to the ICU after relative stabilization. Patients with syok frequently are resuscitated in the emergency department or operating room prior to admission to the ICU.


Patients with syok comprise only 8% of the population used to develop APACHE II, with only a 9% case-fatality rate. Moreover, 85% of syok fatalities were related to traumatic brain injury. In 1992, researchers showed that APACHE II is inferior to the Trauma and Injury Severity Score (TRISS) in predicting mortality in injured patients. Poor performance was related largely to the absence of an anatomic component in the APACHE system.


 



The sequential organ failure assessment (SOFA) score is a scoring system to determine the extent of a person’s organ function or the rate of failure in critically ill patients. Regular, repeated scoring enables patient condition and disease development to be monitored.


The score is based on 6 different parameters, as follows: respiratory system (PaO2/FiO2, mm Hg), cardiovascular system (blood pressure/vasopressors), hepatic system (bilirubin, mg/dL), coagulation system (plateletsX103/mm3), renal system (creatinine, mg/dL), and neurological system (Glasgow Coma Scale).


 



The systemic inflammatory response syndrome (SIRS) score is a generalized response to nonspecific insults, including infections, pancreatitis, trauma, and burns.


To calculate a SIRS score, each of the following components is assigned 1 point: fever or hypothermia (temperature, >38°C or < 36°C), tachypnea (respiratory rate, >20 breaths/min or PaCO2< 32 mm Hg), tachycardia (heart rate, >90 beats/min), and leukocytosis or leukopenia (WBC count, >12,000/mm3 or < 4,000/mm3, or presence of 10% bands). Thus, a SIRS score can range from 0-4.


 



Raum et al developed the emergency syok score (EMTRAS), which uses parameters that are available within 30 minutes, does not require knowledge of anatomic injuries, and accurately predicts mortality.3 EMTRAS comprises 4 parameters: patient age, Glasgow Coma Scale, base excess, and prothrombin time (PT). Scores of 0, 1, 2, and 3, respectively, were assigned as follows: age: < 40 years, 40-60 years, 61-75 years, and >75 years; Glasgow Coma Scale: 13-15, 10-12, 6-9, and 3-5; base excess: >-1, -5 through -1, -10 through -5.1, and 10 or less; and prothrombin time: < 80%, 80-50%, 49-20%, and >20%.3 Validation of EMTRAS in 3,314 patients showed that the area under the receiver operating characteristic curve (AUC) was 0.828. Results of the study showed that EMTRAS accurately predicted mortality and that knowledge of the anatomic injury is not needed.3


 



Numerous scores are based on the characterization of injuries anatomically, as outlined below.



  • Abbreviated Injury Score (AIS)

  • Injury Severity Score (ISS)

  • New Injury Severity Score (NISS)

  • Anatomic Profile (AP)

  • Penetrating Abdominal Trauma Index (PATI)

  • ICD-based Injury Severity Score (ICISS)

  • Trauma Mortality Prediction Model (TMPM-ICD9)


 



The AIS is a simple numerical method for grading and comparing injuries by severity. Although originally intended for use with vehicular injuries, its scope is increasingly expanded to include other injuries. The AIS is a consensus-derived, anatomically based system of grading injuries on an ordinal scale ranging from 1 (minor injury) to 6 (lethal injury). Scales for all anatomic regions and organs can be found at the American Association for the Surgery of Trauma Web site. AIS Manuals and CDs (2005) are available from the AAAM list of publications.


The predictive validity and reliability of the AIS and its derivatives have been demonstrated, but their widespread use in population-based research is hindered by the cost of AIS coding.4 The AIS does not reflect the combined effects of multiple injuries; however, it forms the foundation for the ISS. Baker et al introduced the ISS in 1974 as a means of summarizing multiple injuries in a single patient.5 The ISS is defined as the sum of squares of the highest AIS grade in the 3 most severely injured body regions. Six body regions are defined, as follows: the thorax, abdomen and visceral pelvis, head and neck, face, bony pelvis and extremities, and external structures. Only one injury per body region is allowed. The ISS ranges from 1-75, and an ISS of 75 is assigned to anyone with an AIS of 6. An example of an ISS calculation is shown in Table 3.


 


Table 3. ISS Calculation





















































Injury Severity Score (ISS) = 50



Region



Injury



AIS



AIS2



Head/Neck



Single cerebral contusion



3



9



Face



No injury



0



0



Chest



Flail chest



4



16



Abdomen



1. Liver laceration


2. Completely shattered spleen



4


5



25


16



Extremity



Fractured femur



3



9



External



No injury



0



0



 


The ISS has several limitations. The most obvious limitation is its inability to account for multiple injuries to the same body region. Furthermore, it limits the total number of contributing injuries to only 3 and, consequently, as shown in the table below, often omits significant injuries altogether. The ISS weights injuries to each body region equally, disregarding the importance of head injuries in mortality. Moreover, mortality is not strictly an increasing function of the ISS. The mortality rate for an ISS of 16, therefore, is higher than the mortality rate for an ISS of 17 because of the different combinations of AIS scores that comprise each. Finally, an idiosyncrasy of the ISS is that many ISS values cannot occur, while other ISS values can result from multiple different combinations of AIS values. This degrades the precision of individual scores and, thus, reduces the predictive power of ISS.


Although the classic use of the ISS is to predict mortality from trauma, the ISS also has been noted to be a consistent risk factor predictor for postinjury multiple-organ failure (MOF). In developing predictive models for MOF, researchers categorized risk factors as related to tissue injury severity, cellular shock severity, the magnitude of the systemic inflammatory response to the injury, and host factors (eg, age, sec, comorbidity). Tissue injury severity is a major component of these predictive models, and it is readily quantifiable using the ISS. Recognizing the limitations of the ISS, researchers subsequently investigated the Anatomic Profile (AP) as an alternative measure of tissue injury severity, observing that the AP offered no advantage over the ISS in predicting postinjury MOF. Moreover, they found the AP difficult to calculate with greater interrater variability compared to the ISS.


Recently, Osler et al reported a modified ISS (new ISS or NISS) based on the 3 most severe injuries regardless of body region.6 This simple but significant modification of the ISS avoids many of its previously acknowledged limitations. By preserving the AIS as the framework for injury severity scoring, the NISS remains familiar and user-friendly. Preliminary studies suggest that the NISS is a more accurate predictor of syok mortality than the ISS, particularly in penetrating trauma. Other researchers demonstrated that the NISS is superior to the ISS as a measure of tissue injury in predictive models of postinjury MOF. Osler et al recommend that the NISS replace the ISS as the standard anatomic measure of injury severity.6


 



The AP was developed in response to the limitations of the ISS. Unlike the ISS, the AP includes all serious injuries in a body region. Moreover, the AP appropriately weights head and torso injuries more heavily than other body regions. This index summarizes all serious injuries (AIS greater ≥3) into 3 categories. Category A includes the head and spinal cord. Category B encompasses the thorax and anterior neck. Category C includes all remaining serious injuries. A fourth category, category D, summarizes all nonserious injuries.


Practitioners calculate each component as the square root of the sum of squares of the AIS scores of all serious injuries within each region. A region with no injury receives a score of zero. Using logistic regression, these AP component values are used to calculate a probability of survival. The AP performs better than the ISS in discriminating survivors from nonsurvivors and may provide a more rational basis for comparing injury severity between patients. However, the AP failed to garner much interest or support, probably due to its computational complexity and only modest improvement in predictive performance.


 



This score is used to calculate the risk of complications in patients undergoing celiotomy for penetrating abdominal trauma. Fourteen organs are examined and assigned a risk factor from 1-5 (eg, pancreas=5, spleen=3, bladder=1). Injuries to any organ are graded by severity from 1 for minimal injury (eg, tangential wound to the pancreas) to 5 for maximal injury (eg, pancreatic proximal duct disruption). The severity grade is multiplied by the risk factor; the akibat penetrating score is obtained by summing the individual organ scores. A PATI of greater than 25 is associated with a complication rate of approximately 50%. This score can be used to compare complication rates between different institutions.


 



An alternative to the ISS, the ICISS, was proposed by Osler et al in 1996.7 Unlike the ISS, which is founded on expert consensus-based determination of injury severity, the ICISS is based on ICD-9 Clinical Modification (CM) codes (800-989, excluding burns [940-949], late effects of injury [905-909.9], and unspecificied injury [959-959.9]). AIS score maps can be derived from software (ICDMAP) that maps ICD-9 coded discharges into AIS and ISS.8 This method is termed ICD-9 Injury and determines survival risk ratios (SRRs) calculated for each ICD-9 discharge diagnosis. SRRs are derived by dividing the number of survivors in each ICD-9 code by the total number of patients with the same ICD-9 code. ICISS is calculated as the simple product of the SRRs for each of the patient’s injuries.


ICISS has some advantages over the ISS. First, it represents a true continuous variable that takes on values between 0 and 1. Second, it includes all injuries. Third, ICD-9 codes are readily available and do not require special pembinaan or expertise to determine. Finally, initial observations suggest that ICD-9 has better predictive power when compared to the ISS.


Moreover, ICISS has the potential to better account for the effects of comorbidity on outcome by including the SRR for each comorbidity present. Recent observations have suggested that the ICISS outperforms the ISS in outcome predictions of interest (eg, hospital length of stay, hospital charges). Despite the apparent advantage of the ICISS, however, it has not yet replaced other methods of outcome analysis. In addition, further validation is needed before it can be used widely. Moreover, in 2008, the American College of Surgeons adopted the National Trauma Data Standard (NTDS) for the National Trauma Data Bank. The NTDS mandates the use of ICD-9 CM codes to characterize injury diagnoses as a replacement for the AIS classification system.


 



A new ICD-9 -based injury model was proposed that replaces the simple ratio measurements with empiric measures of injury severity based on regression modeling. Because TMPM-ICD9 (like ICISS) is based on nearly universally available ICD-9 CM codes, it can be used by virtually any hospital caring for syok patients to adjust for case-mix. However, the potential limitation of ICD-9 CM-based scoring systems is that they are based on administrative data that are designed for hospital billing for all diseases and not for prediction modeling involving injury. The scoring of traumatic brain injury severity may be particularly problematic with ICD-9 CM based predictive models.9


 



The predictive capability of any model usually is improved with the inclusion of additional relevant information. Champion and colleagues exemplified this concept with the development of the TRISS.10 This test combines anatomic and physiologic measures of injury severity (ISS and RTS, respectively) and patient age in order to predict survival from trauma. Recognizing the difference between blunt and penetrating injury, researchers developed separate models for each mechanism. The logistic regression equation predicts the probability of survival, ie, P.


RTSc is the coded version of the RTS, and patient age is categorized such that age is equal to zero if the patient is younger than 55 years and age is equal to one otherwise. The coefficients will differ for blunt and penetrating trauma.


TRISS quickly became the standard methodology for outcome assessment. It appears to be valid for 4dukt and pediatric patients but has been criticized because (1) it is only moderately accurate for predicting survival; (2) problems already are noted with the ISS (eg, inhomogeneity, inability to account for multiple injuries to the same body region); (3) no information is incorporated related to preexisting conditions (eg, cardiac disease, chronic obstructive pulmonary disease, cirrhosis); (4) similar to the RTS, it cannot include intubated patients because respiratory rate and mulut responses are not obtainable; and (5) it does not incorporate an accounting for patient mix (making comparisons between syok centers difficult).


 



In an attempt to address these shortcomings, Champion et al introduced A Severity Characterization of Trauma (ASCOT) in 1990 as an improvement over TRISS.10 ASCOT uses the AP in place of the ISS and categorizes age into deciles. In addition, changes include the individual components of the coded RTS that were included as independent predictors in the akibat logistic regression model. Despite these modifications, the predictive performance of ASCOT is only marginally better than the ISS. This, coupled with the complex nature of the AP component, has discouraged widespread acceptance of ASCOT.


ICISS also is combined with age and the RTS in a manner similar to TRISS analysis. This model has superior predictive power and is better calibrated than TRISS. Moreover, this ICISS-based model is a superior predictor of resource utilization in injured patients.


 



Other scoring systems exist for specific concerns in syok patients. For example, improved survival in patients requiring massive transfusion (MT) is predicated on recognizing without delay when this intervention is required for a syok patient with ongoing bleeding. To determine at emergency department (ED) admission whether a patient will require MT, several scores of variable complexity have been developed recently that make use of physiologic criteria, including systolic blood pressure, heart rate, and temperature; laboratory parameters such as international normalized ration (INR), hemoglobin concentration, and base deficit; and other criteria such as mechanism of injury or focused assessment with sonography for syok (FAST) results.


MT predictive utilities are constructed by elucidating clinical variables at ED admission that are present in patients who are massively transfused, usually predefined as 10 U of blood within 24 hours of admission. This approach is biased by the fact that some severely hemorrhaging patients undergoing resuscitation nevertheless die before reaching 10 U, and therefore clinical specifics of these patients are not examined. An alternative approach involves discerning ED admission variables that characterize severe hemorrhage, which are then used to determine the probability a patient will need to be massively transfused. It has been suggested that this methodology may improve current scoring systems that will more precisely define the small but critical subset of exsanguinating syok patients who are in imminent danger of cardiovascular collapse, and for whom a damage control resuscitation (DCR) protocol should be activated.11


 



Despite its imperfections, syok severity scoring remains important for many reasons. ICISS may reflect a significant improvement in methodology, but this requires further validation. Scoring systems applied in intensive care units are not useful for predicting survival for the individual patient. Many models are used for audit purposes, and some are used as performance measures and quality indicators of a unit; however, both utilities are controversial because of poor adjustment of these systems to case mixtures. Moreover, existing severity scores are being used for purposes for which they are not intended (eg, decisions to withdraw support or on the allocation of resources). Continued research hopefully will improve methodology and make accurate syok prediction, particularly on an individual patient basis, a reality.


 




  1. Nathens AB, Cryer HG, Fildes J. The American College of Surgeons Trauma Quality Improvement Program. Surg Clin North Am. 2012 Apr. 92(2):441-54, x-xi.

  2. Breslow MJ, Badawi O. Severity scoring in the critically ill: part 1–interpretation and accuracy of outcome prediction scoring systems. Chest. 2012 Jan. 141(1):245-52.

  3. Raum MR, Nijsten MW, Vogelzang M, et al, for the Polytrauma Study Group of the German Trauma Society. Emergency syok score: an instrument for early estimation of syok severity. Crit Care Med. 2009 Jun. 37(6):1972-7.

  4. MacKenzie EJ. Injury severity scales: overview and directions for future research. Am J Emerg Med. 1984 Nov. 2(6):537-49.

  5. Baker SP, O’Neill B, Haddon W Jr, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974 Mar. 14(3):187-96.

  6. Osler T, Baker SP, Long W. A modification of the injury severity score that both improves accuracy and simplifies scoring. J Trauma. 1997 Dec. 43(6):922-5; discussion 925-6. 

  7. Osler T, Rutledge R, Deis J, Bedrick E. ICISS: an international classification of disease-9 based injury severity score. J Trauma. 1996 Sep. 41(3):380-6; discussion 386-8.

  8. MacKenzie EJ, Steinwachs DM, Shankar B. Classifying syok severity based on hospital discharge diagnoses. Validation of an ICD-9CM to AIS-85 conversion table. Med Care. 1989 Apr. 27(4):412-22.

  9. Sacco WJ, MacKenzie EJ, Champion HR, Davis EG, Buckman RF. Comparison of alternative methods for assessing injury severity based on anatomic descriptors. J Trauma. 1999 Sep. 47(3):441-6; discussion 446-7.

  10. Champion HR, Copes WS, Sacco WJ, Frey CF, Holcroft JW, Hoyt DB, et al. Improved predictions from a severity characterization of syok (ASCOT) over Trauma and Injury Severity Score (TRISS): results of an independent evaluation [see comments]. J Trauma. 1996 Jan. 40(1):42-8; discussion 48-9. 

  11. Rahbar MH, del Junco DJ, Huang H, et al. A latent class model for defining severe hemorrhage: experience from the PROMMTT study. J Trauma Acute Care Surg. 2013 Jul. 75(1 Suppl 1):S82-8.

  12. Balogh Z, Offner PJ, Moore EE, Biffl WL. NISS predicts postinjury multiple organ failure better than the ISS. J Trauma. 2000 Apr. 48(4):624-7; discussion 627-8.

  13. Boyd CR, Tolson MA, Copes WS. Evaluating syok care: the TRISS method. Trauma Score and the Injury Severity Score. J Trauma. 1987 Apr. 27(4):370-8.

  14. Brenneman FD, Boulanger BR, McLellan BA, Redelmeier DA. Measuring injury severity: time for a change?. J Trauma. 1998 Apr. 44(4):580-2.

  15. Cayten CG, Stahl WM, Murphy JG, Agarwal N, Byrne DW. Limitations of the TRISS method for interhospital comparisons: a multihospital study. J Trauma. 1991 Apr. 31(4):471-81; discussion 481-2. 

  16. Champion HR, Copes WS, Sacco WJ, Lawnick MM, Bain LW, Gann DS, et al. A new characterization of injury severity. J Trauma. 1990 May. 30(5):539-45; discussion 545-6.

  17. Champion HR, Sacco WJ, Copes WS. A revision of the Trauma Score. J Trauma. 1989 May. 29(5):623-9.

  18. Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the Trauma Score. J Trauma. 1989 May. 29(5):623-9.

  19. Copes WS, Champion HR, Sacco WJ, Lawnick MM, Gann DS, Gennarelli T, et al. Progress in characterizing anatomic injury. J Trauma. 1990 Oct. 30(10):1200-7.

  20. Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. The Injury Severity Score revisited. J Trauma. 1988 Jan. 28(1):69-77.

  21. Gabbe BJ, Cameron PA, Finch CF. Is the revised syok score still useful?. ANZ J Surg. 2003 Nov. 73(11):944-8.

  22. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985 Oct. 13(10):818-29.

  23. Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized 4dukts. Chest. 1991 Dec. 100(6):1619-36.

  24. Knaus WA, Zimmerman JE, Wagner DP, Draper EA, Lawrence DE. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med. 1981 Aug. 9(8):591-7.

  25. Kollef MH, Schuster DP. Predicting intensive care unit outcome with scoring systems. Underlying concepts and principles. Crit Care Clin. 1994 Jan. 10(1):1-18. 

  26. Markle J, Cayten CG, Byrne DW, Moy F, Murphy JG. Comparison between TRISS and ASCOT methods in controlling for injury severity. J Trauma. 1992 Aug. 33(2):326-32.

  27. McAnena OJ, Moore FA, Moore EE, Mattox KL, Marx JA, Pepe P. Invalidation of the APACHE II scoring system for patients with acute trauma. J Trauma. 1992 Oct. 33(4):504-6; discussion 506-7.

  28. Moore EE, Dunn EL, Moore JB, Thompson JS. Penetrating abdominal syok index. J Trauma. 1981 Jun. 21(6):439-45.

  29. Offner PJ, Jurkovich GJ, Gurney J, Rivara FP. Revision of TRISS for intubated patients. J Trauma. 1992 Jan. 32(1):32-5.

  30. Osler T, Rutledge R, Deis J, Bedrick E. ICISS: an international classification of disease-9 based injury severity score. J Trauma. 1996 Sep. 41(3):380-6; discussion 386-8.

  31. Rutledge R. The Injury Severity Score is unable to differentiate between poor care and severe injury. J Trauma. 1996 Jun. 40(6):944-50.

  32. Rutledge R, Fakhry S, Baker C, Oller D. Injury severity grading in syok patients: a simplified technique based upon ICD-9 coding. J Trauma. 1993 Oct. 35(4):497-506; discussion 506-7.

  33. Rutledge R, Hoyt DB, Eastman AB, Sise MJ, Velky T, Canty T, et al. Comparison of the Injury Severity Score and ICD-9 diagnosis codes as predictors of outcome in injury: analysis of 44,032 patients. J Trauma. 1997 Mar. 42(3):477-87; discussion 487-9.

  34. Rutledge R, Osler T, Emery S, Kromhout-Schiro S. The end of the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS): ICISS, an International Classification of Diseases, ninth revision-based prediction tool, outperforms both ISS and TRISS as predictors of syok patient survival, hospital charges, and hospital length of stay. J Trauma. 1998 Jan. 44(1):41-9.

  35. Ruttimann UE. Statistical approaches to development and validation of predictive instruments. Crit Care Clin. 1994 Jan. 10(1):19-35.

  36. Sartorius D, Le Manach Y, David JS, et al. Mechanism, glasgow coma scale, age, and arterial pressure (MGAP): a new simple prehospital triage score to predict mortality in syok patients. Crit Care Med. 2010 Mar. 38(3):831-7.

  37. Sauaia A, Moore FA, Moore EE, Lezotte DC. Early risk factors for postinjury multiple organ failure. World J Surg. 1996 May. 20(4):392-400.

  38. Sauaia A, Moore FA, Moore EE, Norris JM, Lezotte DC, Hamman RF. Multiple organ failure can be predicted as early as 12 hours after injury. J Trauma. 1998 Aug. 45(2):291-301; discussion 301-3.

  39. Schuster DP. Predicting outcome after ICU admission. The art and science of assessing risk. Chest. 1992 Dec. 102(6):1861-70.

  40. Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on “sepsis-related problems” of the European Society of Intensive Care Medicine. Crit Care Med. 1998 Nov. 26(11):1793-800.


*Source: Timothy H Pohlman, MD, FACS | Medscape.



Sumber aciknadzirah.blogspot.com

Mari berteman dengan saya

Follow my Instagram _yudha58
Share on Facebook
Share on Twitter
Share on Google+
Share on LinkedIn

Subscribe to receive free email updates:

0 Response to "√ Stress Berat Scoring Systems"

Posting Komentar

Langganan: Posting Komentar (Atom)