Risk stratification in trauma and haemorrhagic shock: Scoring systems derived from the TraumaRegister DGU®

Injury, Int. J. Care Injured 45S (2014) S29–S34

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Risk stratification in trauma and haemorrhagic shock: Scoring systems derived from the TraumaRegister DGU1§ Sebastian Wutzler a,*, Marc Maegele b,c, Arasch Wafaisade b,c, Hendrik Wyen a, Ingo Marzi a, Rolf Lefering b the TraumaRegister DGU1 a

Department of Trauma, Hand and Reconstructive Surgery, Hospital of the Johann Wolfgang Goethe-University, Frankfurt, Germany Institute for Research in Operative Medicine, Cologne Merheim Medical Center (CMMC), University of Witten/Herdecke, Cologne, Germany c Department of Orthopedics, Trauma and Sports Medicine, Cologne Merheim Medical Center (CMMC), University of Witten/Herdecke, Cologne, Germany b

A R T I C L E I N F O

A B S T R A C T

Keywords: Scoring system Polytrauma Mortality Outcome

Scoring systems commonly attempt to reduce complex clinical situations into one-dimensional values by objectively valuing and combining a variety of clinical aspects. The aim is to allow for a comparison of selected patients or cohorts. To appreciate the true value of scoring systems in patients with multiple injuries it is necessary to understand the different purposes of quantifying the severity of specific injuries and overall trauma load, being: (1) clinical decision making; (2) triage; (3) planning of trauma systems and resources; (4) epidemiological and clinical research; (5) evaluation of outcome and trauma systems, including quality assessment; and (6) estimation of costs and allocation of resources. For the first two, easy-to estimate scores with immediate availability are necessary, mainly based on initial physiology. More sophisticated scores considering age, gender, injury pattern/severity and more are usually used for research and outcome evaluation, once the diagnostic and therapeutic process has been completed. For score development large numbers of data are necessary and thus, it appears as a logical consequence that large registries as the TraumaRegister DGU1 of the German Trauma Society (TR-DGU) are used to derive and validate clinical scoring systems. A variety of scoring systems have been derived from this registry, the majority of them with focus on hospital mortality. The most important among these systems is probably the RISC score, which is currently used for quality assessment and outcome adjustment in the annual audit reports. This report summarizes the various scoring systems derived from the TraumaRegister DGU1 over the recent years. ß 2014 Elsevier Ltd. All rights reserved.

Introduction ‘‘If you have never felt the need for any type of severity scoring system, then you probably have never had to explain how it is that survival rate of 85% in your trauma center is actually better than the survival rate of 97% in some other hospital where the patients are much less seriously injured’’ (S. Baker, J Trauma 1983) [28].

§ The Trauma Register of the German Trauma Society (Deutsche Gesellschaft fu¨r Unfallchirurgie) was partly funded in the past by the Deutsche Forschungsgemeinschaft Ne 385/5 and by a grant from Novo Nordisk A/S, Bagsvaerd, Denmark. It is actually funded by fees from the participating trauma centres (a list of participating trauma centres is available at www.traumaregister.de). * Corresponding author at: Department of Trauma, Hand and Reconstructive Surgery, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60596 Frankfurt, Germany. Tel.: +49 069 6301 83304; fax: +49 069 6301 6439. E-mail addresses: [email protected], [email protected] (S. Wutzler). 1 Committee on Emergency Medicine, Intensive Care and Trauma Management (Sektion NIS) of the German Trauma Society (DGU).

http://dx.doi.org/10.1016/j.injury.2014.08.014 0020–1383/ß 2014 Elsevier Ltd. All rights reserved.

Probably quoted over a million times, this statement perfectly characterizes the basic idea behind scoring systems. In this regard many attempts have been made to further specify the entity ‘‘Trauma patient’’, e.g. by the well known Abbreviated Injury Scale (AIS) [18] and the Injury Severity Score (ISS) derived from this injury classification [2]. To appreciate the true value of scoring systems in patients with multiple injuries, we have to understand the different purposes of quantifying the severity of specific injuries and overall trauma load: (1) clinical decision making; (2) triage; (3) planning of trauma systems and resources; (4) epidemiological and clinical research; (5) evaluation of outcome and trauma systems, including quality assessment; and (6) estimation of costs [13,28]. For the first two, easy-to estimate scores with immediate availability are necessary, mainly based on the initial physiologic response. More sophisticated scores considering age, gender, injury pattern/ severity and more are usually used for research and outcome evaluation, once the diagnostic and therapeutic process has been completed.

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S. Wutzler et al. / Injury, Int. J. Care Injured 45S (2014) S29–S34

Fig. 1. In-hospital trauma mortality as a function of the Injury Severity Score (ISS). Results from 30,866 selected TR-DGU cases (selection criteria: years 2010–2011, ISS  4, age available, only Europe, transferred patients excluded). Mortality does not show linear increase but peaks at 16 and 25 points due to isolated severe AIS 4 and AIS 5 injuries.

All scores have one goal in common: they attempt to reduce a complex clinical situation into a one-dimensional value by objectively valuing and combining different clinical aspects. The aim is to allow for a comparison of selected patients or cohorts. Most scoring systems have limitations and a common weakness is the ability to predict outcome or risk at the individual patient level. Even highly accurate scores are derived from statistical analyses and provide only probabilities. The prediction in the individual case remains uncertain. To avoid misinterpretations physicians should know about these limitations. As shown in Fig. 1 the correlation of scores with their dependent variable can also vary within certain scoring ranges. To develop valid and reliable scores large amounts of data are necessary, besides clinical experts. It seems to be a logical consequence that large registries like the TraumaRegister DGU1 of the German Trauma Society (TR-DGU) are used to derive clinical scoring systems. While the data set documented for each individual trauma patient has undergone several minor adjustments since the start of TR-DGU in 1993, the main basic parameters have remained consistent over time. As early as in 1998 the first reports on scoring and outcome prediction based on the TR-DGU have been published [22]. To develop trauma scores the first step is usually to select potential predictor variables for a dependent endpoint like mortality or complications. The potential statistical association is tested in univariate analyses and multivariate models afterwards, the results for the different predictors are often expressed by odds ratios (OR). Continuous

variables can also be grouped to categorical variables. Based on the model coefficients simplified point scores can be derived and the resulting score points or ranges then correspond to the risk that the respective outcome occurs. This method was used, e.g. in the Trauma Associated Severe Hemorrhage (TASH) Score [16,35], or Lung Organ Failure Score (LOFS) [34]. Moreover, logistic functions can transform point scores into a probability for individual patients, e.g. by the Revised Injury Severity Classification (RISC) score [12]. Newly developed scores should be validated. This can be done by either splitting the available data into a development sample and a validation sample, or the score is validated in newly collected data. To evaluate and compare the discriminatory power of different scores receiver operating characteristic (ROC) curves are often used. As a summary measure for the ability to discriminate, e.g. survivors from non-survivors, the area under the ROC curve (AUC) should be as close as possible to the value of 1.00, which represents a perfect separation. The greater the area under the ROC curve, the better the score. AUCs should be presented with 95% confidence intervals. However, AUCs from different datasets are not directly comparable. If a dataset contains a lot of easy to predict cases, the AUC tends to substantially higher. Scores In patients with severe life-threatening injuries mortality naturally is the most important endpoint. Thus, most scores derived from the TR-DGU focus on hospital mortality, as

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Table 1 Summary of scores derived from the TR-DGU. Score

Author

Year

Dependent variable

Main prognostic variables

Specific characteristics

LOFS (Lung Organ Failure Score)

Wutzler

2012

Pulmonary organ failure

Simplified point score

Pediatric Trauma BIG Score

Borgman

2011

Mortality

Age, ISS, volume, AISHead, AISThorax, male gender, emergency surgery, surgical interventions Base deficit, INR, GCS

TASH (Trauma Associated Severe Hemorrhage) Score

Maegele Yu¨cel

2011 2006

Mass transfusion  10 PRBC

Sequential Trauma Score (STS)

Huber-Wagner

2010

Mortality

Revised Injury Severity Classification (RISC)

Lefering

2009

Mortality

Emergency Trauma Score (EMTRAS)

Raum

2009

Mortality

‘‘RIXEN Score’’

Rixen

2001 1998

Mortality

documentation currently ends with discharge from the acute care hospitals. An alternative would be 30 days mortality, which would, however, require a follow-up for patients discharged earlier. Other fields of interest are pulmonary organ failure or mass transfusion. Table 1 summarizes scores based on the data of the TR-DGU. Each score is briefly explained below. Revised Injury Severity Classification (RISC) In the early years after foundation in 1993 the TR-DGU used the Trauma and Injury Severity Score (TRISS) [7] to compare and adjust outcomes across hospitals. However, the use of the TRISS had been critically discussed [8]. The TRISS is based on results from mostly American hospitals in the late 1980s and the German trauma care in part differs substantially from the American ones [25,33]. While the TRISS has been criticized for not adequately considering age or head injury, own investigations of the TR-DGU showed that Table 2 Variables of the Revised Injury Severity Classification (RISC) score. Categories not listed receive a coefficient of zero. Variable

Value

Coefficient

Age (yrs.)

55–64 65–74 75 Score (1–75) 4 5/6 5 3–5 40–49 50–79 80 9.0 to 19.9 20 Yes 1 2 3

1.0 2.0 2.3 0.03 0.5 1.8 1.0 0.9 0.8 1.0 1.2 0.8 2.7 2.5 0.4 0.8 1.6 5.0

New Injury Severity Score [NISS] (pts.) AISHEAD (pts.) AISEXTREMITIES (pts.) Glasgow Coma Scale [GCS] (pts.) Partial thromboplastin time [PTT] (s; ED)

Base excess (mmol/l) Cardiac arrest Number of indirect signs of bleeding*

Constant

* Systolic BP < 90 mmHg; haemoglobin < 9 mg/dl (ED); mass transfusion > 9 units of PRBC (ED). ED, emergency department; BP, blood pressure; PRBC, packed red blood cells; AIS, Abbreviated Injury Scale. Laboratory values refer to the first measurement after admission.

Hb, base excess, SBP, HR, free intraabdominal fluid, instable pelvic fracture, open/dislocated femur fracture, male gender Various (dependent on sequence) Age, NISS, head injury, severe pelvic injury, GCS, partial thromboplastin time, base excess, cardiac arrest, bleeding signs (Hb, MT, SBP) Age, prehospital GCS, Base excess, prothrombin time Age, GCS, ISS, base excess, prothrombin time as continuous variables

External validation by the TRDGU Revalidation and update of original score in 2011

Sequential score for different phases during early trauma care Highest discrimination ability of all established scoring systems

Simplified point score First score based on the TR-DGU

prognostic factors like coagulation parameters or base deficit could improve outcome estimation [20,23]. Thus, in 2001 it was decided to develop a new prognostic system with additional parameters to improve outcome prediction. In a two-step analysis based on 2008 primarily admitted cases with complete data for all selected variables the Revised Injury Severity Classification (RISC) score was derived [12]. All cases were required to have a valid TRISS score for comparison. The group was split into a development and validation sample. The final variables with corresponding score points are presented in Table 2. For each individual the point weights of each variable are subtracted from the constant 5.0 resulting in a final score X. This score X could then be transformed into a probability of survival P(s) by the logistic function: PðsÞ ¼

1 eX ¼ 1 þ eX 1 þ eX

Especially the further specification of age in four categories, the more detailed injury description (NISS, unstable pelvic fracture, head injury) and the reflection of coagulation disorders by initial laboratory parameters (base excess and PTT) improved the discrimination ability compared to the TRISS. The area under the ROC curve for RISC is 0.907 and superior to those of other established prognostic score systems like ISS, TRISS or Revised Trauma Score (RTS) (all areas under the ROC curve <0.90). The RISC score allows for reliable and valid comparison of outcome data from severely injured patients from different hospitals, even if the populations are not comparable regarding injury severity. It has been used for outcome adjustment in the annual audit reports for the participating centres of the TR-DGU since 2004. In the years after its introduction, predicted and observed mortality in the TR-DGU fitted quite well, with a deviation of less than 1% [12]. However, result of the most recent years showed that mortality rates improved and remained significantly below the estimated level (see annual audit reports available at www.traumaregister.de). In conclusion, hospital mortality today is lower than in the 1990s in Germany. For this reason, an update of the initial RISC score is required. The revised RISC II score has already been developed with data from >30,000 cases documented in the years 2010–2011 and validated with data from 2012. It will soon be published.

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S32 Table 3 Trauma Associated Severe Hemorrhage (TASH) Score. Variable

Value

Points

Score

Probability for mass transfusion (MT) TASH (Pts.)

Haemoglobin (g/dl)

Base excess (mmol)

Systolic blood pressure (mmHg)

Heart rate (bpm) Free intraabdominal fluid Clinically instable pelvic fracture Open or dislocated femur fracture Male gender TASH: (sum of points)

Probability

<7 <9 <10 <11 <12

8 6 4 3 2

1–8 9 10 11

<5% 6% 8% 11%

<10 <6 <2

4 3 1

12 13 14

14% 18% 23%

<100

4

15

29%

<120

1

16

35%

>120

2 3 6

17 18 19

43% 50% 57%

3

20

65%

1

21

71%

22 23 24

77% 82% >85%

Trauma Associated Severe Hemorrhage (TASH) Score Although trauma care has substantially improved over the last decades, uncontrolled haemorrhage accounts for over 50% of all trauma-related deaths within the first 48 h after admission [26]. Therefore, early coagulopathy is strongly associated with outcome and trauma research has increasingly focused on this topic [5,6,9,17]. The rapid identification of patients with acute posttraumatic coagulopathy is challenging. However, in cases requiring massive transfusion (MT) due to ongoing bleeding an adequate and aggressive management with balanced use of blood components can correct coagulopathy, control bleeding and improve outcome [4,15]. The TASH score was developed to recognize patients with substantial bleeding problems and potential need for massive transfusion at a very early stage of trauma care. The initial score was derived from the 1993–2003 TR-DGU database and has been updated using the 2004–2007 data [16,35]. It consists of 8 independent variables as shown in Table 3. A simplified point score can directly be transferred into a probability for MT defined as the need of at least 10 units of packed red blood cells (PRBC) until ICU admission. All variables should be available within the first 15 min upon arrival to the ED. Discrimination of the revised TASH score is excellent as expressed by an ROC/AUC of 0.905. The revised version did not change the point values but only the probability for MT derived from this score. This reflects the decreasing rate of patients requiring MT in the ED, probably as a consequence of better treatment strategies for coagulopathy. ‘‘RIXEN Score’’ A first brief report about this score was published by Rixen et al. in 1998 [22], followed by a more detailed analysis in 2001 [24]. It was the first attempt to determine the initial probability of death in multiple trauma patients by the TR-DGU database. From the first 5 years (1993–1997) of documentation 2069 cases from 20 hospitals were used to develop a multivariate probability model. From the more than 30 variables documented in the initial phase of trauma

care at that time age, GCS, ISS, base excess and prothrombin time contributed significantly to the prognostic model. It was a characteristic of this score that it did not reduce the variables into categories but used the continuous values for prediction. This procedure is optimal in case of a linear association (like for GCS, ISS) but has limitations if such an association is weak (e.g. for age). However, it was the first score to include early laboratory values after admission. The AUC of ROC for the ‘‘Rixen Score’’ in the development sample was 0.903. When applied to the 1998 dataset as validation sample, the ROC/AUC was 0.901 and the good ability of this score to discriminate could be confirmed. Pediatric Trauma BIG Score Obviously, paediatric trauma patients differ from adults with respect to physiological response, clinical course and outcome. Many studies, among those some of the above-mentioned publications, identified age as one of the main prognostic factors in trauma patients. Thus, the development of a specific mortality prediction score for children with traumatic injuries was goal of an analysis using the Joint Theater Trauma Registry (US Army Institute of Surgical Research, San Antonia, Texas, USA) data from 2002 to 2009. The final score was validated on a data set of paediatric patients (18 years) from the TR-DGU 2002–2007. As this score was made for children, age was not among the predictor variables. It could be validated that admission base deficit, international normalized ratio and GCS were independently associated with mortality in paediatric trauma patients. The final score had a similar ROC/AUC of 0.89 in the derivation and validation data set and was accurate in both penetrating-injury and blunt injury populations [3]. Lung Organ Failure Score (LOFS) Altered post-traumatic inflammation with subsequent organ failure is common in multiple trauma patients. Especially blunt chest trauma is known to be associated with high rates of respiratory complications. More than 50% of all trauma patients with an ISS  16 points suffer from severe thoracic trauma [1]. Since the clinical course can be positively influenced by means such as kinetic therapy or prophylactic PEEP ventilation the identification of high-risk patients would be wishful. The determination of factors predisposing these patients for pulmonary organ failure lasting 3 days was the goal of the LOFS study. A multivariate analysis of some 6000 patients was transformed into a simplified score with 10 independent variables, among them as strongest predictors the number and timing of surgical interventions and the severity of the thoracic injury itself. Point of criticism is the lack of a validation data set. However, the LOFS could be helpful for efficacy analyses of therapeutic means such as rotational bed therapy in selected subgroups with comparable risks for respiratory complications [34]. Emergency Trauma Score (EMTRAS) The basic idea of the EMTRAS was the development of a scoring system for the emergency room (ER) that is based on a few early available clinical parameters. Since the complete diagnosis of all relevant injuries can take up to hours after admission, scores including anatomical injuries, or the ISS, are not available at an early stage of trauma care. The EMTRAS was derived from 11,533 cases documented from 1993 to 2003 and validated on 3314 patients from 2004 to 2005. The EMTRAS includes four variables: age, GCS, bases excess and prothrombin time, each one grouped in subcategories with scores of 0–3 points. Therefore, the lowest/best EMTRAS is 0 points and the highest/worst is 12 points. Score values

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above 8 points were associated with a mortality of over 80%. The ROC/AUC for the EMTRAS was 0.828 [21]. This is considerably lower than the other scores, which is based on the missing component of anatomical injury severity. It could serve as a classical triage score, which is available shortly after admission to hospital. Sequential Trauma Score (STS) The STS was developed to determine the prognosis of multiple trauma patients at different early stages of trauma care. The primary aim was a dynamic score, which demonstrates the increasing precision of prediction from the prehospital setting until ICU admission based on the additional information becoming available over time, e.g. laboratory parameters or exact knowledge of the anatomical injury pattern and severity. Using the 2002–2006 data sets, the authors identified the most relevant prognostic variables from the patients’ basic data (P), prehospital phase (A), early (B1) and late (B2) trauma room phase. While the ROC/AUC for a prognostic model based on P data only was 0.63, this value could stepwise be increased to 0.90 (ROC=AUCPþAþB1 þB2 ) for the final model at the end of the emergency room phase. The relative impact of each segment on mortality was P = 25%, A = 7%, B1 = 17% and B2 = 51%. Among the most important variables were signs of massive bleeding, head injury (GCS/AIS/anisocoria), coagulation parameters, and maximum AIS [11]. Discussion Several scoring systems have been derived from the TR-DGU, the majority focusing on hospital mortality. The most important among them is probably the RISC score, which is currently used for quality assessment and outcome adjustment in the annual audit reports. However, although the individual weights of each scoring parameter slightly vary between the different scores, the main important prognostic variables are quite similar. These key parameters include age, injury severity (and here specifically head injury), clinical and laboratory sights of acute haemorrhage, and vital signs. With these variables ROC/AUCs of 80–90% can be achieved. This seems to be the highest discrimination ability that is achievable with the parameters that are commonly documented in trauma registries. Of course the retrospective development of scoring systems using registry data is limited to those parameters that were once decided to be included in the data set. These parameters are regularly updated in meetings of the TR-DGU steering committee. Some variables such as concomitant diseases or use of kinetic therapy were quite imprecise or poorly documented. Their scientific and clinical value was also doubtful and a selection of parameters therefore has been deleted throughout the years. However, the current data sheet seems to compromise all relevant parameters of trauma care. Obviously none of the scores has yet been implemented into everyday clinical practice and emergency room decision-making. Even the TASH score and the LOFS, originally developed for this purpose, seem to be too complicated and require too much information at an early stage. However, theoretically it would be possible to calculate the risks for MT or lung organ failure once the secondary survey is completed. The treatment of multiple trauma patients with chest trauma is far from being standardized and the benefit of means such as kinetic therapy has been questioned [34]. Therefore it is not surprising that the LOFS is of little clinical relevance at this time point. The score could be used for observational studies of complication rates depending on the employed treatment to select cohorts with comparable initial risks of lung organ failure. This

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statement is further supported by the fact that Watkins et al. performed a related study to develop a score for the risk of Acute Respiratory Distress Syndrome (ARDS) after trauma [32]. Some of the final variables such as age, ISS, and the presence of lung injuries were quite similar to the LOFS. The scientific debate on the best treatment of trauma patients with ongoing severe haemorrhage includes a variety of options, e.g. fixed transfusion ratios or point of care diagnostics [27]. Considerable changes in the management of severely injured patients over the years could be observed, among them the early substitution of PRBC, Fresh Frozen Plasma (FFP), and others [29– 31]. These changes in transfusion practice were associated with better outcome [14]. Besides being extremely helpful in clinical research, the early identification of patients at high risk for MT by a scoring system could lead to direct consequences in clinical practice. Moreover, it has recently been published that calculation of TASH takes less than 8 min after admission [19]. But still the clinical usefulness of the TASH score has not yet been proved. The most obvious value of each the TASH score and LOFS at this point appears to be in medical research, e.g. stratifying patients in clinical trials and quantifying outcome. As trauma care has improved over the past years scoring systems can be used to retrospectively identify possible reasons for reduced mortality. The RISC score and TRISS were used to compare predicted and observed mortality in one of the major studies on whole body computed tomography (WBCT). In this registry analysis Huber-Wagner et al. showed a significant and clinically relevant survival benefit for multiple trauma patients who underwent WBCT diagnostic during the early resuscitation period. For patients receiving WBCT Standardized Mortality Ratios (SMRs) were 0.745 according to the TRISS and 0.865 according to the RISC score, respectively, while the SMRs for the non-WBCT group were close to one [10]. The more accurate the score, the better the quantification of the survival benefit. In conclusion the authors recommended the use of WBCT in clinical routine when treating patients with suspicion of multiple trauma. See also the paper on WBCT in the present issue of this journal by Huber-Wagner et al. Trauma surgeons are in clear need of reliable scoring systems for quality assessment in trauma care and identification of reasons for improvement. The development of scores often takes years and many have to be updated as soon as they have been implemented in outcome evaluation and medical research. Since 2005 the SMRs in the annual reports of the TR-DGU based on the RISC score have consistently been below 1, indicating that the individual weights of the parameters may have changed since the 1990s [1]. From 2014 on the new and improved RISC II score will be used. It contains 13 partly new variables, e.g. penetrating mechanism of trauma, or pre-existing diseases. Pupil size and reactivity will be included as a much more reliable tool for quantification of head injury severity. The score can also be calculated with missing values except for age and injury pattern. With increased completeness of variables the precision of the prognosis improves. For 2012 (n = 24,473) RISC II prognosis for mortality was 9.7% and observed mortality was 9.9% [1] which prompts us to speculate that this scoring system will have excellent discrimination ability. Conflict of interest There are no conflicts of interest. References [1] Annual Report 2013 TraumaRegister DGU1. www.traumaregister.de. [2] Baker SP, O’Neill B, Haddon Jr W, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma 1974;14187–96.

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