|Year : 2019 | Volume
| Issue : 4 | Page : 1246-1251
Evaluation of Prince of Wales Hospital score in predicting massive blood transfusion in trauma
Moharam A Mohamed1, Mahmoud S El-Desoky1, Mohamed AE. Elhamzawy2
1 Department of General Surgery, Faculty of Medicine, Menoufia University, Menoufia, Egypt
2 Emergency Medicine Unit, Department of General Surgery, Faculty of Medicine, Menoufia University, Menoufia, Egypt
|Date of Submission||08-Oct-2018|
|Date of Decision||14-Nov-2018|
|Date of Acceptance||17-Nov-2018|
|Date of Web Publication||31-Dec-2019|
Mohamed AE. Elhamzawy
Manshyat Sultan, Menoufia 32511
Source of Support: None, Conflict of Interest: None
To evaluate the performance of Prince of Wales Hospital score (PWH score) in predicting the need for massive transfusion (MT) and comparing its performance with Assessment of Blood Consumption (ABC) score and the Trauma-associated Severe Hemorrhage (TASH) score.
Because of the great risk of massive bleeding that may be life-threatening especially if uncontrolled, early prediction of ongoing bleeding and the need for MT reduce mortality of polytraumatized patients. PWH score was developed for the purpose of early prediction of need of MT.
Patients and methods
This is a prospective comparative study done on 61 polytraumatized patients, with Injury Severity Score more than or equal to 16, who presented to the Emergency Department of Menoufia University Hospital during the period from September 2017 to September 2018. PWH score was applied to assess its performance and comparing it with ABC and TASH scores.
PWH, TASH, and ABC scores were applied on 61 patients with trauma meeting inclusion criteria to predict the need for MT. The area under the receiver operating characteristics curve was 0.92 for PWH score, 0.96 for TASH score, and 0.72 for ABC score. PWH was better in performance than ABC score, and TASH score was the most accurate and specific.
The results of this study support that TASH score has greater area under the (receiver operating characteristics) curve than PWH and ABC scores and is more accurate than both of them at cutoff point of more than or equal to 13.5. PWH performs better than ABC score at cutoff point of more than or equal to 4.5 for PWH and cutoff point of more than or equal to 1.5 for ABC score.
Keywords: bleeding, hemorrhagic shock, massive blood transfusion, polytrauma, scoring system
|How to cite this article:|
Mohamed MA, El-Desoky MS, Elhamzawy MA. Evaluation of Prince of Wales Hospital score in predicting massive blood transfusion in trauma. Menoufia Med J 2019;32:1246-51
|How to cite this URL:|
Mohamed MA, El-Desoky MS, Elhamzawy MA. Evaluation of Prince of Wales Hospital score in predicting massive blood transfusion in trauma. Menoufia Med J [serial online] 2019 [cited 2020 Jun 1];32:1246-51. Available from: http://www.mmj.eg.net/text.asp?2019/32/4/1246/274255
| Introduction|| |
It is a well-known fact that trauma is a worldwide problem and a main cause of morbidity and mortality universally. Trauma is the leading cause of mortality in the first four decades of life . Early death after trauma is attributed mostly to massive and ongoing bleeding. Overall, 10–25% of patients with major trauma present with acute traumatic coagulopathy that contributes to ongoing bleeding, and both are associated with early mortality . Hemorrhage is the commonest potentially preventable cause of early death after trauma .
Early definitive surgery to control bleeding and early recognition of patients at risk of massive transfusion (MT) help in correction of lethal triad that includes coagulopathy, acidosis, and hypothermia , which are associated with increased mortality .
During resuscitation, the use of fluid and large-volume packed red blood cell (RBC) transfusion aiming at restoration of circulating volume contributed also to dilutional coagulopathy, so hemostatic resuscitation is the current strategy for management of the combination of dilutional coagulopathy and acute traumatic coagulopathy ,.
For early prediction of the potential need for MT after trauma, numerous prediction models were developed and are gradually increasing over time. Since 2011, 19 models have been published. This may be triggered by the work of Guly et al. , which validated the Advanced Trauma Life Support.
Prince of Wales Hospital (PWH) Trauma registry in Hong Kong developed the PWH score, which contains seven variables measured to give a score (PWH score).
This study aims to assess the performance of PWH and compared it with Trauma-associated Severe Hemorrhage (TASH) score and Assessment of Blood Consumption (ABC) scores as tools for prediction of MT in polytraumatized patients.
| Patients and Methods|| |
This is a prospective comparative study. This study had been conducted at the Outpatient surgery Departments of Shebin El-Kom Teaching Hospital, after obtaining an approval from the Hospital Local Medical Ethics Committee.
A total of 61 consecutive seriously injured polytraumatized patients from September 2017 to September 2018 were studied.
Cases were chosen according inclusion and exclusion criteria.
Inclusion criteria for selected cases were both sexes, adult patients (18 years old or more), and patients with injury to several physical regions or organ systems where at least one injury or a combination of several injuries is life-threatening and needs ICU admission, with the severity of injury being equal or above 16 on the scale of the Injury Severity Score (ISS). The ISS describes injury severity purely on the basis of anatomical findings defined in the Abbreviated Injury Scale (AIS). The severity of each individual injury is graded on a scale from 1 to 6 points where 1 point describes minor injuries, and 6 points are given for untreatable, mostly lethal injuries. To calculate the ISS, each AIS score is assigned to one of six different body regions. The ISS is calculated as the sum of the squares of the highest AIS code in each of the three most severely affected body regions.
Exclusion criteria were patients transferred from other hospitals after performing any medical or surgical procedure, patients with minor trauma (ISS <16), death on arrival, known anemic patients, and patients with chronic renal failure or anemia [with hemoglobin (Hb) <7 g/dl], as Hb level drop will not only be attributed to acute blood loss from trauma and will affect results of PWH and TASH scores.
Data were collected in a pre-organized data sheet (Case Sheet) by the researcher from patients fulfilling the inclusion and exclusion criteria.
Each patient was subjected to ABCDE protocol, with each of the following being quickly evaluated and treated:
- Primary survey (ABCDE) protocol: airway and cervical spine control, breathing, circulation and hemorrhage control, disability, and exposure
- Secondary survey: allergy, medication, past illness/pregnancy, last meal and events/environment related to injury, and general and local examination
- Investigations including radiological: radiography (chest and pelvis), abdominal ultrasound, and other additional radiological investigations needed, and laboratory: complete blood count, arterial blood gas, urea, creatinine, albumin, bilirubin, serum sodium, and blood glucose level.
Calculation of PWH, TASH, and ABC scores for each patient was done.
The variables of PWH score were systolic blood pressure (SBP), Glasgow coma score, heart rate (HR), displaced pelvic fractures, positive focused assessment by sonography for trauma (FAST) or computed tomography scan, base deficit, and Hb.
The ABC score consists of four components that are available at bedside of acutely injured patient early in the assessment phase and include penetrating mechanism, SBP, HR, and a positive FAST.
The TASH scoring system uses seven independent variables to identify patients who will require an MT. These include blood pressure, sex, Hb, FAST, pulse, base excess, and extremity or pelvic fractures.
Data were collected and coded and then entered into a spreadsheet using Microsoft Excel for Windows office 2010 (Microsoft, Washington, USA). Data were statistically analyzed using statistical package of social science. Quantitative data were expressed as mean ± SD, whereas qualitative data were expressed as frequency and percentages. Qualitative variables were compared using a χ2 test, whereas quantitative continuous data were compared using the Mann–Whitney test. The area under the receiver operating characteristics (ROC) curve for each scale was used to compare the accuracy of the studied models. A P value less than 0.05 was considered statistically significant. The ability of the scores to predict an MT was estimated by the area under the ROC curve. The scores were evaluated for differences by comparing the area under the ROC curve between each by χ2 analysis. Cutoff points for each score were used to calculate sensitivity, specificity, and the percentage of patients correctly classified.
| Results|| |
The study was conducted on 61 patients, comprising 45 (73.9%) males and 16 (26.1%) females. Of them, 21 patients (34.4% of cases) received MT, comprising 19 males and only two females.
The age of the patients ranged from 19 to 75 years. The main mechanism of trauma was blunt trauma, represented by 56 (91.8%) patient. The main mode of trauma was road traffic accidents (RTAs), represented by 44 (72.1%), followed by falling from height, represented by 12 (19.7%), and violence, represented by five (8.2%) cases [Table 1].
|Table 1: Sociodemographic criteria and type of trauma among the studied cases|
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Clinical data, laboratory data, and investigations for cases receiving MT and cases not receiving MT are illustrated in [Table 2] and [Table 3].
|Table 2: Clinical data and laboratory investigations in relation to massive bleeding among the studied cases|
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|Table 3: Focused assessment by sonography for trauma, pelvic fracture, and femur fracture in relation to massive bleeding among the studied cases|
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The mean SBP was lower in cases receiving MT (78.43 ± 19.80).
The mean HR was higher in cases receiving MT (134.24 ± 10.73). The mean ISS (29.24 ± 8.08) was higher in cases receiving MT. Those three variables (SBP, HR, and ISS) showed significant relation in predicting MT. Relation between Glasgow coma scale (GCS) and prediction of MT was not significant. Mean Hb level was lower in cases receiving MT (7.12 ± 1.06). The mean of base deficit was −7.87 ± 1.62 in cases receiving MT. Both Hb level and base deficit showed significant relation in predicting MT. Unstable pelvic fracture was noted in eight (38.1%) cases receiving MT and showed significant relation in predicting MT.
The performance of PWH score and its comparison with ABC score and TASH score is illustrated in [Table 4]. The best cutoff points in this study were 4.5 for PWH score, 13.5 for TASH score, and 1.5 for ABC score.
|Table 4: Validity of Prince of Wales Hospital score, Trauma-associated Severe Hemorrhage score, and Assessment of Blood Consumption score for prediction of massive bleeding|
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Area under the curve (AUC) was greater in TASH score (0.96) than ABC score (0.72) and PWH (0.92).
TASH score was more specific (85%) than both ABC (80%) and PWH (82.5).
TASH was the more accurate (88.5%) than both ABC (75.4%) and PWH (86.9%) [Figure 1].
|Figure 1: ROC curve analysis for PWH score, TASH score, and ABC score for prediction of massive bleeding. ABC, Assessment of Blood Consumption; PWH, Prince of Wales Hospital; ROC, receiver operating characteristics; TASH, Trauma-associated Severe Hemorrhage.|
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| Discussion|| |
MT refers to the transfusion of a large volume of blood products over a short period of time to a patient who has severe or uncontrolled hemorrhage. MT is defined as transfusion of more than or equal to 10 RBC units, which approximates the total blood volume of an average adult patient, within 24 h; transfusion of four RBC units in 1 h with anticipation of continued need for blood product support; or replacement of 50% of the total blood volume by blood products within 3 h.
Although MT is not common, as it only affects a small subset of traumatized patients, mortality from severe bleeding occurs early and often (40%) in patients who need MT ,. Aggressive management of acute coagulopathy of trauma through activation of MT protocols with hemostatic resuscitation improves survival in patients with trauma ,. Our study included 61 cases with ISS more than 16. Of them, 21 received MT, and 40 did not. Incidence of MT was 34.4% which can be explained by high ISS used in selected patients. Cases that received MT experienced great percent loss of blood volume. All were class III hemorrhage (31–40% blood volume loss) and class IV hemorrhage (>40% blood volume loss). MT protocol is activated in patients with massive abdominal, thoracic, pelvic, or multiple long bone fractures with anticipated ongoing blood loss and hemodynamic instability.
Most cases were male (73.9%), whereas females represented 26.1%.
This can be explained, as trauma affects mainly the young population, as this group is the most active group in society, and also there is also a marked male preponderance in all communities of the world among polytrauma victims because of conditions in the workplace and for behavioral reasons. These results were near to the results of Mitra et al. , Brockamp et al. , and Subramanian et al. , who reported that most cases were males.
Regarding the mechanism of trauma, the results showed most cases with blunt trauma (91.8%), and the remaining were penetrating with multiple injuries (8.2%). No statistically significant relation between mechanism of trauma and predicting MT in polytrauma cases. This result agrees with Nunez et al.  and Maegele et al. , who reported no statistically significant relation between mechanism of trauma and predicting MT in polytrauma cases.
RTA was responsible for most of the cases. Approximately 72.1% of cases were owing to RTA. Falling from height represented ~19.7%. The remaining 8.2% were owing to violence. This is explained as most studies done on the epidemiology of trauma found that most cases are due to RTAs and the most commonly affected are road users such as pedestrians, passengers, and motorcyclists as well as drivers, who are involved in most of the deaths and disabilities owing to lack of safety measures in our roads and ignorance of safety instruction. These results confirmed the report published by WHO which stated that ~90% of the world's fatalities occur on the roads in low-income and middle-income countries.
In the present study, cases receiving MT experienced significant physiologic changes such as higher HR, lower SBP, base deficit, and Hb level before transfusion. These changes have significant relation with prediction of MT.
The present study showed that the mean SBP was 78.43 ± 19.80 among cases that received MT and was statistically significant in predicting MT in polytrauma cases. The present study also showed that the mean HR was 134.24 ± 10.73 in cases receiving MT and was statistically significant in predicting MT in polytrauma cases. This result agrees with results of Cheng-Shyuan et al. , David et al. , Nunez et al. , and Schroll et al. , who reported that there was a statistically significant association between SBP and HR and predicting MT in polytrauma cases.
Regarding GCS, the mean of GCS in patients receiving MT was 11.19 ± 3.30 and a mean of 9.33 ± 4.12 in patients not receiving MT. GCS is not statistically significant in predicting MT in polytrauma cases. The results conducted by Nunez et al.  was different from this study and showed significant relation between GCS and predicting MT.
This can be explained by the characteristics of the cases included in our study as the mean of GCS in cases receiving MT was near to that of cases not receiving MT, so the net result showed nonsignificant association between GCS and MT.
In this study, the mean of Hb level in patients receiving MT was 7.12 ± 1.06 and was statistically significant in predicting MT in polytrauma. This goes in accordance with the results of Poon et al.  and Maegele et al. . Both of them showed significant association between Hb level and predicting MT. In this study, the mean of base deficit in cases with MT was −7.87 ± 1.62. Significant relation between base deficit and prediction of MT was found.
This agreed with the results of Poon et al.  and results of Cheng-Shyuan et al. , as both showed significant relation between base deficit and predicting MT. In this study, no significant relation between positive FAST and predicting MT. This goes in accordance with Schroll et al. . Another study by Poon et al.  showed different result and showed significant relation between positive FAST and predicting MT. This can be explained by different amounts of blood collections detected by FAST. In this study, there were cases that have minimal collection in FAST examination and not received MT.
Unstable pelvic fracture was noted in eight (38.1%) cases receiving MT and noted in ~2 (5.0%) cases not receiving MT. In this study, there was a significant relation between unstable pelvic fracture and predicting MT. This agrees with the results of Poon et al. , who found a significant relation between unstable pelvic fracture and predicting MT. In this study, there was no significant relation between open or dislocated femur fracture and predicting MT. This disagrees with the results published by Poon et al. , who found a significant relation between open or dislocated femur fracture and predicting MT.
The cornerstone of this study is to evaluate the performance of PWH in predicting MT and comparing it with TASH and ABC scores. All scores were significant in predicting MT. For PWH score, the best cutoff point was 4.5 with a sensitivity of 100% and specificity of 80% in predicting MT. AUC for PWH score was 0.92. A study by Mitra et al.  showed different cutoff point for PWH more than or equal to 6 in which sensitivity was 36.9% and specificity was 97% in predicting MT. The AUC was 0.84. Poon et al.  used different cutoff point for PWH more than or equal to 6, with sensitivity of 33% and specificity of 98% in predicting MT. The AUC was 0.886. A study by Brockamp et al.  used different cutoff point for PWH (≥2.5), in which PWH score's sensitivity was 80.6% and specificity was 77.7% in predicting MT. The AUC was 0.86.
In this study, the best cutoff point of TASH score was 13.5 with a sensitivity of 95.2% and specificity of 85% in predicting MT. The AUC was 0.96. A study by Mitra et al.  showed different cutoff point for TASH more than or equal to 18 in which sensitivity was 25.13% and specificity was 99% in predicting MT. The AUC was 0.89. Poon et al.  used different cutoff point for TASH at more than or equal to 16, in which the sensitivity was 25% and specificity was 99% in predicting MT. The AUC was 0.911. Brockamp et al.  used different cutoff point for TASH at more than or equal to 8.5 in which sensitivity was 84.4% and specificity was 78.4% in predicting MT. The AUC was 0.88.
In this study, the best cutoff point of ABC score was 1.5 with a sensitivity of 61.9% and specificity of 82.5% in predicting MT. The AUC was 0.72. A study by Mitra et al.  showed a different cutoff point for ABC more than or equal to 2 in which sensitivity was 45.6% and specificity was 94% in predicting MT. The AUC was 0.72. Poon et al.  used different cutoff point for ABC more than or equal to 2 in which sensitivity was 33% and specificity was 96% in predicting MT. The AUC was 0.809. Brockamp et al.  used different cutoff point for ABC (2.5). The sensitivity was 76% and specificity was 70% in predicting MT. The AUC was 0.76. The diagnostic accuracy of TASH score (88.5%) was higher than that of ABC score (75.4%) and PWH score (86.9%) in predicting MT.
PWH score was more sensitive, specific, and accurate than ABC score.
| Conclusion|| |
The results of this study lends support that TASH score has greater AUC than PWH and ABC scores and more accurate than both of them at cutoff point of more than or equal to 13.5. PWH performs better than ABC score at cutoff point of more than or equal to 4.5 for PWH and cutoff point of more than or equal to 1.5 for ABC score.
The study was conducted in a single center and with a small sample size.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]