Prehospital identification of trauma patients requiring transfusion: results of a retrospective study evaluating the use of the trauma induced coagulopathy clinical score (TICCS) in 33,385 patients from the TraumaRegister DGU®

The predictive value of four traumatic hemorrhage scores for early massive blood transfusion in trauma patients in the pre-hospital setting

OBJECTIVES

We aimed to explore the predictive value of four traumatic hemorrhage scores for early massive blood transfusion in trauma patients in the pre-hospital setting.

METHODS

Trauma patients admitted to Shenzhen University General Hospital from July 2018 to December 2022 were included in this study. They were divided into the massive transfusion group and the non-massive transfusion group according to the blood transfusion volume within 24 h. Basic information about patients was collected. Glasgow Coma Scale (GCS), focused assessment with sonography for trauma (FAST), and injury severity score (ISS) were performed. The receiving operating characteristic (ROC) curve was used to compare the predictive value of four trauma transfusion scores for early massive blood transfusion in the pre-hospital setting.

RESULTS

A total of 475 patients were enrolled, 43 received massive blood transfusions and 29 died within 24 h. The sensitivity and specificity of the four trauma hemorrhage scores in predicting the need for massive blood transfusions in trauma patients at their recommended cutoff points were all high. Among the four scores, the area under the ROC curve was larger for the assessment of blood consumption (ABC) score (0.864) and smaller for the trauma-induced coagulopathy clinical score (TICCS) score (0.795, p > 0.05).

CONCLUSIONS

All four pre-hospital trauma hemorrhage scores have a high predictive value in assessing massive blood transfusion in trauma patients.

Predictors of transfusion in trauma and their utility in the prehospital environment: a scoping review

Background: Hemorrhage is a leading cause of preventable mortality from trauma, necessitating resuscitation through blood product transfusions. Early and accurate identification of patients requiring transfusions in the prehospital setting may reduce delays in time to transfusion upon arrival to hospital, reducing mortality. The purpose of this study is to characterize existing literature on predictors of transfusion and analyze their utility in the prehospital context.Objectives: The objectives of this study are to characterize the existing quantity and quality of literature regarding predictor scores for transfusion in injured patients, and to analyse the utility of predictor scores for massive transfusions in the prehospital setting and identify prehospital predictor scores for future research.Methods: A search strategy was developed in consultation with information specialists. A literature search of OVID MEDLINE from 1946 to present was conducted for primary studies evaluating the predictive ability of scoring systems or single variables in predicting transfusion in all trauma settings.Results: Of the 5824 studies were identified, 5784 studies underwent title and abstract screening, 94 studies underwent full text review, and 72 studies were included in the final review. We identified 16 single variables and 52 scoring systems for predicting transfusion. Amongst single predictor variables, fluids administered and systolic blood pressure had the highest reported sensitivity (100%) and specificity (89%) for massive transfusion protocol (MTP) activation respectively. Amongst scoring systems for transfusion, the Shock Index and Modified Shock Index had the highest reported sensitivity (96%), while the Pre-arrival Model had the highest reported specificity (95%) for MTP activation. Overall, 20 scores were identified as being applicable to the prehospital setting, 25 scores were identified as being potentially applicable, and seven scores were identified as being not applicable.Conclusions: We identified an extensive list of predictive single variables, validated scoring systems, and derived models for massive transfusion, presented their properties, and identified those with potential utility in the prehospital setting. By further validating applicable scoring tools in the prehospital setting, we may begin to administer more timely transfusions in the trauma population.

A prehospital scoring system for predicting the need for emergent blood product transfusion

BACKGROUND

Several tools have been proven to predict the need for massive transfusion in trauma casualties, yet tools that are easily applicable in the prehospital setting for predicting the need for any blood product transfusion in the emergency department (ED) are lacking.

METHODS

A retrospective analysis of the cross-referenced Israeli Defense Forces Trauma Registry and the Israeli National Trauma Registry databases was performed to identify predictors for any blood product transfusion in the ED. A scoring system was developed after internally validating the prediction model. Division to risk groups was performed.

RESULTS

Seven variables (systolic blood pressure, heart rate, arterial oxygen saturation, trunk involvement, mechanism of injury, chest decompression, and tourniquet application) were included in the scoring system, ranging from 0 to 11.5. Risk groups for ED transfusion included very low (0.8%), low (3.2%), intermediate (8.5%), and high (31.2%) risk.

CONCLUSION

A scoring system for predicting the need for any blood product transfusion in the ED was developed, based on information readily available in the early stages of prehospital resuscitation, allowing the receiving medical facility to prepare for that need.

What's new in trauma 2020

Throughout the past 2020, the pandemic COVID-19 has caused a big global shock, meanwhile it brought a great impact on the public health network. Trauma emergency system faced a giant challenge and how to manage trauma under the pandemic of COVID-19 was widely discussed. However, the trauma treatment of special population (geriatric patients and patients taking anticoagulant drugs) has received inadequate attention. Due to the high mortality following severe traumatic hemorrhage, hemostasis and trauma-induced coagulopathy are the important concerns in trauma treatment. Sepsis is another topic should not be ignored when we talking about trauma. COVID-19 itself is a special kind of sepsis, and it may even be called as serious systemic infection syndrome. Sepsis has been become a serious problem waiting to be solved urgently no matter in the fields of trauma, or in intensive care and infection, etc. This article reviewed the research progress in areas including trauma emergency care, trauma bleeding and coagulation, geriatric trauma and basic research of trauma within 2020.

Tools to predict acute traumatic coagulopathy in the pre-hospital setting: a review of the literature

Introduction:

Recognising acute traumatic coagulopathy (ATC) poses a significant challenge to improving survival in emergency care. Paramedics are in a prime position to identify ATC in pre-hospital major trauma and initiate appropriate coagulopathy management.

Method:

A database literature review was conducted using Scopus, CINAHL and MEDLINE.

Results:

Two themes were identified from four studies: prediction tools, and point-of-care testing. Prediction tools identified key common ATC markers in the pre-hospital setting, including: systolic blood pressure, reduced Glasgow Coma Score and trauma to the chest, abdomen and pelvis. Point-of-care testing was found to have limited value.

Conclusion:

Future research needs to explore paramedics using prediction tools in identifying ATC, which could alert hospitals to prepare for blood products for damage control resuscitation.

mTICCS and its inter-rater reliability to predict the need for massive transfusion in severely injured patients

Purpose

The modified Trauma-Induced Coagulopathy Clinical Score (mTICCS) presents a new scoring system for the early detection of the need for a massive transfusion (MT). This easily applicable score was validated in a large trauma cohort and proven comparable to more established complex scoring systems. However, the inter-rater reliability of the mTICCS has not yet been investigated.

Methods

Therefore, a dataset of 15 randomly selected and severely injured patients (ISS ≥ 16) derived from the database of a level I trauma centre (2010–2015) was used. Moreover, 15 severely injured subjects that received MT were chosen from the same databank. A web-based survey was sent to medical professionals working in the field of trauma care asking them to evaluate each patient using the mTICCS.

Results

In total, 16 raters (9 residents and 7 specialists) completed the survey. Ratings from 15 medical professionals could be evaluated and led to an ICC of 0.7587 (95% Bootstrap confidence interval (BCI) 0.7149–0.8283). A comparison of working experience specific ICC (n = 7 specialists, ICC: 0.7558, BCI: 0.7076–0.8270; n = 8 residents, ICC: 0.7634, BCI: 0.7183–0.8335) showed no significant difference between the two groups (p = 0.67).

Conclusion

In summary, reliability values need to be considered when making clinical decisions based on scoring systems. Due to its easy applicability and its almost perfect inter-rater reliability, even with non-specialists, the mTICCS might therefore be a useful tool to predict the early need for MT in multiple trauma.

Review of Existing Scoring Systems for Massive Blood Transfusion in Trauma Patients: Where Do We Stand?

BACKGROUND

Uncontrolled bleeding is the main cause of the potential preventable death in trauma patients. Accordingly, we reviewed all the existing scores for massive transfusion posttraumatic hemorrhage and summarized their characteristics, thus making it easier for the reader to have a global view of these scores-how they were created, their accuracy and to which population they apply.

METHODS

A narrative review with a systematic search method to retrieve the journal articles on the predictive scores or models for massive transfusion was carried out. A literature search using PubMed, SCOPUS, and Google scholar was performed using relevant keywords in different combinations. The keywords used were "massive transfusion," "score," "model," "trauma," and "hemorrhage" in different combinations. The search was limited for full-text articles published in English language, human species and for the duration from January 1, 1998 to November 30, 2018.

RESULTS

The database search yielded 295 articles. The search was then restricted to the inclusion criteria which retrieved 241 articles. Duplicates were removed and full-texts were assessed for the eligibility to include in the review which resulted in inclusion of 24 articles. These articles identified 24 scoring systems including modified or revised scores. Different models and scores for identifying patients requiring massive transfusion in military and civilian settings have been described. Many of these scorings were complex with difficult calculation, while some were simple and easy to remember.

CONCLUSIONS

The current prevailing practice that is best described as institutional or provider centered should be supplemented with score-based protocol with auditing and monitoring tools to refine it. This review summarizes the current scoring models in predicting the need for MT in civilian and military trauma. Several questions remain open; i.e., do we need to develop new score, merge scores, modify scores, or adopt existing score for certain trauma setting?

Validation of the mTICCS Score as a Useful Tool for the Early Prediction of a Massive Transfusion in Patients with a Traumatic Hemorrhage

The modified Trauma-Induced Coagulopathy Clinical Score (mTICCS) presents a new scoring system for the early detection of the need for a massive transfusion (MT). While validated in a large trauma cohort, the comparison of mTICCS to established scoring systems is missing. This study therefore validated the ability of six scoring systems to stratify patients at risk for an MT at an early stage after trauma. A dataset of severely injured patients (ISS ≥ 16) derived from the database of a level I trauma center (2010-2015) was used. Scoring systems assessed were Trauma-Associated Severe Hemorrhage (TASH) score, Prince of Wales Hospital (PWH) score, Larson score, Assessment of Blood Consumption (ABC) score, Emergency Transfusion Score (ETS), and mTICCS. Demographics, diagnostic data, mechanism of injury, injury pattern (graded by AIS), and outcome (length of stay, mortality) were analyzed. Scores were calculated, and the area under the receiver operating characteristic curves (AUCs) were evaluated. From the AUCs, the cut-off point with the best relationship of sensitivity-to-specificity was used to recalculate sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). A total of 479 patients were included; of those, blunt trauma occurred in 92.3% of patients. The mean age of patients was 49 ± 22 years with a mean ISS of 25 ± 29. The overall MT rate was 8.4% (n = 40). The TASH score had the highest overall accuracy as reflected by an AUC of 0.782 followed by the mTICCS (0.776). The ETS was the most sensitive (80%), whereas the TASH score had the highest specificity (82%) and the PWH score had the lowest (51.83%). At a cut-off > 5 points, the mTICCS score showed a sensitivity of 77.5% and a specificity of 74.03%. Compared to sophisticated systems, using a higher number of weighted variables, the newly developed mTICCS presents a useful tool to predict the need for an MT in a prehospital situation. This might accelerate the diagnosis of an MT in emergency situations. However, prospective validations are needed to improve the development process and use of scoring systems in the future.

Early Identification of Acute Traumatic Coagulopathy Using Clinical Prediction Tools: A Systematic Review

: Background and objectives: Prompt identification of patients with acute traumatic coagulopathy (ATC) is necessary to expedite appropriate treatment. An early clinical prediction tool that does not require laboratory testing is a convenient way to estimate risk. Prediction models have been developed, but none are in widespread use. This systematic review aimed to identify and assess accuracy of prediction tools for ATC. Materials and Methods: A search of OVID Medline and Embase was performed for articles published between January 1998 and February 2018. We searched for prognostic and predictive studies of coagulopathy in adult trauma patients. Studies that described stand-alone predictive or associated factors were excluded. Studies describing prediction of laboratory-diagnosed ATC were extracted. Performance of these tools was described. Results: Six studies were identified describing four different ATC prediction tools. The COAST score uses five prehospital variables (blood pressure, temperature, chest decompression, vehicular entrapment and abdominal injury) and performed with 60% sensitivity and 96% specificity to identify an International Normalised Ratio (INR) of >1.5 on an Australian single centre cohort. TICCS predicted an INR of >1.3 in a small Belgian cohort with 100% sensitivity and 96% specificity based on admissions to resuscitation rooms, blood pressure and injury distribution but performed with an Area under the Receiver Operating Characteristic (AUROC) curve of 0.700 on a German trauma registry validation. Prediction of Acute Coagulopathy of Trauma (PACT) was developed in USA using six weighted variables (shock index, age, mechanism of injury, Glasgow Coma Scale, cardiopulmonary resuscitation, intubation) and predicted an INR of >1.5 with 73.1% sensitivity and 73.8% specificity. The Bayesian network model is an artificial intelligence system that predicted a prothrombin time ratio of >1.2 based on 14 clinical variables with 90% sensitivity and 92% specificity. Conclusions: The search for ATC prediction models yielded four scoring systems. While there is some potential to be implemented effectively in clinical practice, none have been sufficiently externally validated to demonstrate associations with patient outcomes. These tools remain useful for research purposes to identify populations at risk of ATC.

Early identification of trauma patients in need for emergent transfusion: results of a single-center retrospective study evaluating three scoring systems

BACKGROUND

The Trauma-Induced Coagulopathy Clinical Score (TICCS) was developed to be calculable on the site of injury to discriminate between trauma patients with or without the need for damage control resuscitation and thus transfusion. This early alert could then be translated to in-hospital parameters at patient arrival. Base excess (BE) and ultrasound (FAST) are known to be predictive parameters for emergent transfusion. We emphasize that adding these two parameters to the TICCS could improve the scoring system predictability.

METHODS

A retrospective study was conducted in the University Hospital of Liège. TICCS was calculated for every patient. BE and FAST results were recorded and points were added to the TICCS according to the TICCS.BE definition (+ 3 points if BE < - 5 and + 3 points in case of a positive FAST). Emergent transfusion was defined as the use of at least one blood product in the resuscitation room. The capacity of the TICCS, the TICCS.BE and the Trauma-Associated Severe Hemorrhage (TASH) to predict emergent transfusion was assessed.

RESULTS

A total of 328 patients were included. Among them, 14% needed emergent transfusion. The probability for emergent transfusion grows with the TICCS and the TICCS.BE values. We did not find a significant difference between the TICCS (AUC 0.73) and the TICCS.BE (AUC 0.76). The TASH proved to be more predictive (AUC 0.89). 66.6% of the patients with a TICCS ≥ 10 and 81.5% with a TICCS.BE ≥ 14 required emergent transfusion.

CONCLUSION

Adding BE and FAST to the original TICCS does not significantly improve the scoring system predictability. A prehospital TICCS > 10 could be used as a trigger for emergent transfusion activation. TASH could then be used at hospital arrival. Prehospital TASH calculation may be possible but should be further investigated.

LEVEL OF EVIDENCE

Diagnostic test, level III.