European Journal of Cardiovascular Medicine

Injuries rank as the leading cause of death for individuals under 40 years old as the sixth most prevalent cause of death worldwide. The avoidable cause of death for patients with severe trauma is haemorrhage, which accounts for around half of deaths that occur within 24 hours of the trauma [1,2]. It has been demonstrated that massive transfusion protocols (MTPs) for severe bleeding enhance outcomes; still, it is critical to identify patients with enormous haemorrhage as soon as possible [3]. To predict massive transfusion (MT) in patients with severe trauma, numerous studies have been published [4,5]. Trauma-associated severe haemorrhage (TASH) scoring methods and assessment blood consumption (ABC) are two of the very few helpful indications to anticipate the requirement for massive transfusion. These scoring systems are somewhat sophisticated, though as they need the assessment of multiple criteria, including vital signs, focused assessment with sonography for trauma (FAST) , pelvic fracture, and/or femur fracture. As a result, we must identify some practical MT indications that the emergency room may quickly and easily apply.

One tool for determining the degree of trauma in patients is the shock index (SI). It is the heart rate to systolic blood pressure ratio, first described by Allowernd Burri in 1967[6]. Hemodynamic instability, as defined by SI, often refers to a condition where the SBP is lower than the HR; it does not, however mean that the HR is lower than the SBP. To improve this,Chung et al created the idea of reverse shock index [7] which is derived by dividing SBP by HR and a smallrSIG value signifies that the patient’s condition is critical. In contrast, the GCS which evaluates consciousness, has been shown to  be a more reliable indicator of death in trauma patients.Reverse shock index and GCS are two straight forward but effective predictors combined to create rSIG[8].SI is easily collected at the patient’s bedside and can assess the shock status more precisely than HR and SBP alone because it is calculated using HR and SBP readings[9-11]. SI is also useful in identifying occur shock patients. Numerous studies have shown that SI helps predict mortality and MT in trauma patients due to its simplicity and accuracy [12-15].

They discovered that rSIG out performed SI and SIA as a predictor of in-hospital mortality nd24 hour blood transfusion. Additionally, it has been shown in two other studies [16,17] that rSIG is a reliable indicator of mortality in trauma patients. Although this study has limitations, Young Tark Lee et al [18] recent study found that rSIG is a helpful biomarker for predicting massive transfusion in patients with severe trauma. The current investigation attempts to ascertain whether rSIG can predict MT in trauma patients. It also seeks to ascertain whether rSIG can predict coagulopathy, in-hospital mortality and 24hour hospital mortality in trauma patients. rSIG predictive value for MT in trauma patients is compared to earlier scoring systems, such as the trauma-associated severe haemorrhage (TASH) Scores and the assessment blood consumption (ABC) Scores.

This study is a prospective observational single-center study that was conducted from August 2022 to April 2024 at the Emergency Medicine department of BLDE, Shri B.M. Patil Medical College Hospital and Research Centre, Vijayapura, with patients who met the criteria for inclusion and was older than 18 years.Our hospital's institutional review board gave its approval for this study.The informed consent is obtained from the patients or caretakers for the section detailing the consent. A total of 195 data forms was collected. Based on inclusion and exclusion criteria. The data is collected according to Pro-forma in terms of detailed history and clinical examination and of the patients who fulfil the inclusion criteria. All adult trauma patients who were taken directly to the emergency department were included.Those who were admitted to the emergency department(ED) with a cardiac arrest, isolated head injuries, or transfers from other hospitals were not included.

Sample Size

With Anticipated Proportion of Trauma patients 7.2% (ref: Young tark lee et al. study reverse shock index multiplied by Glasgow coma scale as a predictor of massive transfusion in trauma), the study would require a sample size of 195 to achieve a power of 80% for predicting massive transfusion by rSIG at a two-sided p-value of 0.05 with effect size- 0.059 using G*power software 3.1.9.7 (Exact - Proportion: Difference from constant (binomial test, one sample case).

Data collection variables

Patient demographic factors of interest included age, gender, and vital signs (HR, RR, SBP, and DBP). Glassgow Coma Scale (GCS), hemoglobin, PT/INR, aPTT, lactate level, and packed red blood cells transfused within the first 24 hours of admission in the ED are among the laboratory findings. We used the following formula to determine SI, SIA, and rSIG. SI=HR/SBP, SIA=SI*age, rSIG=(SI*age) ,The qSOFA score was determined by adding the 1 points for SBP≤100mmhg, GCS≤14, and RR≥22. MT in- hospital mortality,24-hour mortality, and coagulopathy are better predicted by Rsig than SI, SIA andqSOFA.

Statistical analyses

Continuous variables with normal distribution will be presented by Mean±SD and abnormal distribution by Median and Inter quartile range. Categorical variables will be presented by Frequency and percentage, and Charts.For normally distributed continuous variables will be compared using independent t test. To examine variables that are not normally distributed, the Mann Whitney U test will be used. To compare categorical variables, the Chi square test will be employed.AUROC curve [19] analysis will be carried out for Comparing rSIG with previous scoring systems, such as assessment blood consumption(ABC) and trauma-associated severe hemorrhage (TASH) Scores for predicting MT  in trauma patients.p<0.05 will be considered statistically significant. All statistical tests will perform two tailed.The data obtained will be entered in a Microsoft Excel sheet, and statistical analysis will be performed using JMP Software.

A total of 195 trauma patients needed to be admitted to the hospital over the three-year trial period,  from August 2022 to April 2024.The analysis of the sample distribution based on sex reveals insightful details about the composition of the sample group.Among all patients ,55%(107 ) were male and 45%(88 ) were female.This balanced distribution between males and females, albeit with a slight male majority, ensures that the perspectives and characteristics of both sexes are adequately captured and analysed.. The data derived from this well-rounded sample will contribute to more accurate and reliable conclusions, enhancing the overall validity of the study

Comparative Analysis of Massive and Non-Massive Transfusion Groups

In a comparative study of patients undergoing massive transfusion (N=20) versus those not undergoing massive transfusion (N=175), several key parameters were analysed to identify significant differences between the two groups (Table 1). The Prothrombin Time International Normalized Ratio (PT_INR) was found to be higher in the massive transfusion group, with a mean of 1.22 ± 0.36, compared to 1.04 ± 0.20 in the non-massive transfusion group, yielding a highly significant P value of 0.0007. Similarly, the activated Partial Thromboplastin Time (aPTT) was markedly elevated in the massive transfusion group, averaging 34.50 ± 8.6, as opposed to 27.40 ± 6.0 in the non-massive transfusion group, with a P value of less than 0.0001.

Table 1 : variables of labortatory , vital sign

Hb: Heamoglobin; Prothrombin Time International Normalized Ratio (PT_INR);activated Partial Thromboplastin Time (aPTT)

Haemoglobin (Hb) levels were comparable between the two groups, with the massive transfusion group having a mean of 12.70 ± 1.05 and the non-massive transfusion group slightly higher at 12.80 ± 1.6, showing no significant difference (P value = 0.7855). However, lactic acid levels were significantly elevated in the massive transfusion group, with a mean of 5.07 ± 1.6, in contrast to 2.50 ± 1.4 in the non-massive transfusion group, also with a P value of less than 0.0001.These findings highlight the marked differences in coagulation parameters and lactic acid levels between patients receiving massive transfusions and those who do not, underscoring the need for careful monitoring and management of these critical variables in transfusion practices

The respiratory rate in the massive transfusion group averaged 25 ± 2.7, significantly higher than the 22 ± 2.4 observed in the non-massive transfusion group, with a P value of less than 0. 0001.The systolic blood pressure in the massive transfusion group was significantly lower, averaging 75 ± 8.0, in contrast to 120 ± 10.4 in the non-massive transfusion group, with a P value of less than 0.0001. Likewise, the diastolic blood pressure was markedly reduced in the massive transfusion group, with a mean of 60 ± 7.0, compared to 90 ± 8.7 in the non-massive transfusion group, again with a P value of less than 0.0001(table 1).These pronounced differences in respiratory rate, heart rate, and blood pressure between the two groups highlight the critical impact of massive transfusion on these vital physiological parameters, emphasizing the importance of vigilant monitoring and targeted management in patients undergoing such procedures

Clinical Severity and Assessment Scores in Massive vs. Non-Massive Transfusion Groups

The Glasgow Coma Scale (GCS) scores were significantly lower in the massive transfusion group, with a mean of 8 ± 2.4, compared to 15 ± 1.3 in the non-massive transfusion group, indicating a higher degree of impaired consciousness (P value < 0.0001). The shock index, which is a measure of hemodynamic instability, was markedly higher in the massive transfusion group, with a mean of 1.34 ± 0.6, versus 0.72 ± 0.4 in the non-massive transfusion group (P value < 0.0001). The SIA (age-adjusted shock index) and rSIG (reverse shock index multiplied by Glasgow Coma Scale) further highlighted the severity in the massive transfusion group, with values of 61.02 ± 4.5 and 6.37 ± 3, respectively, compared to 34.34 ± 2.7 and 19.54 ± 2.9 in the non-massive transfusion group (P value < 0.0001).(Table 2)

The quick Sequential Organ Failure Assessment (qSOFA) scores averaged 2.00 ± 1.0 in the massive transfusion group, significantly higher than the 1 ± 0.6 observed in the non-massive transfusion group, indicating a greater risk of organ failure (P value < 0.0001). The Injury Severity Score (ISS), which quantifies trauma severity, was also higher in the massive transfusion group, with a mean of 31.00 ± 7.4, compared to 18.00 ± 6.1 in the non-massive transfusion group (P value < 0.0001).(Table2)

Additionally, the assessment of blood consumption (ABC) score and the TASH (Trauma-Associated Severe Haemorrhage) score were both significantly elevated in the massive transfusion group, with means of 2.00 ± 0.3 and 21.00 ± 5.6, respectively, compared to 1 ± 0.2 and 10.00 ± 3.4 in the non-massive transfusion group, underscoring the higher blood product usage and haemorrhage severity in these patients (P value <0.0001)(Table 2).

GCS:Glasgow coma scale,SIA: Age shock index, rSIG: reverse shock index multiplied by glagow coma scale ,qSOFA: quick sequential organ failure assessment.

These findings highlight the substantial differences in clinical severity and assessment scores between the two groups, emphasizing the critical condition of patients requiring massive transfusion and the need for intensive management and monitoring.

Coagulopathy in the Massive Transfusion Group

The study focused on a sample of 20 patients who required massive transfusions. The objective was to determine the prevalence of coagulopathy within this group and to analyse the significance of this association. Out of the 20 patients, 5 (25%) were found to have coagulopathy.This extremely low P value(0.0001) indicates a highly significant relationship, underscoring the critical nature of this finding.Therefore, the study conclusively demonstrates that patients undergoing massive transfusions are significantly more likely to develop coagulopathy.This result has important clinical implications. The strong association between massive transfusion and coagulopathy suggests that healthcare providers must be particularly vigilant in monitoring coagulation parameters in these patients.

Mortality in the Massive Transfusion Group

Out of the 20 patients in the massive transfusion group, 2 patients (10%) unfortunately succumbed, while 18 patients (90%) survived. This distribution highlights the relatively high survival rate but also points to the notable presence of mortality among these critically ill patients.The statistical analysis revealed a P value of less than 0.0001, indicating a highly significant association between massive transfusion and mortality.. This result strongly suggests that the need for massive transfusion is closely linked with increased mortality risk.

ROC-rSIG vs ABC vs TASH on X axis 1-specificity with y axis sensitivity. (graph 1)

The ROCcurve analysis presented here evaluates the performance of three different diagnostic tests: rSIG, assessment blood, and TASH Score. The ROC curve, which plots sensitivity (true positive rate) against 1-specificity (false positive rate), is a graphical representation of a diagnostic test’s ability to discriminate between positive and negative cases. In this analysis, the area under the curve (AUC) serves as a crucial metric for assessing the accuracy of the tests. The results show that the AUC values are 0.8828 (Graph 1)forrSIG, 0.930  (Graph 1)for assessment blood, and 0.924 (Graph 1) for TASH Score. These values indicate that all three tests have high discriminative power, with assessment blood showing the highest accuracy, followed closely by TASHScore and rSIG. The reference line, representing a test with no discriminative ability, is included for comparison. The ROC curves depicted in the graph highlight the superior performance of the assessment blood and TASHScore tests in terms of sensitivity and specificity compared to the rSIG test. This analysis underscores the effectiveness of these diagnostic tools in clinical practice, providing valuable insights into their relative strengths and potential applications in patient care.

Graph 1: ROC-rSIG vs ABC vs TASH on X axis 1-specificity with y axis sensitivity.

rSIG in determining mortality time (MT) in patients with severe trauma. Additionally, the study aimed to compare the predictive abilities of rSIG with those of SI, SIA, and qSOFA. The results of the current study indicate that the predictive accuracy of rSIG for MT was significantly superior to SI, SIA, and qSOFA. In addition, the rSIG demonstrated superior AUROC in predicting coagulopathy, in-hospital mortality, and 24-hour mortality compared to other indices.

The rSIG may be quantified by use the reverse shock index and Glasgow Coma Scale (GCS). Systemic Inflammatory Response (SI) is very pragmatic and valuable in evaluating the hemodynamic condition of trauma patients. Nevertheless, the calculation of SI involves dividing HR (heart rate) by SBP (systolic blood pressure), which contradicts the fundamental principle of shock. Hemodynamic instability often refers to a condition where the systolic blood pressure (SBP) is lower than the heart rate (HR). However, it is important to note that hemodynamic instability does not always imply a situation where the HR is lower than the SBP, as shown by the stroke index (SI). In order to enhance this, Chung et al. established the notion of reverse shock index (20) which is computed by dividing SBP by HR, and a low rSI value indicates a serious situation in the patient. Furthermore, the Glasgow Coma Scale (GCS), which evaluates the degree of awareness, is recognised as a more reliable indicator of death risk in individuals with traumatic injuries. rSIG is a fusion of two potent predictors: reverse shock index and GCS(21).

The rSIG was initially proposed by Kimura and Tanaka in 2018 [22]. An evaluation was conducted on trauma patients from 256 hospitals in Japan between 2006 and 2015 in order to identify a more accurate predictor than the Injury Severity Score (SI) for post-injury mortality and the need for early blood transfusion. The researchers conducted a comparison of several modified models using the SI method and determined that the rSIG model was a dependable tool for evaluating the risk in trauma patients. The reported AUROC of rSIG for in-hospital mortality was 0.901. Wu et al. conducted an external validation of the rSIG in patients who were hospitalised to a level 1 trauma centre in Taiwan [23].

The study's findings indicated that the predictive accuracy of death was greater with rSIG compared to SI in trauma patients. The AUROC of rSIG for mortality prediction was 0.83. In a recent study, Chu et al. utilisedrSIG to assess the in-hospital mortality rate among patients with severe trauma and brain damage [24]. They discovered that the use of rSIG was beneficial in predicting the mortality risk in severe trauma patients with brain damage. The current study found that the AUROC (Area Under the Receiver Operating Characteristic) of rSIG (a specific measure) for predicting in-hospital mortality was 0.812. Furthermore, the predictive value of rSIG for mortality was better than that of SI (another measure), SIA (another measure), and qSOFA (another measure). The findings align with the outcomes of previous investigations, indicating that rSIG serves as a valuable indicator of death in trauma patients. One noteworthy aspect of our investigation is that all instruments, including rSIG, have a low positive predictive value (PPV) and a high negative predictive value (NPV) for medical treatment (MT) and death. The low occurrence of MT (7.2%) and in-hospital death (8.4%) [53] is the likely cause of this phenomenon. (25).

It is worth mentioning that the majority of prior research has examined the correlation between rSIG and mortality in trauma patients. As far as we know, there have been no studies that have revealed the ability to predict mortality in individuals with severe trauma. The AUROC of rSIG for MT in our study was 0.842, indicating that rSIG had a superior predictive value compared to SI, SIA, and qSOFA. The underlying cause for this outcome is ambiguous. One potential reason is that traumatic brain injury may be accompanied by scalp lacerations, facial bone fractures, and oronasal bleeding, which can cause bleeding (26).

Additionally, a trauma patient can experience significant mental decline even without brain injury if they fall into severe shock [27].

Therefore, the incorporation of both a bleeding measure (rSI) and consciousness assessment (GCS) provides a more comprehensive evaluation of the patient's trauma condition. An advantage of our study is that we have determined that rSIG can serve as a prognostic indicator for coagulopathy. Approximately one-third of trauma patients treated through the Emergency Department (ED) experience coagulopathy, which can lead to multiple organ failure and a significant risk of death [28,29].

There are two forms of trauma-induced coagulopathy: acute traumatic coagulopathy (ATC) and resuscitation-associated coagulopathy. ATC in trauma patients refers to the coagulopathy that is caused directly by the trauma itself. On the other hand, resuscitation-associated coagulopathy is coagulopathy that is worsened by factors such as hypothermia, metabolic acidosis, consumption of coagulating factors, and haemodilution [30].

Early identification of coagulopathy can result in the initiation of rewarming, correction of acidosis, balanced transfusion, and activation of massive transfusion protocol (MTP). Based on our current understanding, this study is the first to use rSIG to make predictions about coagulopathy. Furthermore, our investigation showed that rSIG has superior predictive accuracy compared to SI, SIA, and qSOFA.

rSIG in determining mortality time (MT) in patients with severe trauma. Additionally, the study aimed to compare the predictive abilities of rSIG with those of SI, SIA, and qSOFA. The results of the current study indicate that the predictive accuracy of rSIG for MT was significantly superior to SI, SIA, and qSOFA. In addition, the rSIG demonstrated superior AUROC in predicting coagulopathy, in-hospital mortality, and 24-hour mortality compared to other indices.

The rSIG may be quantified by use the reverse shock index and Glasgow Coma Scale (GCS). Systemic Inflammatory Response (SI) is very pragmatic and valuable in evaluating the hemodynamic condition of trauma patients. Nevertheless, the calculation of SI involves dividing HR (heart rate) by SBP (systolic blood pressure), which contradicts the fundamental principle of shock. Hemodynamic instability often refers to a condition where the systolic blood pressure (SBP) is lower than the heart rate (HR). However, it is important to note that hemodynamic instability does not always imply a situation where the HR is lower than the SBP, as shown by the stroke index (SI). In order to enhance this, Chung et al. established the notion of reverse shock index (20) which is computed by dividing SBP by HR, and a low rSI value indicates a serious situation in the patient. Furthermore, the Glasgow Coma Scale (GCS), which evaluates the degree of awareness, is recognised as a more reliable indicator of death risk in individuals with traumatic injuries. rSIG is a fusion of two potent predictors: reverse shock index and GCS(21).

The rSIG was initially proposed by Kimura and Tanaka in 2018 [22]. An evaluation was conducted on trauma patients from 256 hospitals in Japan between 2006 and 2015 in order to identify a more accurate predictor than the Injury Severity Score (SI) for post-injury mortality and the need for early blood transfusion. The researchers conducted a comparison of several modified models using the SI method and determined that the rSIG model was a dependable tool for evaluating the risk in trauma patients. The reported AUROC of rSIG for in-hospital mortality was 0.901. Wu et al. conducted an external validation of the rSIG in patients who were hospitalised to a level 1 trauma centre in Taiwan [23].

The study's findings indicated that the predictive accuracy of death was greater with rSIG compared to SI in trauma patients. The AUROC of rSIG for mortality prediction was 0.83. In a recent study, Chu et al. utilisedrSIG to assess the in-hospital mortality rate among patients with severe trauma and brain damage [24]. They discovered that the use of rSIG was beneficial in predicting the mortality risk in severe trauma patients with brain damage. The current study found that the AUROC (Area Under the Receiver Operating Characteristic) of rSIG (a specific measure) for predicting in-hospital mortality was 0.812. Furthermore, the predictive value of rSIG for mortality was better than that of SI (another measure), SIA (another measure), and qSOFA (another measure). The findings align with the outcomes of previous investigations, indicating that rSIG serves as a valuable indicator of death in trauma patients. One noteworthy aspect of our investigation is that all instruments, including rSIG, have a low positive predictive value (PPV) and a high negative predictive value (NPV) for medical treatment (MT) and death. The low occurrence of MT (7.2%) and in-hospital death (8.4%) [53] is the likely cause of this phenomenon. (25).

It is worth mentioning that the majority of prior research has examined the correlation between rSIG and mortality in trauma patients. As far as we know, there have been no studies that have revealed the ability to predict mortality in individuals with severe trauma. The AUROC of rSIG for MT in our study was 0.842, indicating that rSIG had a superior predictive value compared to SI, SIA, and qSOFA. The underlying cause for this outcome is ambiguous. One potential reason is that traumatic brain injury may be accompanied by scalp lacerations, facial bone fractures, and oronasal bleeding, which can cause bleeding (26).

Additionally, a trauma patient can experience significant mental decline even without brain injury if they fall into severe shock [27].

Therefore, the incorporation of both a bleeding measure (rSI) and consciousness assessment (GCS) provides a more comprehensive evaluation of the patient's trauma condition. An advantage of our study is that we have determined that rSIG can serve as a prognostic indicator for coagulopathy. Approximately one-third of trauma patients treated through the Emergency Department (ED) experience coagulopathy, which can lead to multiple organ failure and a significant risk of death [28,29].

There are two forms of trauma-induced coagulopathy: acute traumatic coagulopathy (ATC) and resuscitation-associated coagulopathy. ATC in trauma patients refers to the coagulopathy that is caused directly by the trauma itself. On the other hand, resuscitation-associated coagulopathy is coagulopathy that is worsened by factors such as hypothermia, metabolic acidosis, consumption of coagulating factors, and haemodilution [30].

Early identification of coagulopathy can result in the initiation of rewarming, correction of acidosis, balanced transfusion, and activation of massive transfusion protocol (MTP). Based on our current understanding, this study is the first to use rSIG to make predictions about coagulopathy. Furthermore, our investigation showed that rSIG has superior predictive accuracy compared to SI, SIA, and qSOFA.

The purpose of the current study was to assess the predictive ability of the Glasgow Coma Scale (rSIG) multiplied by the reverse shock index (rSIG) for identifying patients who require massive transfusions (MT) after suffering severe trauma. Additionally, the study compared the predictive abilities of rSIG with those of the Shock Index (SI), age-adjusted SI (SIA), and quick Sequential Organ Failure Assessment (qSOFA). The results demonstrate that rSIG has superior predictive accuracy for MT, in-hospital mortality, 24-hour mortality, and coagulopathy compared to SI, SIA, and qSOFA. Specifically, the AUROC for rSIG in predicting in-hospital mortality was 0.812, highlighting its reliability as a prognostic tool.The study provides compelling evidence of the significant relationship between massive transfusion and the incidence of coagulopathy.

Overall, the study supports the use of rSIG as a practical and effective tool for early identification of trauma patients at high risk for massive transfusion, coagulopathy, and mortality These findings emphasize the necessity for enhanced clinical strategies to manage and prevent coagulopathy in patients requiring massive transfusions, ultimately aiming to improve patient outcomes in critical care settings

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