Shock Index as a Predictor of Mortality in Trauma: A Systematic Review and Meta-Analysis of Observational Studies

Ezequiel Garcia-Ballestas; Javier Villafane; Claudio Rivas-Palacios; Wendy Henriquez; William A Florez Perdomo; Miguel A Duran López; Tariq Janjua; Luis Rafael Moscote-Salazar; Amit Agrawal

doi:10.32587/jnic.2026.00934

J Neurointensive Care > Volume 9(1); 2026 > Article

Garcia-Ballestas, Villafane, Rivas-Palacios, Henriquez, Perdomo, López, Janjua, Moscote-Salazar, and Agrawal: Shock Index as a Predictor of Mortality in Trauma: A Systematic Review and Meta-Analysis of Observational Studies

Review Article

Journal of Neurointensive Care 2026; 9(1): 11-18.

Published online: April 30, 2024

DOI: https://doi.org/10.32587/jnic.2026.00934

Shock Index as a Predictor of Mortality in Trauma: A Systematic Review and Meta-Analysis of Observational Studies

Ezequiel Garcia-Ballestas^1,², Javier Villafane¹, Claudio Rivas-Palacios³, Wendy Henriquez¹, William A Florez Perdomo^2,^4,⁵, Miguel A Duran López⁵, Tariq Janjua⁶, Luis Rafael Moscote-Salazar^1,², Amit Agrawal⁷

¹Center of Biomedical Research (CIB), Faculty of Medicine, University of Cartagena, Cartagena, Colombia

²Latinamerican Council of Neurocritical Care (CLaNI), Bogota, Colombia

³Department of Neurosurgery, University of Cartagena, Cartagena, Colombia

⁴Department of surgery, Sahagún Clínic, Sahagún – Córdoba, Colombia

⁵Surcolombian University, Neiva- Huila, Colombia

⁶Department of Neurology and Critical Care Medicine, Regions Hospital, Saint Paul, MN, USA

⁷Department of Neurosurgery, All India Institute of Medical Sciences, Saket Nagar, Bhopal, India

Corresponding Author: Ezequiel Garcia-Ballestas, MD Center of Biomedical Research (CIB), Faculty of Medicine, University of Cartagena, Calle de la Universidad, Cra. 6 #36-100, Cartagena, Bolívar, Colombia Email: ezegames@hotmail.es

Received: January 20, 2026 Revised: March 22, 2026 Accepted: April 10, 2026

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The shock index (SI) is a rapid and practical physiological parameter used to identify decompensated shock and assess severity in cases of progressive hemodynamic decline. This study aims to determine an optimal SI cutoff for adult trauma patients (18 years or older). The research was conducted in accordance with MOOSE checklist for systematic reviews and a comprehensive search for observational studies through December 2023 in various databases. Search terms included “Shock index” AND “Trauma”. Statistical analysis encompassed the extraction of data on mortality, shock index, ROC for mortality, and cutoff point calculations from individual studies. A pooled ROC analysis was performed using a random-effects analysis model. Heterogeneity was assessed via Chi-square and I-squared calculations. Following rigorous search and assessment procedures, 6 studies comprising 292,171 participants were considered eligible for the meta-analysis. These studies predominantly featured retrospective observational cohorts. An optimal SI cut-off of greater than 0.75 was identified (Youden Index J = 0.5673). The combined ROC area value was found to be 0.779 (95 percent confidence interval: 0.707 to 0.852), indicating a statistically significant predictive value for shock index in trauma patient outcomes. The moderate inconsistency level (I-squared = 38.55 percent) highlights the need to account for methodological variations among the studies. The statistically significant aggregated ROC area value supports the utility of shock index as a diagnostic tool at a threshold of greater than 0.75, provided that neurological confounders are considered in neuro-trauma cases.

Keywords: Shock index; Trauma; Shock; Mortality.

INTRODUCTION

Trauma is a major health concern, annually an estimated 5 million of deaths are calculated¹⁾. Particularly, hemorrhagic shock is one of the main contributors to death and disability in polytraumatized patients. Due to increased incidence in young people, its high mortality rate contributes to nearly 75 million years of life lost²⁾. The shock index (SI) is a very useful, quick and easily applicable physiological parameter to detect decompensated shock and estimate severity in cases of progressive hemodynamic deterioration^3-⁵⁾ This is obtained from the quotient by dividing the heart rate (HR) between the systolic blood pressure (SBP) [SI= HR/SBP]; therefore, it is considered a useful guide for early diagnosis of acute hypovolemia when there are normal HR and SBP^6,⁷⁾. Over time, variants of the SI have been proposed in order to adjust it to the variety of patients in the emergency department, such as the Modified Shock Index (MSI), the Pediatric Age-Adjusted Shock Index (SIPA), the Obstetric Shock Index (OSI), the Respiratory Adjusted Shock Index (RASI), the reverse shock index (rSI), among others; This has made it possible to broaden the use of the physiological basis by which the SI scale is governed to estimate severity according to the different types of shock associated with trauma in the context of patients who, due to characteristics such as age, present different physiological mechanisms; this has been shown to be useful for those patients presenting with bleeding and trauma, to identify patients who are at higher risk of requiring massive transfusion^4,^8,⁹⁾, for patients requiring endotracheal intubation¹⁰⁾ to help to identify patients at risk of post-intubation hypotension and for patients with suspected sepsis^11-¹³⁾. The SI was initially used to determine hypovolemia in patients with septic or hemorrhagic shock, evidenced by a value > 0.7^11,¹⁴⁾. Its prognostic capacity has also been studied in acute myocardial infarction¹⁵⁾, acute heart failure¹⁶⁾ and in trauma^17-²²⁾. Previously two meta-analyses have attempted to demonstrate an association between SI and outcomes in patients who suffered major trauma, however, no optimal cut-off has been proved to predict outcomes for adult patients. Carsetti et al.²³⁾ conducted a meta-analysis with several flaws in design which influenced final results, such as including pediatric population within the analysis. On the other hand, Vang et al.²⁴⁾ did not attempt to establish an optimal cut-off for SI in trauma patients but demonstrated 4-fold increased risk of mortality. Therefore, our study is the first to attempt establishing an optimal cut-off for SI in adult patients (>18 years) who suffered a major trauma.

METHODS

The scheme followed was according to the MOOSE checklist²⁵⁾ applied to systematic reviews of observational studies. For the presentation of systematic reviews and meta-analysis, the quality of the evidence was evaluated through the Newcastle-Ottawa scale²⁶⁾. A search for observational studies was carried out in the following databases: PUBMED (until December 2023); Cochrane Injuries Group Specialized Register (until December 2023); Cochrane Central Register of Controlled Trials (The Cochrane Library) (until December 2023); MEDLINE (Ovid); EMBASE (Ovid); PubMed; as well as the reference list of included studies and other relevant data. We conducted the Internet search through the Google Scholar search engine (www.googlescholar.com) and the Science Direct database (www.sciencedirect.com) with the terms selected in the search strategy. The search was constructed using the following Medical Subject Heading (MeSH) terms and descriptors: “Shock index” AND “Trauma”.

Statistical Analysis

Individually and separately, the following data were extracted: mortality, shock index, and ROC for mortality, calculating the cut-off points for each study. Authors were contacted for missing data, complete study protocols and data matrix. Doubts were clarified by consultation by consensus. Statistical analysis was performed using the pooled ROC for dichotomous variables with a random-effects analysis model calculated using the MedCalc 19.03 software (London, UK). Heterogeneity was assessed by calculating Chi-square and the I-squared statistic. An I-squared value of 38.55 percent was observed, and a sensitivity analysis was performed to confirm the robustness of the results. Statistical significance was defined as P less than 0.05.

RESULTS

Study Selection

After conducting the systematic search of the information following our strategy, 72 bibliographic citations were identified and after removal of duplicates (n=17), 55 studies were considered potentially eligible based on the title or abstract, or both, and full texts were obtained. After a review of the full text, 30 studies were considered eligible, 15 were ruled out because they did not meet the inclusion criteria and 6 met the inclusion criteria for the review (Fig. 1).

Systematic Review

Among the 6 studies included, a total of 292,171 participants were found to be eligible for the meta-analysis. The studies were mostly retrospective observational cohorts. The studies examine various patient populations, all contributing to the understanding of trauma patient outcomes (Table 1). The main outcomes evaluated are mortality rates and related metrics, such as ROC AUC, sensitivity, and specificity (Table 2). The lengths of follow-up vary among the studies, restricted to in-hospital stay and 48-hour mortality.

Meta-analysis

Forest plot pooling each study (Fig. 2) and ROC analysis (Fig. 3) results from studies are shown, including their individual and combined ROC area values, along with other statistical measures used to assess the validity and significance of the findings (Table 4). The studies collectively suggest an aggregated ROC area value of 0.779 with a 95 percent confidence interval of 0.707 to 0.852, and this combined result is statistically significant (P less than 0.001). The level of inconsistency among the studies is indicated by the I-squared value of 38.55 percent. Diagnostic performance metrics for different shock index thresholds, including the optimal cut-off of greater than 0.75, are provided (Table 5). The quality of the studies is evaluated using the Newcastle Ottawa Scale (NOS), with scores ranging from 5 to 7 (Table 3). Risk of publication bias was assessed via a funnel plot (Fig. 4), showing no apparent asymmetry.

DISCUSSION

Our study found that the aggregated ROC area value is significantly different from zero, reinforcing the validity of shock index as a predictor for trauma patient outcomes. Overall, this meta-analysis brings together multiple studies to provide a comprehensive overview of the predictive value of shock index for trauma patient outcomes. The statistically significant combined result supports the use of shock index as a valuable tool for predicting these outcomes. However, the moderate level of inconsistency highlights the need to consider the variations in methodologies and patient populations among the studies when interpreting the findings. The hypothesis proposed by Allgöwer and Burri consists in the ability of SI to detect progressively unfavorable hemodynamic states earlier in trauma patients compared to HR or SBP as individual markers^7, ¹⁴⁾. Hypotension can be seen mainly in late shock, due to reflex tachycardia, a compensatory mechanism; therefore, measurement of systolic blood pressure alone could not indicate that the patient is in an early state of shock³⁾.

Shock usually occurs as a consequence of inadequate cardiac output. For this reason, the presence of cardiac anomalies that decrease the pump capacity of the heart, decreased venous return due to decreased blood volume, decreased vascular tone, or obstruction of blood flow will cause decreased cardiac output; As a result, in the non-progressive phase of shock, the central nervous system ischemic response and baroreceptor reflexes cause stimulation of the sympathoadrenal response when blood pressure falls below 50 mmHg, resulting in increased heart rate and an improvement in myocardial contractility as a compensatory mechanism, thus maintaining blood pressure due to constriction of the arteries, increasing arterial and venous peripheral vascular resistance, consequently producing an increase in venous return, which improves preload and causes a redistribution of blood to vital organs^27,²⁸⁾. However, when shock is not attended and treated correctly, the progressive stage of shock begins, therefore sustained systemic vasoconstriction and progressive hypovolemia lead to tissue ischemia that produces the release of vasoactive mediators from the affected cells, thus changing the myocardial contractility, vascular tone and promoting the release of inflammatory mediators that subsequently increase capillary permeability and alter organ function more markedly^12,²⁷⁾.

It is worth noting that in patients with trauma, who also present head trauma (TBI), low and high SI values (less than 0.4 and greater than 0.79) increase the probability of mortality, compared to those without TBI, in which it only increases when SI is greater than 0.79¹⁷⁾. On the other hand, SI values less than 0.5 could be explained by high SBP^14,²⁹⁾ which, consequently, increase the risk of intracerebral brain hemorrhage (ICH) expansion^30,³¹⁾, mainly within the first 24 hours³²⁾. SI values less than 0.5 can also be found in patients with bradycardia and elevated SBP (Cushing's reflex), which is a clinical sign of intracranial hypertension^33,³⁴⁾.

The Shock Index in Neuro-Trauma: The Cushing Reflex Paradox. This distinction is vital for accurate triage. In the neuro-ICU setting, bradycardia and hypertension (resulting in a low shock index) often signal impending herniation rather than hemodynamic stability. Therefore, a low SI in the context of TBI should not be treated with the same prognostic weight as a normal SI in general trauma. Without this distinction, clinicians may encounter false negatives for shock. We recommend that the proposed cutoff be interpreted alongside the Glasgow Coma Scale (GCS) and Mean Arterial Pressure (MAP) to ensure clinical accuracy in neurosurgical patients.

In the MSI, systolic blood pressure is replaced in the equation by mean arterial pressure (MAP) (MSI = HR/MAP). The MSI has been shown to be a good predictor of clinical outcomes in studies³⁵⁾. Another proposal included with good results is the shock index multiplied by age (Age-Adjusted Shock index: ASI), indicating that it is a good predictor of mortality in older adult patients³⁶⁾. Given that age decreases the physiological reserve, the ASI could be a measure that improves prognostic accuracy in older age groups. In the field of pediatrics, the SI adjusted by age ranges (SIPA) was developed, proving to be more reliable than the cut-offs for standard adults^37-³⁹⁾. Based on several studies carried out in Taiwan, the concept of inverse SI (rSI) was introduced, defined as the relationship between SBP and HR (RSI = SBP/HR), concluding that RSI less than 1 was associated with poor clinical outcomes, so it could help identify trauma patients at high risk of mortality, even those who do not yet have arterial hypotension^40-⁴²⁾. Considering the prognostic capacity of the Glasgow Coma Scale (GCS) in brain injuries, an investigation carried out in hospitals in Japan led to the proposal of a new scoring tool, the Reverse shock index multiplied by the Glasgow Coma Scale (rSIG), originated from a multicenter retrospective study, after multiplying the rSI by GCS score⁴³⁾.

The American Stroke Association (ASA) in conjunction with the American Heart Association (AHA) recently published the results of a multicenter retrospective observational study, which showed the prognostic value of SI for in-hospital mortality and post-acute stroke disability in the population of the United States of North Americ³³⁾. Despite this, the prognostic capacity of SI in the development of secondary brain lesions of ICH (for example: intracranial hypertension, herniation and cerebral ischemia) has not been clearly established, nor in the indication of surgical treatment.

CONCLUSION

Overall, this meta-analysis sheds light on the value of using different injury severity scoring systems and shock index measurements to understand trauma patient outcomes. Our results identify an optimal shock index cut-off of greater than 0.75 as a significant predictor of mortality in adult trauma patients. The statistically significant aggregated ROC area value of 0.779 suggests the utility of shock index as a diagnostic tool in trauma assessment, despite the moderate inconsistency observed among the studies. In the context of neurocritical care, clinicians should interpret the shock index with caution, as neurological confounders such as the Cushing's reflex can alter heart rate and blood pressure, potentially leading to a falsely low shock index despite critical injury. Nevertheless, the shock index remains a rapid, bedside-applicable tool that provides valuable prognostic information for the management of adult trauma.

NOTES

Ethics statement

Not applicable

Author contributions

Conceived and designed the analysis: WF, EGB, LRMS. Collected the data: WF, WH, EGB. Contributed data or analysis tools; EGB, CRP, AA, LRMS, JV. Performed the analysis: CRP, WF, AA, JV, TJ. Wrote the paper: EGB, JV, WH, CRP, MDL, AA, LRMS. Other contributions: EGB, CRP, MDL, LRMS.

Conflict of interest

There are no conflicts of interest to disclose.

Funding

Authors acknowledge that no financial support was received to conduct this study.

Data availability

None.

Acknowledgments

None.

Fig. 1.

PRISMA flowchart.

Fig. 2.

Forest plot assessing shock index cut-off to predict mortality in patients with trauma.

Fig. 3.

ROC analysis including individual and combined ROC area values assessing the optimal cut-off for shock index to predict mortality in patients with trauma.

Fig. 4.

Funnel plot of studies showing the risk of publication bias among studies included.

Table 1.

Characteristic of included studies

Study	Type	Patients (N)	Patient details/severity	Outcomes	Length of follow-up	NOS
King, 1996^⁴⁴⁾	Retrospective observational cohort	1,101	Mean age: 36.9. (Excluded GCS ≤ 8)	Mortality, ISS, ICU stay, Blood Transfusion	In Hospital (24 hours)	6/7
Cannon, 2009^⁶⁾	Retrospective observational cohort	2,445	Median ISS: 17 (in high SI group)	Mortality, ISS	In Hospital	6/7
Bruijns, 2013^⁴⁵⁾	Retrospective observational cohort	69,367	ISS 9–15: 46.4%; ISS >15: 4.8%	48-hour Mortality	48 Hours	6/7
Pandit, 2014^²²⁾	Retrospective observational cohort	217,190	Median GCS: 14 (3–15); Median ISS: 9	Mortality, OR, Blood Transfusion, Laparotomy	In Hospital	5/7
Montoya, 2015^¹⁾	Retrospective observational cohort	666	ISS (SI <0.9): 9.6 ± 3.9; ISS (SI >0.9): 17.6 ± 11.1	Mortality, OR, Lactate	In Hospital (24 hours)	5/7
Campos-Serra, 2018^⁴⁾	Retrospective observational cohort	1,402	Mean ISS: 20.9 ± 15.8	Mortality, ROC AUC, Sens/Spec, NPV/PPV	In Hospital	7/7

NOC: Newcastle Ottawa Scale.

Table 2.

Analysis of outcome sorted by study

Study	ROC AUC	SE	Sensitivity	CI 95% Sensitivity	Specificity	CI 95% specificity	Optimal cut-off
King, 1996^⁴⁴⁾	0.75	0.1	57%	20%-94%	94%	92%-95%	≥ 0.8
Cannon, 2009^⁶⁾	0.737	0.108	77.78%	40%-97.2%	66.67%	38.4%-88.2%	>0.7
Bruijns, 2013^⁴⁵⁾	0.73	0.04	45.3%	39.2%-51.5%	90%	89.8%-90.2%	≥ 0.8
Pandit, 2014^²²⁾	0.920	0.0573	100%	59%-100%	82.35%	56.6%-96.2%	>0.8
Montoya, 2015^¹⁾	0.727	0.113	62.5%	24.5%-91.5%	81.25%	54.4%-96%	>0.9
Campos-Serra, 2018^⁴⁾	0.749	0.08	59.2%	53.2%-65.1%	79%	76.6%-91.6%	≥ 0.8

Table 3.

Newcastle–Ottawa scale for quality assessment of studies included in this meta-analysis

Study	Representativeness of sample	Size sample	Source of information	Demonstration that outcome was not present at start	Confusion variable control	Assessment of outcome	Enough follow-up period	Total Score
King, 1996^⁴⁴⁾	★	★	★	★	★	★		6/7
Cannon, 2009^⁶⁾	★	★		★	★	★	★	6/7
Bruijns, 2013^⁴⁵⁾	★	★	★	★	★	★		6/7
Pandit, 2014^²²⁾	★	★		★	★	★		5/7
Montoya, 2015^¹⁾	★	★		★	★	★		5/7
Campos-Serra, 2018^⁴⁾	★	★	★	★	★	★	★	7/7

★ represents that the study fulfilled the criterion for that specific item in the Newcastle–Ottawa Scale.

Table 4.

Analysis of outcome sorted by study and pooled analysis

Study	ROC area	Standard error	95% CI	z	P	Weight (%)
King, 1996^⁴⁴⁾	0.750	0.100	0.554 to 0.946			10.89
Cannon, 2009^⁶⁾	0.737	0.108	0.525 to 0.949			9.63
Bruijns, 2013^⁴⁵⁾	0.730	0.0400	0.652 to 0.808			32.22
Pandit, 2014^²²⁾	0.920	0.0573	0.808 to 1.000			23.13
Montoya, 2015^¹⁾	0.727	0.113	0.506 to 0.948			8.94
Campos-Serra, 2018^⁴⁾	0.749	0.0800	0.592 to 0.906			15.20
Total (random effects)	0.779	0.0372	0.707 to 0.852	20.971	<0.001	100.00
Q	8.1371
DF	5
Significance level	P = 0.1488
I² (inconsistency)	38.55%

Table 5.

Analysis of outcome sorted by each cut-off point

Cut-off point	Sensitivity	95% CI	Specificity	95% CI	Positive predictive value	95% CI	Negative predictive value	95% CI	Likelihood ratio +	Likelihood ratio -
≥0.3	0	0.0-24.7	100	79.4-100.0	100	98.0-100.0	55.2	1.0-1.0	1	0
>0.3	15.38	1.9-45.4	100	79.4-100.0	100	98.0-100.0	59.3	0.9-1.5	1.18	0
>0.4	23.08	5.0-53.8	100	79.4-100.0	100	98.0-100.0	61.5	1.0-1.8	1.3	0
>0.5	30.77	9.1-61.4	100	79.4-100.0	100	98.0-100.0	64.0	1.0-2.1	1.44	0
>0.6	61.54	31.6-86.1	93.75	69.8-99.8	88.9	53.3-98.2	75.0	1.2-4.9	2.44	0.1
>0.7	69.23	38.6-90.9	81.25	54.4-96.0	75.0	50.4-89.9	76.5	1.1-6.2	2.64	0.27
>0.727	69.23	38.6-90.9	75.0	47.6-92.7	69.2	47.2-85.0	75.0	1.0-5.8	2.44	0.36
>0.73	69.23	38.6-90.9	68.75	41.3-89.0	64.3	44.4-80.2	73.3	0.9-5.4	2.23	0.45
>0.737	69.23	38.6-90.9	62.5	35.4-84.8	60.0	42.0-75.7	71.4	0.8-5.0	2.03	0.54
>0.749	69.23	38.6-90.9	50.0	24.7-75.3	52.9	37.9-67.4	66.7	0.6-4.2	1.62	0.72
>0.75^*	69.23	38.6-90.9	87.50	61.7-98.4	81.8	53.9-94.5	77.8	1.2-6.6	2.84	0.18
>0.77	69.23	38.6-90.9	43.75	19.8-70.1	50.0	36.3-63.7	63.6	0.5-3.8	1.42	0.81
>0.8	84.62	54.6-98.1	18.75	4.0-45.6	45.8	37.8-54.1	60.0	0.2-6.2	1.22	0.96
>0.9	100	75.3-100.0	6.25	0.2-30.2	46.4	43.3-49.6	100.0	0.2-6.1	1.22	0.94
>0.92	100	75.3-100.0	0	0.0-20.6	44.8	44.8-44.8	100.0	0.2-6.1	1.22	1

^*Optimal Cut-off based on Youden Index J (0.5673) as derived in Fig. 3.

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