Validation of the Shock Index, Modified Shock Index, and Age Shock Index for Predicting Mortality of Geriatric Trauma Patients in Emergency Departments

The elderly population is rapidly growing, and traumatic injury of geriatric individuals is a significant problem for the health care systems of most advanced countries (1). Elderly patients experience traumatic injuries as drivers or passengers in motor vehicles, as pedestrians being struck by motor vehicles, by falling from a height, and from crushing (2, 3, 4). Elderly trauma patients usually have co-morbidities so the complications and the long-term mortality of traumatic injury is greater for elderly individuals than for young individuals (5).

Previous studies have used several methodologies to assess the severity and predict the mortality of patients with traumatic injuries. However, many of these scoring tools are inconvenient for initial use in an emergency department (ED) because the calculations are complex or because detailed clinical and laboratory information is required (6, 7). The shock index (SI), calculated as heart rate (HR) divided by systolic blood pressure (SBP), is a measure of hemodynamic stability that is useful in predicting mortality and injury severity in trauma patients (8, 9, 10, 11, 12). The SI is superior to heart rate and systolic blood pressure alone in predicting mortality in geriatric trauma patients (13, 14).

The SI is easy to calculate, but its accuracy for geriatric populations is controversial. Previous research suggested that SI multiplied by age (Age SI) is a better predictor of mortality following traumatic injury of an elderly patient (15). Another investigator proposed use of the modified shock index (MSI), the ratio of heart rate to mean blood pressure, as a more accurate predictor than systolic blood pressure, heart rate, and SI (16, 17). However SI, MSI, and Age SI were developed and validated for different populations (11, 12, 13). In the present study, we assessed the predictive power of the SI, MSI, and Age SI in geriatric patients using a single large nationwide trauma database.

The aim of this study was to validate the power of the SI, MSI, and Age SI in prediction of mortality in geriatric trauma patients.

Fig. 1 shows the procedure used to select geriatric trauma patients. During the study period, 1,179,175 trauma cases were registered in the EDIIS database and 111,431 (9.4%) of these cases were geriatric patients. Based on our inclusion criteria, we ultimately enrolled 45,880 cases for analysis (Fig. 1).

Fig. 1
Criteria used to select the study population of geriatric patients with traumatic injuries.

ER = emergency room.

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Table 1 compares the demographics and injury epidemiology of survivors and non-survivors. Segregation of patients into 3 age groups (> 85, 75–84, and 65–74 years-old) indicated significantly greater mortality in patients who were older. Cases who had traffic accidents and who used an EMS were more likely to have died, and the time from injury to ER arrival was longer in cases who died.

Table 1
Demographics and injury epidemiology of survivors and non-survivors

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We also assessed the clinical characteristics of survivors and non-survivors (Table 2). The results show the non-survivors had lower SBP, lower DBP, higher heart rate, poorer mental status, and were more likely to be admitted to the ED, given an operation, and admitted to the ICU.

Table 2
Clinical parameters and ED disposition of survivors and non-survivors

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We determined the median values of each index for survivors and non-survivors (Table 3). The results of in-hospital group indicate the non-survivors had a greater median SI (0.84 vs. 0.57, P < 0.001), MSI (1.14 vs. 0.79, P < 0.001), and Age SI (64.0 vs. 41.5, P < 0.001), which of ED group indicate the non-survivors had a greater SI (1.05 vs. 0.57, P < 0.001), MSI (1.40 vs. 0.79, P < 0.001), and Age SI (80.0 vs. 41.5, P < 0.001). We also compared percentage of hemodynamically unstable cases defined by each system among survivors and non-survivors. The percentage of cases classified as unstable were significantly more likely to be non-survivors according to the SI (36.6% vs. 1.8% of in-hospital group, 56.1% vs. 0.9% of ED group), the MSI (38.6% vs. 2.2% of in-hospital group, 58.2% vs. 1.2% of ED group) and the Age SI (69.4% vs. 21.3% of in-hospital group, 83.1% vs. 18.8% of ED group) (Table 3).

Table 3
Percentage of survivors and non-survivors who were classified as stable and unstable according to the SI, MSI, and Age SI

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Finally, we compared the AUROC of each index for prediction of in-hospital and ED mortality (Table 4, Fig. 2). Age SI showed higher predictive power for in-hospital mortality than SI (Binary model: 0.740 vs. 0.674, P < 0.001, Continuous model: 0.808 vs. 0.786, P < 0.001). Age SI also showed higher power than MSI (Binary model: 0.740 vs. 0.682, P < 0.001, Continuous model: 0.808 vs. 0.786, P < 0.001). For ED mortality, Age SI showed better prediction than SI (Binary model: 0.807 vs. 0.771, P = 0.024, Continuous model: 0.890 vs. 0.880, P = 0.039).

Table 4
Predictive power of the SI, MSI, and Age SI for in-hospital mortality and ED mortality based on a binary model and a continuous model

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Fig. 2
Area under the receiver operating characteristic (AUROC) curve for in-hospital mortality based on the SI (A and B), MSI (C and D), and Age SI (E and F) for a binary model (A,C, and E) and a continuous model (B,D, and F).

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In the present study, we validated the SI, MSI and Age SI in predicting the mortality of geriatric trauma patients using a single nationwide injury surveillance system from 20 tertiary EDs across Korea. All of the indexes had higher values for non-survivors than survivors. The percentage of unstable patients who died was 36.6% based on the SI, 38.6% based on the MSI, and 69.4% based on the Age SI. The AUROC curve for in-hospital mortality was 0.674 for the SI, 0.682 for the MSI, and 0.740 for the Age SI. Predictive power for in-hospital mortality of Age SI in both models was higher than SI or MSI. Previous studies developed or validated these indexes for different study populations and used different definitions of “elderly” or “geriatric” (13, 15, 19, 20, 21). Our investigation validated each parameter using a single trauma database and we defined “geriatric” as being older than 65 years.

To analyze the prediction of early mortality, we analyzed the ED patients except for hospitalized patients. The AUROC curve for ED mortality was 0.771 for the SI, 0.779 for the MSI, and 0.807 for the Age SI in binary model, which were higher than the AUROC curve for in-hospital patients (Table 4). We estimated that the SI, MSI, Age SI were more effective in the early mortality prediction.

We determined the percentage of hemodynamically unstable patients among survivors and non-survivors based on cut-off values for each index that were used in previous studies (13, 15, 17, 22). Among the 45,880 enrolled cases, 2.2% of cases were unstable defined by an SI of 1 or more and 2.7% of cases were unstable defined by an MSI of 1.3; but 21.9% of cases were unstable defined by an Age SI of 50 or more. Thus, for patients older than 65 years, use of the Age SI cut-off value of 50 overestimated severity of the trauma. In other words, in very elderly geriatric patients, the age component in the Age SI formula led to a large increase in the number of patients classified as hemodynamically unstable. We calculated the AUROC of the Age SI according to the age group. The AUROC of Age SI predicting ED mortality was 0.816 (95% CI, 0.773–0.860) for age from 65 to 74, and 0.779 (95% CI, 0.738–0.821) for age from 75 to 84, and 0.744 (95% CI, 0.707–0.782) for age over 85 in binary model. In continuous model, the AUROC of Age SI for each 65–74, 85–84 and 85– aged group was 0.876 (0.824–0.927), 0.882 (0.828–0.926) and 0.909 (0.857–0.962), respectively.

We also performed sensitivity analyses in predicting mortality for each index. In these analyses, the SI ranged from 0.1 to 2.0 (by 0.1 unit), the MSI ranged from 0.4 to 2.3 (by 0.1 unit), and the Age SI ranged from 41 to 60 (by 1.0 unit) (Table 5). If we consider the sum of sensitivity and specificity to indicate the best model, then the SI was maximized with a cut-off at 0.7 (sensitivity, 70.0%; specificity, 73.6%), the MSI with a cut-off at 0.8 (sensitivity, 55.9%; specificity, 90.9%), and the Age SI with a cut-off at 49 (sensitivity, 73.0%; specificity, 74.9%). If we consider mean sensitivity and specificity, then the cut-off values are 0.8 for the SI, 0.9 for the MSI, and 55 for the Age SI. Comparing the cut-off value of each index using the same methodology of previous research such as sum or mean value of sensitivity and specificity, there was a difference of the value between our study and previous research (13, 14, 15, 17). The difference could be observed due to the difference of study population or different inclusion criteria of the database used for each study.

Table 5
Sensitivity analysis of SI, MSI, and Age SI for predicting in-hospital mortality

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This study had several limitations. First, this was a retrospective analysis. Second, we did not measure exact time profile from injury occurrence to mortality. Shock index is effective to predict short term mortality. But predictive power of shock index for long term mortality is controversial. To conduct robust assessment of predictive power of shock index, measuring time from injury to mortality is required. But the database used in the study did not collect time profile of mortality. Instead of exact time profile, we assessed predictive power of shock index for mortality during ED stay and total in-hospital period, respectively. Third, we could not assess the effect of anti-hypertensive drug medication (such as beta blockers) on the validity of the SI because the EDIIS did have this information. To overcome this limitation, collection of information about drug use was required, but in the emergency clinical settings where geriatric trauma patients are managed, the SI was used regardless of whether information about medication use was available.

In conclusion, we assessed the statistical power of the SI, MSI, and Age SI for predicting the mortality of geriatric trauma patients using a large nationwide database. As expected, each index classified more non-survivors than survivors as hemodynamically unstable. The AUROC curve for predicting mortality was 0.674 for the SI, 0.682 for the MSI, and 0.740 for the Age SI in binary models. The Age SI showed better predictive power of in-hospital mortality than SI or MSI in geriatric trauma patients visited emergency departments.