The development of simple survival prediction models for blunt trauma victims treated at Asian emergency centers
- Akio Kimura^{1}Email author,
- Shinji Nakahara^{2} and
- Witaya Chadbunchachai^{3}
DOI: 10.1186/1757-7241-20-9
© Kimura et al; licensee BioMed Central Ltd. 2012
Received: 27 August 2011
Accepted: 2 February 2012
Published: 2 February 2012
Abstract
Background
For real-time assessment of the probability of survival (Ps) of blunt trauma victims at emergency centers, this study aimed to establish regression models for estimating Ps using simplified coefficients.
Methods
The data of 10,210 blunt trauma patients not missing both the binary outcome data about survival and the data necessary for Ps calculation by The Trauma and Injury Severity Score (TRISS) method were extracted from the Japan Trauma Data Bank (2004-2007) and analyzed. Half (5,113) of the data was allocated to a derivation data set, with the other half (5,097) allocated to a validation data set. The data of 6,407 blunt trauma victims from the trauma registry of Khon Kaen Regional Hospital in Thailand were analyzed for validation. The logistic regression models included age, the Injury Severity Score (ISS), the Glasgow Coma Scale score (GCS), systolic blood pressure (SBP), respiratory rate (RR), and their coded values (cAGE, 0-1; cISS, 0-4; cSBP, 0-4; cGCS, 0-4; cRR, 0-4) as predictor variables. The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if the coefficients varied across 0.5. The area under the receiver-operating characteristic curve (AUROCC) was calculated for each model to measure discriminant ability.
Results
A group of formulas (log (Ps/1-Ps) = logit (Ps) = -9 + cISS - cAGE + cSBP + cGCS + cRR/2, where -9 becomes -7 if the predictor variable of cRR or cISS is missing) was developed. Using these formulas, the AUROCCs were between 0.950 and 0.964. When these models were applied to the Khon Kean data, their AUROCCs were greater than 0.91. Conclusion: These equations allow physicians to perform real-time assessments of survival by easy mental calculations at Asian emergency centers, which are overcrowded with blunt injury victims of traffic accidents.
Background
Collecting all of this information and performing such complex calculations are not feasible in the clinical setting at emergency centers. For clinicians, especially emergency physicians, it is hoped that a way to predict survival of trauma victims more easily without a significant decrease in accuracy could be developed.
This study aimed to establish regression models for quick assessment of Ps for blunt trauma (BT) victims based on simplified coefficients that could be used even when the variable of RR or the variable of ISS is missing. The former is frequently missing in the trauma registry data of Japan [5], and the latter is rarely determined during the early stage of trauma management in most cases.
Methods
Study design, population, and settings
A retrospective observational study was conducted to create Ps prediction models with simple coefficients for BT victims in Japan and Thailand.
Once approval was obtained from the trauma registry committee of the Japanese Association for the Surgery of Trauma, deidentified, anonymous data from the Japan Trauma Data Bank (JTDB), with which 144 Japanese hospitals have been involved since 2004, were used [6].
Data (10,210) that were not missing both outcome data about survival and the predictors necessary for Ps calculation by the TRISS method were collected from BT patients (17,564) registered in the JTDB from 2004 to 2007. Half (5,113) of the data was randomly allocated to a derivation data set, with the remaining half (5,097) allocated to a validation data set.
For international validation, with the permission of the hospital, the proposed equations were applied to 6,409 of 6,667 BT patients injured in the Khon Kaen District in Thailand between January 2005 and December 2008 and collected in the Khon Kaen Regional Hospital Trauma Registry, where data have been collected since 1997. This hospital is one of the World Health Organization (WHO) collaborating centers for injury prevention and safety promotion. The data of two patients were excluded because they were erroneous.
Coded Values
Coded value | Glasgow coma scale score | Systolic blood pressure (mmHg) | Respiratory rate (/min) | Age (years) | Injury Severity Score |
---|---|---|---|---|---|
4 | 13-15 | > 89 | 10-29 | < 16 | |
3 | 9-12 | 76-89 | > 29 | 16-24 | |
2 | 6-8 | 50-75 | 6-9 | 25-40 | |
1 | 4-5 | 1-49 | 1-5 | ≥ 55 | 41-65 |
0 | < 4 | No pulse | 0 | 0-54 | > 65 |
Analyses
Logistic regression analyses were applied to establish the models. The maximum likelihood estimation was used as the method of coefficient estimation for each model.
For model selection, Akaike's Information Criterion (AIC) [9], - 2log (maximum likelihood) +2 (number of adjusted parameters), was used. The model having the lower AIC is considered to be better fitting. The area under the receiver-operating characteristic curve (AUROCC), which distinguishes between survival and non-survival, and varies between 0.5 (= no discrimination) and 1 (= perfect discrimination), of each model was also measured.
The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if it was nearer to the coefficients than 0 or 1.
The JMP 9.0 (SAS Institute Inc.) and SAS 9.1 (SAS Institute Inc.) software packages were used for statistical analyses.
The protocol of the present study was approved by the Ethics Committee of the National Center for Global Health and Medicine.
Results
Distribution of Variables
Derivation Data | Validation Data | Khon Kaen Data | ||
---|---|---|---|---|
Number | 5113 | 5097 | 6407 | |
cAGE | 0 | 58.0% | 58.5% | 87.6% |
1 | 42.0% | 41.5% | 12.4% | |
RTS | 7.8 [6.9, 7.8] | 7.8 [6.9, 7.8] | 7.8 [7.8, 7.8] | |
cSBP | 4 | 85.0% | 85.3% | 95.9% |
3 | 3.2% | 3.4% | 1.6% | |
2 | 2.6% | 2.6% | 1.1% | |
1 | 1.3% | 1.1% | 0.2% | |
0 | 7.9% | 7.6% | 1.2% | |
cGCS | 4 | 72.4% | 73.4% | 89.4% |
3 | 7.5% | 7.0% | 3.0% | |
2 | 6.1% | 5.9% | 4.6% | |
1 | 2.6% | 2.6% | 1.1% | |
0 | 11.4% | 11.1% | 1.9% | |
cRR | 4 | 76.0% | 76.8% | 92.1% |
3 | 15.0% | 14.8% | 0.2% | |
2 | 0.4% | 0.4% | 0.02% | |
1 | 0.2% | 0.1% | 0.2% | |
0 | 8.4% | 7.9% | 7.5% | |
ISS | 17.6 ± 14.2 | 17.4 ± 14.0 | 9.5 ± 10.1 | |
cISS | 4 | 51.1% | 51.0% | 83.2% |
3 | 21.2% | 22.3% | 6.9% | |
2 | 20.4% | 19.7% | 7.4% | |
1 | 5.3% | 5.2% | 2.3% | |
0 | 2.0% | 1.8% | 0.2% | |
Survival | 82.1% | 83.1% | 95.9% |
Akaike's Information Criterion (AIC) and Discriminant Abilities for Each Model
Predictor variables used for each regression model | AIC | AUROCC |
---|---|---|
ISS, RTS, cAGE (original TRISS) | 1988 | 0.9627 |
ISS, RTS, cAGE | 1788 | 0.9637 |
ISS, cAGE, cSBP, cGCS, cRR | 1791 | 0.9637 |
cISS, cAGE, cSBP, cGCS, cRR | 1732 | 0.9648 |
cISS, cAGE, cSBP, cGCS | 1748 | 0.9649 |
cISS, cAGE, cGCS, cRR | 1819 | 0.9609 |
cISS, cSBP, cGCS, cRR | 1846 | 0.9561 |
cISS, cSBP, cGCS, | 1854 | 0.9562 |
cAGE, cSBP, cGCS, cRR | 1987 | 0.9503 |
cAGE, cSBP, cGCS | 2000 | 0.9465 |
cISS, cAGE, cGCS | 2001 | 0.9547 |
cISS, cAGE, cSBP, cRR | 2017 | 0.9481 |
cISS, cAGE, cBP | 2024 | 0.9433 |
Coefficients of Logistic Regression Models
Models with predictor variables | Intercept | β(c)ISS | βRTS | βcAGE | βcGCS | βcBP | βcRR |
---|---|---|---|---|---|---|---|
Original TRISS | -0.4499 | -0.0835 | 0.8085 | -1.743 | 0.7574 | 0.5923 | 0.2351 |
ISS, RTS, cAGE | -1.7162* (0.279) [37.8] | -0.0675* (0.005) [181] | 0.9301* (0.0368) [639] | -1.439* (0.137) [111] | * | * | * |
ISS, cAGE, cSBP, cGCS, cRR | -1.8646 (0.340) [30.2] | -0.0678* (0.0050) [181] | * | - 1.452* (0.137) [112] | 0.846* (0.047) [328] | 0.670* (0.077) [75.8] | 0.346* (0.090) [14.9] |
cISS, cAGE, cSBP, cGCS, cRR | -6.281* (0.335) [351] | 1.0 58* (0.070) [227] | * | -1.4 04* (0.137) [104] | 0.7 77* (0.047) [267] | 0.7 18* (0.077) [87.5] | 0.3 70* (0.090) [17.0] |
cISS, cAGE, cSBP, cGCS | -5.734* (0.283) [410] | 1.0 38* (0.069) [225] | * | -1.3 48* (0.136) [98.8] | 0.8 41* (0.045) [345] | 0.8 89* (0.063) [202] | x |
cAGE, cSBP, cGCS, cRR | -4.663* (0.357) [170] | x | * | -1.3 28* (0.129) [105] | 1.0 25* (0.044) [540] | 0.8 43* (0.077) [122] | 0.3 49* (0.094) [13.7] |
The coefficients of cISS, cAGE, cSBP, and cGCS in Table 4 were rounded off after the decimal point, and the coefficient of cRR was regarded as 0.5.
β value and Actual Survival Percentage in the Derivation Data
β | cISS-cAGE+cSBP+cGCS+cRR/2 Survival (%) | cISS-cAGE+cSBP+cGCS Survival (%) | -cAGE+cSBP+cGCS+cRR/2 Survival (%) |
---|---|---|---|
-1 | 0.0 | 0.0 | 0.0 |
0 | 0.0 | 0.0 | 0.0 |
1 | 0.0 | 0.0 | 0.0 |
2 | 0.0 | 0.0 | 0.0 |
3 | 1.7 | 11.8 | 30.0 |
4 | 14.0 | 19.7 | 28.0 |
5 | 30.0 | 26.8 | 33.8 |
6 | 32.4 | 40.3 | 47.6 |
7 | 28.8 | 51.5 | 66.5 |
8 | 38.3 | 75.3 | 82.7 |
9 | 58.3 | 88.5 | 96.5 |
10 | 78.7 | 96.7 | 99.5 |
11 | 91.4 | 98.6 | |
12 | 96.1 | 99.9 | |
13 | 98.5 | ||
14 | 99.9 |
Proposed Regression Models with Simplified Coefficients
Logit (Ps) of each model | AUROCC JTDB derivation data | AUROCC JTDB validation data | AUROCC Khon Kaen registry data |
---|---|---|---|
-9 + cISS - cAGE + cSBP + cGCS + cRR/2 | 0.9635 | 0.9640 | 0.9619 |
-7 + cISS - cAGE + cSBP + cGCS | 0.9633 | 0.9622 | 0.9601 |
-7 + cAGE + cSBP + cGCS + cRR/2 | 0.9503 | 0.9524 | 0.9115 |
The AUROCCs of each model, applied to the data of the Khon Kaen Trauma Center in Thailand, are also shown in Table 6. The two models with cISS, cAGE, cSBP, cGCS as predictor variables showed AUROCCs greater than 0.96, almost the same as that of the TRISS model. For the model without cISS, the AUROCC was even greater than 0.91.
Discussion
The TRISS [1, 2] method is the most popular method of survival estimation. However, it is not a suitable tool for quick assessment of survival probability in the clinical setting, because it requires complicated calculations using the ISS, for which precise coding of the Abbreviated Injury Scale (AIS) [10] is required, and the RTS, which also has complex coefficients for cGCS, cSBP, and cRR. Therefore, we tried to simplify Ps prediction without a significant decrease in accuracy for clinical rather than for administrative uses. Without substantial loss in the AUROCC compared with the original TRISS, the present study showed that logit (Ps) can be obtained even with a marked simplification of variables and intercepts (Table 6), sufficient to enable its mental calculation. In any case where logit (Ps) is greater than 0, by easy mental calculation it provides for quick determination as to whether the Ps is greater than 0.5, which is considered the lower limit for decision making about unexpected trauma death.
Relationship between Coded ISS and Most Severe Abbreviated Injury Scale (AIS)
Coded ISS | ISS Interval | Most severe AIS/2^{nd} most severe AIS Included |
---|---|---|
4 | < 16 | 3 |
3 | 16-24 | 4 |
2 | 25-40 | 5 or 4/3 |
1 | 41-65 | Two 5 or 5/4 |
0 | > 65 | Two 5/4 or Three 5 or 6 |
Probability of Survival (Ps) Chart
b1~3 | < -3 | -2.5 | -2 | -1.5 | -1 | -0.5 | 0 | 0.5 | 1 | 1.5 | 2 | 2.5 | > 3 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ps | < 0.05 | 0.08 | 0.12 | 0.18 | 0.27 | 0.38 | 0.5 | 0.62 | 0.73 | 0.82 | 0.88 | 0.92 | 0.95 |
In Japan, RR information is frequently deficient [5], but with the regression equation presented in this study, even without information on cRR, only a slight decline in AUROCC is seen. As shown in Table 4 cRR had the lowest chi-square value in each model. This indicates that, based on their experience, Japanese surgeons or emergency physicians appear to have realized that RR is a less important indicator for survival in BT patients. From the results of Table 6 RR also seems to be unimportant in Thailand, because the models without cRR showed only a slight decline in the AUROCC. The deficiency that most increases the AIC and reduces the AUROCC is cGCS (Table 3), which had the highest chi-square value in each model (Table 4). Thus, the level of consciousness is the most important factor at the time of survival prediction. The importance of information on consciousness level was also proven in a different way in our recent paper [12].
The present study had a few limitations that might have biased the results. Because of missing data related to survival and the predictors, the Ps calculation of the TRISS could be done in only 58% of 17,564 BT patients registered in the JTDB. Tohira et al. [13] pointed out that significant differences existed in age, RTS, and ISS between outcome-missing data and non-outcome-missing data, and that selection bias may exist in research outputs gained from the extracted data from the JTDB by excluding patients with missing outcomes and the TRISS predictors. Thus, it seems to be of great worth to validate the developed models with the Khon Kaen trauma registry data, which are substantially different from the JTDB data in age, RTS, and ISS. At present, the simplified models in this study have been validated only with the Japan Trauma Registry data and the Khon Kaen Trauma Registry data in Thailand. If the results of the present study can be verified with other data from around the world, especially from middle-income countries where traffic injuries are rapidly increasing [14], it will be of greater use internationally.
Conclusion
The proposed, simplified equations allow quick assessments of Ps by simple mental calculation, which should prove useful at Asian emergency centers overcrowded with traffic accident victims suffering from blunt trauma.
Declarations
Acknowledgements
The authors would like to thank all of the people who have been involved in the trauma registry in Japan and those who have been engaged in the trauma registry at the Trauma and Critical Care Center of Khon Kaen Regional Hospital in Thailand
This work was supported by a Health and Labor Sciences Research Grant for Research on Global Health Issues from the Ministry of Health, Labor and Welfare of Japan.
This work was presented at 6^{th} Asian Conference of Emergency Medicine, Bangkok, July 2011.
Authors’ Affiliations
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