Comparison of the Ability to Predict Mortality between the Injury Severity Score and the New Injury Severity Score: A Meta-Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Strategy and Study Selection
2.2. Inclusion and Exclusion Criteria
2.3. Data Extraction and Quality Assessment
2.4. Statistical Analysis
3. Results
3.1. Search Results and Characteristics of Studies
3.2. Test of the Threshold Effect and Heterogeneity
3.3. Overall Analysis
3.4. Meta-Regression and Sensitivity Analyses
3.5. Subgroup Meta-Analysis
4. Discussion
4.1. ISS vs. NISS
4.2. Factors Affecting the Accuracy of the ISS
4.3. Source of Heterogeneity
4.4. Limitations
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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First Author | Country | Sample Size | Mortality | Year | Age (Years) | Male (%) | Tool | Cut-Off Value | TP | FP | FN | TN | AUC | Sen (%) | Spe (%) | Quality |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Chiang [2] | China (Taiwan) | 955 | 0.0450 | 2012 | ≥18 | 59.8 | ISS | 15 | 37 | 169 | 6 | 743 | 0.877 | 85.70 | 81.50 | 11 |
Eftekhar [27] | Iran | 7208 | 0.0380 | 2005 | Mean, 32.5 | 76.0 | ISS | 44 a | 135 | 14 | 139 | 6920 | 0.944 | 49.20 | 99.80 | 10 |
Lefering [26] | Germany | 1206 | 0.1660 | 2009 | Mean, 38.2 | 74.0 | ISS | 40 | 90 | 111 | 110 | 895 | 0.786 | 45.00 | 89.00 | 10 |
NISS | 49 | 98 | 101 | 102 | 905 | 0.804 | 49.00 | 90.00 | ||||||||
Bulut [18] | Turkey | 749 | 0.0360 | 2006 | <14 | 64.0 | ISS | 22 | 24 | 33 | 3 | 689 | 0.962 | 90.50 | 95.40 | 9 |
NISS | 22 | 27 | 82 | 0 | 640 | 0.950 | 100.00 | 88.70 | ||||||||
Woodford [28] | America | 120 | 0.0700 | 2012 | Mean, 42 | 63.0 | ISS | 44 a | 7 | 18 | 1 | 94 | 0.910 | 88.00 | 84.00 | 8 |
Aydin [29] | Turkey | 550 | 0.2160 | 2008 | >16 | 78.0 | ISS | 21 | 106 | 92 | 13 | 339 | 0.907 | 89.10 | 78.70 | 10 |
NISS | 25 | 102 | 76 | 17 | 431 | 0.914 | 85.70 | 82.40 | ||||||||
Turina [24] | Croatia | 43 b | 0.2300 | 2001 | Mean, 30 | 93.0 | ISS | 20 | 10 | 17 | 0 | 16 | 0.750 | 100.00 | 49.00 | 8 |
41 c | 0.3900 | 2001 | Mean, 38 | 90.2 | ISS | 24 | 16 | 11 | 0 | 14 | 0.780 | 100.00 | 56.00 | |||
Schiff [25] | America | 294 | 0.0340 | 2002 | Mean, 27.6 | 0.0 | ISS | 4 | 7 | 108 | 3 | 176 | 0.740 | 70.00 | 62.00 | 9 |
Domingues [13] | Brazil | 533 | 0.2310 | 2011 | Mean, 38 | 80.5 | ISS | 44 a | 97 | 98 | 26 | 312 | 0.900 | 79.00 | 76.00 | 10 |
NISS | 54 a | 101 | 70 | 22 | 340 | 0.920 | 82.00 | 83.00 | ||||||||
Eryllmaz [30] | Turkey | 87 | 0.1034 | 2009 | Mean, 25 | 67.0 | ISS | 31.5 | 9 | 24 | 0 | 54 | 0.910 | 100.00 | 69.20 | 10 |
NISS | 31.5 | 9 | 24 | 0 | 54 | 0.915 | 100.00 | 69.20 | ||||||||
Ahun [31] | Turkey | 100 | 0.1200 | 2014 | Mean, 40.35 | 77.0 | ISS | 16 | 10 | 29 | 2 | 59 | 0.816 | 83.33 | 67.05 | 10 |
Tool | Spearman Correlation Coefficient | p-Value |
---|---|---|
ISS | 0.517 | 0.085 |
NISS | 0.300 | 0.624 |
Tool | Sensitivity (95% CI) | Specificity (95% CI) | PLR (95% CI) | NLR (95% CI) | DOR (95% CI) |
---|---|---|---|---|---|
ISS | 0.64 (0.61–0.68) | 0.93 (0.93–0.94) | 5.11 (3.12–8.37) | 0.27 (0.19–0.40) | 27.75 (9.93–77.53) |
NISS | 0.71 (0.66–0.75) | 0.87 (0.86–0.88) | 5.22 (3.84–7.08) | 0.20 (0.08–0.52) | 24.74 (10.19–60.07) |
Variables | Coefficient | p-Value | RDOR (95% CI) |
---|---|---|---|
Mortality | −4.271 | 0.4823 | 0.01 (0.00–16,071.86) |
Cut-off value | 0.022 | 0.6215 | 1.02 (0.92–1.13) |
Quality | −0.191 | 0.6542 | 0.83 (0.31–2.23) |
Number | 0.001 | 0.0805 | 1.00 (1.00–1.00) |
First Author | Sensitivity | I2 of Sensitivity (%) |
---|---|---|
None a | 0.64 | 93.2 |
Turina [24] b | 0.64 | 93.4 |
Domingues [13] | 0.62 | 93.2 |
Lefering [26] | 0.70 | 91.6 |
Bulut [18] | 0.64 | 93.4 |
Schiff [25] | 0.64 | 93.8 |
Subgroup | I2 of Sensitivity (%) | I2 of Specificity (%) |
---|---|---|
All | 0.64 (93.2) | 0.93 (99.3) |
Mortality < 0.1 | 0.58 (90.0) | 0.96 (99.6) |
Mortality ≥ 0.1 | 0.69 (94.5) | 0.82 (93.7) |
Sample size < 100 | 1.00 (0.0) | 0.62 (56.8) |
Sample size ≥ 100 | 0.63 (93.8) | 0.94 (99.5) |
Cut-off value < 44 | 0.69 (93.1) | 0.83 (97.0) |
Cut-off value ≥ 44 | 0.59 (94.2) | 0.98 (99.6) |
Developed country | 0.47 (71.6) | 0.83 (98.0) |
Developing country | 0.70 (93.3) | 0.95 (99.4) |
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Deng, Q.; Tang, B.; Xue, C.; Liu, Y.; Liu, X.; Lv, Y.; Zhang, L. Comparison of the Ability to Predict Mortality between the Injury Severity Score and the New Injury Severity Score: A Meta-Analysis. Int. J. Environ. Res. Public Health 2016, 13, 825. https://doi.org/10.3390/ijerph13080825
Deng Q, Tang B, Xue C, Liu Y, Liu X, Lv Y, Zhang L. Comparison of the Ability to Predict Mortality between the Injury Severity Score and the New Injury Severity Score: A Meta-Analysis. International Journal of Environmental Research and Public Health. 2016; 13(8):825. https://doi.org/10.3390/ijerph13080825
Chicago/Turabian StyleDeng, Qiangyu, Bihan Tang, Chen Xue, Yuan Liu, Xu Liu, Yipeng Lv, and Lulu Zhang. 2016. "Comparison of the Ability to Predict Mortality between the Injury Severity Score and the New Injury Severity Score: A Meta-Analysis" International Journal of Environmental Research and Public Health 13, no. 8: 825. https://doi.org/10.3390/ijerph13080825