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韩国采用更新后的生存风险比率的创伤死亡率预测模型比较

Comparison of Trauma Mortality Prediction Models With Updated Survival Risk Ratios in Korea.

作者信息

Kim Juyoung, Heo Yun Jung, Kim Yoon

机构信息

Division of Trauma Surgery, Department of Surgery, Ajou University School of Medicine, Suwon, Korea.

Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, Korea.

出版信息

J Korean Med Sci. 2025 Apr 21;40(15):e51. doi: 10.3346/jkms.2025.40.e51.

Abstract

BACKGROUND

Despite the considerable disease burden due to trauma injury, sufficient effort has not been made for the assessment of nationwide trauma care status in Korea. We explored the feasibility of a diagnosis code-based injury severity measuring method in light of its real-world usage.

METHODS

We used datasets from the National Emergency Department Information System to calculate the survival risk ratios (SRRs) and the Korean Trauma Data Bank to predict models, respectively. The target cohort was split into training and validation datasets using stratified random sampling in an 8:2 ratio. We established six major mortality prediction models depending on the included parameters: 1) the Trauma and Injury Severity Score (TRISS) (age, sex, original Revised Trauma Score [RTS], Injury Severity Score [ISS]), 2) extended International Classification of Diseases-based Injury Severity Score (ICISS) 1 (age, sex, original RTS, ICISS using international SRRs), 3) extended ICISS 2 (age, sex, original RTS, ICISS using Korean SRRs based on 4-digit diagnosis codes), 4) extended ICISS 3 (age, sex, original RTS, ICISS using Korean SRRs based on full-digit diagnosis codes), 5) extended ICISS 4 (age, sex, modified RTS, and ICISS using Korean SRRs based on 4-digit diagnosis codes), 6) extended ICISS 5 (age, sex, modified RTS, and ICISS using Korean SRRs based on full-digit diagnosis codes). We estimated the model using training datasets and fitted it to the validation datasets. We measured the area under the receiver operating characteristic curve (AUC) for discriminative ability. Overall performance was also evaluated using the Brier score.

RESULTS

We observed the feasibility of the extended ICISS models, though their performance was slightly lower than the TRISS model (training cohort, AUC 0.936-0.938 vs. 0.949). Regarding SRR calculation methods, we did not find statistically significant differences. The alternative use of the Alert, Voice, Pain, Unresponsive Scale instead of the Glasgow Coma Scale in the RTS calculation did not degrade model performance.

CONCLUSION

The availability of the practical ICISS model was observed based on the model performance. We expect our ICISS model to contribute to strengthening the Korean Trauma Care System by utilizing mortality prediction and severity classification.

摘要

背景

尽管创伤损伤造成了相当大的疾病负担,但韩国在评估全国创伤护理状况方面仍未做出足够努力。我们根据其实际应用情况探讨了基于诊断编码的损伤严重程度测量方法的可行性。

方法

我们分别使用国家急诊科信息系统的数据集来计算生存风险比(SRR),并使用韩国创伤数据库来预测模型。目标队列采用分层随机抽样以8:2的比例分为训练数据集和验证数据集。我们根据纳入的参数建立了六个主要的死亡率预测模型:1)创伤和损伤严重程度评分(TRISS)(年龄、性别、原始修订创伤评分[RTS]、损伤严重程度评分[ISS]),2)基于国际疾病分类扩展的损伤严重程度评分(ICISS)1(年龄、性别、原始RTS、使用国际SRR的ICISS),3)扩展ICISS 2(年龄、性别、原始RTS、使用基于4位诊断编码的韩国SRR的ICISS),4)扩展ICISS 3(年龄、性别、原始RTS、使用基于完整诊断编码的韩国SRR的ICISS),5)扩展ICISS 4(年龄、性别、改良RTS、使用基于4位诊断编码的韩国SRR的ICISS),6)扩展ICISS 5(年龄、性别、改良RTS、使用基于完整诊断编码的韩国SRR的ICISS)。我们使用训练数据集估计模型,并将其应用于验证数据集。我们测量了用于判别能力的受试者操作特征曲线(AUC)下的面积。还使用Brier评分评估整体性能。

结果

我们观察到扩展ICISS模型的可行性,尽管其性能略低于TRISS模型(训练队列,AUC 0.936 - 0.938对0.949)。关于SRR计算方法,我们未发现统计学上的显著差异。在RTS计算中使用警觉、语音、疼痛、无反应量表替代格拉斯哥昏迷量表并未降低模型性能。

结论

基于模型性能观察到实用ICISS模型的可用性。我们期望我们的ICISS模型通过利用死亡率预测和严重程度分类为加强韩国创伤护理系统做出贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0294/12011615/510e08c83e74/jkms-40-e51-g001.jpg

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