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一项关于军事作战和训练暴露对退伍军人事务部与服役相关残疾影响的预测模型:一项CENC研究。

A prediction model of military combat and training exposures on VA service-connected disability: a CENC study.

作者信息

Eggleston B, Dismuke-Greer C E, Pogoda T K, Denning J H, Eapen B C, Carlson K F, Bhatnagar S, Nakase-Richardson R, Troyanskaya M, Nolen T, Walker W C

机构信息

RTI International, Research Triangle Park, NC, USA.

Health Economics Resource Center (HERC), VA Palo Alto Healthcare System, Palo Alto, California, USA.

出版信息

Brain Inj. 2019;33(13-14):1602-1614. doi: 10.1080/02699052.2019.1655793. Epub 2019 Sep 2.

DOI:10.1080/02699052.2019.1655793
PMID:31476880
Abstract

: Research has shown that number of and blast-related Traumatic Brain Injuries (TBI) are associated with higher levels of service-connected disability (SCD) among US veterans. This study builds and tests a prediction model of SCD based on combat and training exposures experienced during active military service.: Based on 492 US service member and veteran data collected at four Department of Veterans Affairs (VA) sites, traditional and Machine Learning algorithms were used to identify a best set of predictors and model type for predicting %SCD ≥50, the cut-point that allows for veteran access to 0% co-pay for VA health-care services.: The final model of predicting %SCD ≥50 in veterans revealed that the best blast/injury exposure-related predictors while deployed or non-deployed were: 1) number of controlled detonations experienced, 2) total number of blast exposures (including controlled and uncontrolled), and 3) the total number of uncontrolled blast and impact exposures.: We found that the highest blast/injury exposure predictor of %SCD ≥50 was number of controlled detonations, followed by total blasts, controlled or uncontrolled, and occurring in deployment or non-deployment settings. Further research confirming repetitive controlled blast exposure as a mechanism of chronic brain insult should be considered.

摘要

研究表明,在美国退伍军人中,爆炸相关创伤性脑损伤(TBI)的数量与更高水平的服役相关残疾(SCD)有关。本研究基于现役军事服役期间经历的战斗和训练暴露情况,构建并测试了一个SCD预测模型。

基于在四个退伍军人事务部(VA)站点收集的492名美国现役军人和退伍军人的数据,使用传统算法和机器学习算法来确定预测SCD≥50%的最佳预测指标集和模型类型,该切点允许退伍军人享受VA医疗保健服务0%的自付费用。

预测退伍军人SCD≥50%的最终模型显示,部署或未部署期间与爆炸/损伤暴露相关的最佳预测指标为:1)经历的受控爆炸次数;2)爆炸暴露总数(包括受控和不受控);3)不受控爆炸和撞击暴露总数。

我们发现,SCD≥50%的最高爆炸/损伤暴露预测指标是受控爆炸次数,其次是受控或不受控的总爆炸次数,且发生在部署或未部署环境中。应考虑进一步研究以证实重复性受控爆炸暴露是慢性脑损伤的一种机制。

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