Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort.
Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort.
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人体成分的体积标记可能会改善骨盆骨折后主要动脉出血的个性化预测：巴尔的摩CT预测模型队列的次要分析。
DOI：
10.1177/0846537120952508
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发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Chen R
中科院分区：
文献类型：
--
作者：
Dreizin D;Rosales R;Li G;Syed H;Chen R
巴尔的摩骨盆骨折出血的计算机断层扫描（CT）预测模型是一种多变量决策工具，它根据骨盆血肿体积、动脉活动性出血、骨折类型和动脉粥样硬化来预测血管造影阳性情况。我们假设身体成分和虚弱的定量指标可以进一步提高模型的性能。
这项工作是对用于开发巴尔的摩CT预测模型的单一机构队列进行的回顾性二次分析。该队列包括115例因骨盆环断裂的钝性创伤而接受腹部和骨盆入院增强CT检查，随后进行常规血管造影的连续患者。需要血管栓塞的主要动脉损伤作为结果变量。115例患者中有73例（占队列的63%）需要血管栓塞。平均年龄为46.9岁（±标准差20.4）。身体成分测量确定为二维（2D）或三维（3D）参数，包括L3椎体中部小梁骨衰减、腹部内脏脂肪面积或体积以及肌肉脂肪分数百分比（作为肌肉减少症的标志），通过分割和直方图分析进行测量。
纳入身体成分的2D（模型B）或3D标记（模型C）的模型在性能、质量和拟合的所有参数（受试者工作特征曲线下面积[AUC]、赤池信息准则、布里尔分数、霍斯默 - 莱梅肖检验和调整后的R²）方面都比原始的巴尔的摩模型（模型A）有所改进。受试者工作特征曲线下面积从0.83（A）提高到0.86（B）和0.88（C）。3D参数的改进最为显著。
一旦实现自动化，提供“免费”3D身体成分信息的定量可视化工具有望在骨盆骨折出血患者中改善个性化精准诊断、结果预测和决策支持。
The Baltimore computed tomography (CT) prediction model for bleeding pelvic fractures is a multivariable decision tool that predicts angiopositivity from pelvic hematoma volume, active arterial bleeding, fracture patterns, and atherosclerosis. We hypothesized that quantitative markers of body composition and frailty could further improve model performance. This work is a retrospective secondary analysis of a single institution cohort used in the development of the Baltimore CT prediction model. The cohort includes 115 consecutive patients that underwent admission contrast-enhanced CT of the abdomen and pelvis for blunt trauma with pelvic ring disruption followed by conventional angiography. Major arterial injury requiring angioembolization served as the outcome variable. Angioembolization was required in 73/115 patients (63% of the cohort). Average age was 46.9 years (±SD 20.4). Body composition measurements were determined as 2-dimensional (2D) or 3-dimensional (3D) parameters and included mid-L3 trabecular bone attenuation, abdominal visceral fat area or volume, and percent muscle fat fraction (as a marker of sarcopenia) measured using segmentation and histogram analysis. Models incorporating 2D (Model B) or 3D markers (model C) of body composition showed improvement over the original Baltimore model (model A) in all parameters of performance, quality, and fit (area under the receiver-operating curve [AUC], Akaike information criterion, Brier score, Hosmer-Lemeshow test, and adjusted-R2). Area under the receiver-operating curve increased from 0.83 (A), to 0.86 (B), and 0.88 (C). The greatest improvement was seen with 3D parameters. Once automated, quantitative visualization tools providing “free” 3D body composition information can be expected to improve personalized precision diagnostics, outcome prediction, and decision support in patients with bleeding pelvic fractures.
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