Incorporating Immunoproteins in the Development of Classification Models of Progression of Intracranial Hemorrhage After Traumatic Brain Injury.
Incorporating Immunoproteins in the Development of Classification Models of Progression of Intracranial Hemorrhage After Traumatic Brain Injury.
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结合免疫蛋白建立外伤性脑损伤后颅内出血进展的分类模型。
DOI：
10.1097/htr.0000000000000654
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发表时间：
2021-09-01
期刊：
影响因子：
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通讯作者：
McWeeney S
中科院分区：
文献类型：
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作者：
Hinson HE;Li P;Myers L;Agarwal C;Pollock J;McWeeney S
确定临床、影像学和基于血液的生物标志物特征，将其纳入颅内出血进展（PICH）的分类模型，并对这些模型进行初步评估。
从一个前瞻性纳入的创伤性脑损伤受试者队列中确定入院头部计算机断层扫描显示出血的患者。初始和随访图像由2名独立的阅片者解读，不一致的情况进行裁决。分析入院血浆样本，并选择由与关注结果显著相关的免疫蛋白（IPs）组成的主成分（PCs）进行进一步评估。基于（1）临床变量（CV）和（2）临床变量 + 免疫蛋白（CV + IP）构建了一系列逻辑回归模型。估计了这些模型对PICH正确分类的错误率；显著性设定为P <.05。
我们确定了106例患者，其中36%患有PICH。二分类的入院格拉斯哥昏迷评分（P =.004）、马歇尔评分（P =.004）以及3个主成分与PICH显著相关。对于仅含临床变量的模型，敏感度为1.0，特异度为0.29（95%置信区间，0.07 - 0.67）。临床变量 + 免疫蛋白模型表现显著更好，敏感度为0.93（95%置信区间，0.64 - 0.99），特异度为1.0（P =.008）。对PICH定义进行细化调整以及更好地确定PICH的影像学预测因子并没有显著改善模型的性能。
在这项初步研究中，我们观察到免疫蛋白组合在与临床变量结合时可能会改善PICH分类模型。然而，整体模型性能必须进一步优化；研究结果将为后续模型中应包含的特征提供信息。
To define clinical, radiographic, and blood-based biomarker features to be incorporated into a classification model of progression of intracranial hemorrhage (PICH), and to provide a pilot assessment of those models. Patients with hemorrhage on admission head computed tomography were identified from a prospectively enrolled cohort of subjects with traumatic brain injury. Initial and follow-up images were interpreted both by 2 independent readers, and disagreements adjudicated. Admission plasma samples were analyzed and principal components (PCs) composed of the immune proteins (IPs) significantly associated with the outcome of interest were selected for further evaluation. A series of logistic regression models were constructed based on (1) clinical variables (CV) and (2) clinical variables + immune proteins (CV+IP). Error rates of these models for correct classification of PICH were estimated; significance was set at P < .05. We identified 106 patients, 36% had PICH. Dichotomized admission Glasgow Coma Scale (P = .004), Marshall score (P = .004), and 3 PCs were significantly associated with PICH. For the CV only model, sensitivity was 1.0 and specificity was 0.29 (95% CI, 0.07-0.67). The CV+IP model performed significantly better, with a sensitivity of 0.93 (95% CI, 0.64-0.99) and a specificity of 1.0 (P = .008). Adjustments to refine the definition of PICH and better define radiographic predictors of PICH did not significantly improve the models’ performance. In this pilot investigation, we observed that composites of IPs may improve PICH classification models when combined with CVs. However, overall model performance must be further optimized; results will inform feature inclusion included in follow-up models.