UNASSIGNED: We evaluated 1309 adult CVT patients from the prospective BEAST (Biorepository to Establish the Aetiology of Sinovenous Thrombosis) study. The area under the receiver operating characteristic (AUROC) curve confirmed the goodness-of-fit of prediction models. The normalised importance (NI) of the NNs determines the significance of independent predictors.
UNASSIGNED: The stepwise logistic regression model found thrombolysis (OR 32.1; 95% CI 3.6-287.0; P=0.002), craniotomy (OR 6.9; 95% CI 1.3-36.8; P=0.02), and cerebral haemorrhage (OR 4.5; 95% CI 1.3-15.4; P=0.01) as predictors of poor clinical outcome with the AUROC of 0.71. Conversely, the NNs model identified major independent predictors of long-term poor clinical outcomes as cerebral haemorrhage (NI 100%) and thrombolysis (NI 98%), as well as trivial predictors of age (NI 2.8%) and altered mental status (NI 3.5%). The accuracy of the NNs model was 95.1% and 94.1% for self-learned randomly selected training and testing samples with an AUROC of 0.82. Positive and negative predictive values for poor outcomes were 13.2% and 97.1% for the LR model, compared with the NNs model of 18.8% and 98.7%, respectively.
UNASSIGNED: Cerebral haemorrhage and thrombolysis was a strong independent predictor, whereas age merely impacts the long-term poor clinical outcome in adult CVT. Integrating unorthodox neural networks risk prediction model can improve decision-making as it outperforms conventional logistic regression with complex datasets.
■我们从前瞻性BEAST(建立静脉血栓形成病因的生物栓剂)研究中评估了1309名成年CVT患者。接收器工作特性(AUROC)曲线下的面积证实了预测模型的拟合优度。神经网络的归一化重要性(NI)决定了独立预测因子的重要性。
■逐步逻辑回归模型发现溶栓(OR32.1;95%CI3.6-287.0;P=0.002),开颅手术(OR6.9;95%CI1.3-36.8;P=0.02),和脑出血(OR4.5;95%CI1.3-15.4;P=0.01)作为不良临床结局的预测因子,AUROC为0.71。相反,神经网络模型确定了长期不良临床结局的主要独立预测因素,如脑出血(NI100%)和溶栓(NI98%),以及年龄(NI2.8%)和精神状态改变(NI3.5%)的微不足道的预测因子。在AUROC为0.82的情况下,自学习随机选择的训练和测试样本的NN模型的准确率为95.1%和94.1%。LR模型对不良结局的阳性和阴性预测值分别为13.2%和97.1%,与神经网络模型的18.8%和98.7%相比,分别。
■脑出血和溶栓是一个强有力的独立预测因子,而年龄仅影响成人CVT的长期不良临床结局。集成非正统神经网络风险预测模型可以改善决策,因为它优于传统的复杂数据集的逻辑回归。