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Abstract：
UNASSIGNED: Risk prediction models are commonly performed with logistic regression analysis but are limited by skewed datasets. We utilised neural networks (NNs) model to identify independent predictors of poor outcomes in cerebral venous thrombosis (CVT) due to the limitations of logistic regression (LR) analysis with complex datasets.
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.
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.
摘要：
■风险预测模型通常通过逻辑回归分析来执行，但受到偏斜数据集的限制。由于复杂数据集的逻辑回归（LR）分析的局限性，我们利用神经网络（NN）模型来识别脑静脉血栓形成（CVT）不良结局的独立预测因子。
■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。
■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。
