Medical Faculty, University of Niš, Nis, Serbia.
Acta Neurol Belg. 2012 Dec;112(4):375-82. doi: 10.1007/s13760-012-0093-2. Epub 2012 Jun 7.
Numerous outcome prediction models have been developed for mortality and functional outcome after spontaneous intracerebral haemorrhage (ICH). However, no outcome prediction model for ICH has considered the impact of care restriction. To develop and compare results of the artificial neural networks (ANN) and logistic regression (LR) models, based on initial clinical parameters, for prediction of mortality after spontaneous ICH. Analysis has been conducted on consecutive dataset of patients with spontaneous ICH, over 5-year period in tertiary care academic hospital. Patients older than 18 years were eligible for inclusion if they had been presented within 6 h from the start of symptoms and had evidence of spontaneous supratentorial ICH on initial brain computed tomography within 24 h. Initial clinical parameters have been used to develop LR and ANN prediction models for hospital mortality as outcome measure. Models have been accessed for discrimination and calibration abilities. We have analyzed 411 patients (199 males and 212 females) with spontaneous ICH, medically treated and not withdrawn from therapy, with average age of 67.35 years. From them, 256 (62.29%) patients died during hospital treatment and 155 (37.71%) patients survived. In the observed dataset, ANN model overall correctly classified outcome in 93.55% of patients, compared with 79.32% of correct classification for the LR model. Discrimination and calibration parameters indicate that both models show an adequate fit of expected and observed values, with superiority of ANN model. Our results favour the ANN model for prediction of mortality after spontaneous ICH. Further studies of the strengths and limitations of this method are needed with larger prospective samples.
已经开发出许多用于预测自发性脑出血（ICH）后死亡率和功能结局的预后预测模型。然而，还没有一个 ICH 的预后预测模型考虑到了护理限制的影响。为了开发和比较基于初始临床参数的人工神经网络（ANN）和逻辑回归（LR）模型在预测自发性 ICH 后死亡率方面的结果。在一所三级保健学术医院进行了为期 5 年的连续患者数据集分析。如果患者年龄超过 18 岁，在症状开始后 6 小时内就诊，并在 24 小时内的初始脑计算机断层扫描上有自发性幕上 ICH 的证据，则符合纳入标准。使用初始临床参数开发用于预测医院死亡率的 LR 和 ANN 预测模型作为结局指标。评估了模型的判别能力和校准能力。我们分析了 411 名接受药物治疗且未停止治疗的自发性 ICH 患者（199 名男性和 212 名女性），平均年龄为 67.35 岁。其中，256 名（62.29%）患者在住院期间死亡，155 名（37.71%）患者存活。在观察到的数据集，ANN 模型整体上正确分类了 93.55%的患者结局，而 LR 模型的正确分类率为 79.32%。判别和校准参数表明，两种模型均显示出预期值和观察值的拟合度适中，且 ANN 模型具有优势。我们的结果支持 ANN 模型用于预测自发性 ICH 后的死亡率。需要使用更大的前瞻性样本进一步研究该方法的优势和局限性。
