Iosa Marco, Morone Giovanni, Antonucci Gabriella, Paolucci Stefano
Department of Psychology, Sapienza University of Rome, 00185 Roma, Italy.
IRCCS Fondazione Santa Lucia, 00179 Roma, Italy.
Brain Sci. 2021 Aug 29;11(9):1147. doi: 10.3390/brainsci11091147.
There is a large body of literature reporting the prognostic factors for a positive outcome of neurorehabilitation performed in the subacute phase of stroke. Despite the recent development of algorithms based on neural networks or cluster analysis for the identification of these prognostic factors, the literature lacks a rigorous comparison among classical regression, neural network, and cluster analysis. Moreover, the three methods have rarely been tested on a sample independent from that in which prognostic factors have been identified. This study aims at providing this comparison on a wide sample of data (1522 patients) and testing the results on an independent sample (1000 patients) using 30 variables. The accuracy was similar among regression, neural network, and cluster analyses on the analyzed sample (76.6%, 74%, and 76.1%, respectively), but on the test sample, the accuracy of neural network decreased (70.1%). The three models agreed in identifying older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The binary regression analysis also provided solid results in the test sample, especially in terms of specificity (81.8%). Cluster analysis also showed a high sensitivity in the test sample (82.6%) and allowed a meaningful easy-to-use classification tree to be obtained.
有大量文献报道了中风亚急性期进行神经康复积极预后的相关因素。尽管最近基于神经网络或聚类分析开发了算法来识别这些预后因素，但文献中缺乏对经典回归、神经网络和聚类分析的严格比较。此外，这三种方法很少在与已识别预后因素的样本无关的样本上进行测试。本研究旨在对大量数据样本（1522例患者）进行这种比较，并使用30个变量在独立样本（1000例患者）上测试结果。在分析样本上，回归分析、神经网络分析和聚类分析的准确率相似（分别为76.6%、74%和76.1%），但在测试样本上，神经网络的准确率下降（70.1%）。这三种模型都一致认为年龄较大、严重损伤、单侧空间忽视和完全前循环梗死是重要的预后因素。二元回归分析在测试样本中也给出了可靠的结果，尤其是在特异性方面（81.8%）。聚类分析在测试样本中也显示出高灵敏度（82.6%），并得到了一个有意义且易于使用的分类树。
