基于病证结合的中风后抑郁中医证候量化诊断模型研究

Post apoplectic depression is a complex emotional mental disorder that occurs after a stroke. At present, the diagnosis of post-stroke depression syndrome is mostly focused on the diagnosis of poststroke depression, and has ignored the same syndrome in different diseases. There are many qualitative judgments of syndrome diagnosis, need to solve the expert experience, the lack of uniformity and objectivity, weight and shape, and the syndrome diagnosis model there are no specific syndromes and disease in time, syndrome diagnosis value, lack of objective quantitative standard, progress of changing and evolution of neglect the post stroke depression occurrence, development and prognosis of the syndrome law. Therefore, we need to explore the unified and objective quantitative diagnostic criteria of specific syndromes in different stages of post stroke depression, and clarify the evolution rule of depression after stroke. This study is based on the idea of combination of disease and syndrome, using BP artificial neural network, the syndrome factors of depression after stroke in different course under the premise of clear disease under the extraction, analysis of main syndromes of depression after stroke in the different course type and evolution model of depression syndromes quantitative diagnosis of the establishment of a unified,.guest the concept of depression after stroke, so as to promote the research of TCM syndrome diagnosis standard of stroke, improve the level of clinical diagnosis and treatment.

中风后抑郁是发生在中风后的一种复杂情感性精神障碍性疾病。目前，中医对于中风后抑郁证候的诊断多着眼于中风后抑郁横断面上证候和证型的诊断，忽视了相同证候在不同疾病中受到不同病理变化和病情演变规律的制约和影响。证候诊断中存在许多定性判断，需要依靠专家经验来解决，缺乏统一性和客观性，以往的证候诊断模型没有明确特定证候的主状、次状、兼症在证候诊断中的权重值，缺少客观的量化标准，忽视了中风后抑郁发生、发展、转归过程中证候的不断变化及演变进展规律。因此，探讨中风后抑郁不同病程下特定证候的统一、客观的量化诊断标准，阐明中风后抑郁证候演变进展规律是我们亟需解决的问题。本研究拟基于病证结合的思想，在明确疾病的前提下对中风后抑郁不同病程下的证候要素进行提取，分析不同病程下中风后抑郁的主要证候分型及演变规律，建立统一、客观的中风后抑郁中医证候量化诊断模型，从而推动中风后抑郁中医证候诊断的标准化研究。

中风后抑郁（Post-Stroke Depression，PSD）的发病率大概介于30%至50%之间，是一种高发生率的中风病并发症 。大量研究表明，中医治疗对PSD有着很好的疗效，且副作用小。诊断准确是治疗取得疗效的关键，整体观念和辨证论治是中医学特色，“证”是中医诊断疾病并据此遣方用药和观察疗效的基础，故证候诊断在疾病的中医诊疗中就显得尤为重要。传统的中医诊断方法，更容易受到主观影响，导致诊断偏差，本研究将中西医诊断信息量化、数字化，使PSD中医证型的诊断更加准确，促进提高临床疗效。研究纳入中风后抑郁患者353例，将所有收集的病例信息，转化为数字信息，形成数据库。对各群分别进行主成份分析，根据碎石图和特征根的值提取出有意义的公因子共28个，根据各公因子中的诊断信息指标进行的证候要素归类，结果为阴虚、气郁、气虚、痰湿，并明确其靶位为肝、脾、肾。运用贝叶斯网络分析证候与症状、舌苔脉象、病变部位的关系；所有中西诊断数据经最后整合，运用BP人工神经网络构建中医证候量化诊断模型，并制作成网页版，可直接应用于临床。