【摘要】：目的嘗試將分段常數(shù)強(qiáng)度Markov模型引入到輕度認(rèn)知損害(Mild Cognitive Impairment, MCI)向阿爾茨海默病(Alzheimer's disease, AD)轉(zhuǎn)歸過(guò)程中,深入研究影響轉(zhuǎn)歸過(guò)程的影響因素,為制定不同發(fā)展階段的預(yù)防措施提供理論依據(jù)。從應(yīng)用角度出發(fā),對(duì)分段常數(shù)強(qiáng)度Markov模型在慢性病中的研究進(jìn)行方法學(xué)的探試,為其他多狀態(tài)慢性病不同發(fā)展階段影響因素的探討提供方法學(xué)借鑒。方法應(yīng)用太原市600名社區(qū)老年人的4次隨訪(fǎng)資料,以MCI為狀態(tài)1,中重度認(rèn)知損害為狀態(tài)2,AD為狀態(tài)3,擬合分段常數(shù)強(qiáng)度Markov模型,分析MCI向AD轉(zhuǎn)歸不同發(fā)展階段的影響因素。 結(jié)果經(jīng)假設(shè)檢驗(yàn)，，,數(shù)據(jù)滿(mǎn)足Markov性(P=0.89),不滿(mǎn)足時(shí)齊性(P0.001),可應(yīng)用分段常數(shù)強(qiáng)度Markov模型擬合。采用分段常數(shù)強(qiáng)度Markov模型,對(duì)于第個(gè)時(shí)間區(qū)間[0,12]月,經(jīng)多因素篩選,發(fā)現(xiàn)性別、年齡、文化程度、職業(yè)和高血壓對(duì)狀態(tài)1到狀態(tài)2的轉(zhuǎn)移有統(tǒng)計(jì)學(xué)意義,年齡、文化程度、職業(yè)、高血壓和糖尿病對(duì)狀態(tài)1到狀態(tài)3的轉(zhuǎn)移有統(tǒng)計(jì)學(xué)意義,性別、年齡、文化程度、高血壓和糖尿病對(duì)狀態(tài)2到狀態(tài)3的轉(zhuǎn)移有統(tǒng)計(jì)學(xué)意義。對(duì)于第二個(gè)時(shí)間區(qū)間[12,18]月,經(jīng)多因素篩選,發(fā)現(xiàn)性別、年齡、文化程度、職業(yè)、吸煙和高血壓對(duì)狀態(tài)1到狀態(tài)2的轉(zhuǎn)移有統(tǒng)計(jì)學(xué)意義,文化程度、職業(yè)、吸煙、高血壓和糖尿病對(duì)狀態(tài)1到狀態(tài)3的轉(zhuǎn)移有統(tǒng)計(jì)學(xué)意義,年齡、文化程度、職業(yè)、吸煙、高血壓和糖尿病對(duì)狀態(tài)2到狀態(tài)3的轉(zhuǎn)移有統(tǒng)計(jì)學(xué)意義。因此,女性、年齡、吸煙、高血壓和糖尿病是MCI向AD轉(zhuǎn)歸的危險(xiǎn)因素,高文化程度和從事腦力勞動(dòng)是MCI向AD轉(zhuǎn)歸的保護(hù)因素。根據(jù)模型作出生存曲線(xiàn)并計(jì)算轉(zhuǎn)移強(qiáng)度矩陣和3年轉(zhuǎn)移概率矩陣。 結(jié)論為延緩爾茨海默病的疾病進(jìn)程，應(yīng)根據(jù)影響疾病不同階段轉(zhuǎn)移的影響因素,開(kāi)展疾病分階段重點(diǎn)防治。與齊次Markov模型相比,分段常數(shù)強(qiáng)度Markov模型對(duì)疾病轉(zhuǎn)歸過(guò)程的相關(guān)影響因素及其變化規(guī)律的分析更為全面。應(yīng)根據(jù)收集數(shù)據(jù)的實(shí)際情況,選擇合適的模型進(jìn)行分析。
[Abstract]:Objective to introduce the piecewise constant intensity Markov model into the process of mild cognitive impairment (MCI) to (Alzheimer's disease, AD, and to study the factors influencing the outcome. To provide theoretical basis for formulating preventive measures at different stages of development. From the point of view of application, the research of piecewise constant intensity Markov model in chronic diseases is explored, which provides methodological reference for the discussion of the influencing factors of other multi-state chronic diseases in different stages of development. Methods six hundred elderly people in Taiyuan were followed up for 4 times. MCI was taken as state 1, moderate and severe cognitive impairment as state 2 and AD as state 3, and the Markov model of piecewise constant strength was fitted to analyze the influencing factors of MCI to AD in different stages of development. Results after hypothesis test, the data satisfied Markov property (P0. 89) and unsatisfied time homogeneity (P0. 001), which can be fitted by piecewise constant strength Markov model. Using a piecewise constant intensity Markov model, for the first time interval of [0 ~ 12] months, we found that sex, age, education, occupation and hypertension had statistical significance in the transfer of state 1 to state 2. Occupation, hypertension and diabetes had statistical significance in the metastasis of state 1 to state 3, sex, age, education, and hypertension and diabetes had statistical significance in the metastasis of state 2 to state 3. For the second time interval [12 ~ 18] months, sex, age, education, occupation, smoking and hypertension were found to be statistically significant in the transfer of state 1 to state 2, education level, occupation, smoking. Hypertension and diabetes were statistically significant for the metastasis from state 1 to state 3, and age, education, occupation, smoking, hypertension and diabetes were statistically significant for the metastasis from state 2 to state 3. Therefore, women, age, smoking, hypertension and diabetes are risk factors for MCI to AD, high education and mental labor are the protective factors of MCI to AD. The survival curve is made according to the model and the transfer intensity matrix and the 3-year transition probability matrix are calculated. Conclusion in order to delay the progression of Alzheimer's disease, the prevention and treatment of the disease should be carried out in different stages according to the influencing factors of different stages of disease metastasis. Compared with the homogeneous Markov model, the piecewise constant intensity Markov model is more comprehensive in the analysis of the related factors and the changing rules of the disease outcome process. According to the actual situation of data collection, the appropriate model should be selected for analysis.
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：R749.16

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