本文選題：多狀態(tài)Markov模型 + 狀態(tài)轉(zhuǎn)移�。� 參考：《重慶醫(yī)科大學(xué)》2017年碩士論文

【摘要】：目的本研究將多狀態(tài)“疾病-預(yù)后”Markov模型應(yīng)用到2型糖尿病患者慢性并發(fā)癥罹患個(gè)數(shù)的動(dòng)態(tài)轉(zhuǎn)歸研究中,探討狀態(tài)間傳遞規(guī)律,估量狀態(tài)間的轉(zhuǎn)移概率、轉(zhuǎn)移危險(xiǎn)及滯留時(shí)間,并將其與Logistic回歸結(jié)合用以尋找影響狀態(tài)改變的可能因素,從而為2型糖尿病相關(guān)慢性并發(fā)癥的防治提供科學(xué)依據(jù)。方法回顧性地收集重慶市3所三甲醫(yī)院2011年1月至2015年5月期間出院,且出院診斷包括2型糖尿病及其慢性并發(fā)癥的患者數(shù)據(jù)。根據(jù)慢性并發(fā)癥的合并數(shù)量,本研究將合并不同數(shù)量慢性并發(fā)癥的2型糖尿病患者分為5個(gè)不同的狀態(tài),并擬合一個(gè)連續(xù)時(shí)間、離散狀態(tài)的齊次Markov過(guò)程。然后引入R3.3.2軟件中的msm軟件包進(jìn)行多狀態(tài)Markov模型的分析,并估計(jì)5狀態(tài)之間的傳遞規(guī)律、轉(zhuǎn)移風(fēng)險(xiǎn)、轉(zhuǎn)移概率及滯留時(shí)間。與此同時(shí),根據(jù)是否發(fā)生狀態(tài)轉(zhuǎn)移來(lái)篩選Logistic回歸的結(jié)局變量,并在SAS9.2軟件中進(jìn)行簡(jiǎn)單統(tǒng)計(jì)分析及Logistic回歸分析。結(jié)果從msm軟件包的plot圖和Pearson型擬合優(yōu)度檢驗(yàn)的結(jié)果可知,多狀態(tài)Markov模型擬合效果良好。多狀態(tài)Markov模型結(jié)果提示患者在狀態(tài)1,狀態(tài)2,狀態(tài)3和狀態(tài)4的總滯留時(shí)間分別為3.19月,14.51月23.87月和15.21月。轉(zhuǎn)移強(qiáng)度矩陣顯示狀態(tài)2轉(zhuǎn)化為狀態(tài)3的轉(zhuǎn)移強(qiáng)度是轉(zhuǎn)化為狀態(tài)1的1.5倍,而狀態(tài)3轉(zhuǎn)化為狀態(tài)4轉(zhuǎn)移強(qiáng)度是轉(zhuǎn)化為狀態(tài)2的接近10倍,而狀態(tài)4轉(zhuǎn)移到狀態(tài)3的轉(zhuǎn)移強(qiáng)度約為轉(zhuǎn)移到狀態(tài)5的242倍。轉(zhuǎn)移概率矩陣顯示經(jīng)過(guò)足夠長(zhǎng)的時(shí)間,處于狀態(tài)1的個(gè)體將向狀態(tài)2轉(zhuǎn)移,狀態(tài)2的個(gè)體將向狀態(tài)3轉(zhuǎn)移,狀態(tài)3的個(gè)體將向狀態(tài)4轉(zhuǎn)移,狀態(tài)4的個(gè)體也將向狀態(tài)3轉(zhuǎn)移。將單因素Logistic回歸中有影響的變量納入多因素逐步Logistic回歸中分析,結(jié)果顯示:高血壓、空腹血糖、尿素、尿微量白蛋白和高密度脂蛋白對(duì)狀態(tài)1→狀態(tài)2的轉(zhuǎn)移有影響;年齡、空腹血糖、空腹胰島素、低密度脂蛋白、總膽固醇、載脂蛋白A1和尿素對(duì)狀態(tài)2→狀態(tài)1的轉(zhuǎn)移有影響;空腹血糖、空腹胰島素、高密度脂蛋白、甘油三酯和游離脂肪酸對(duì)狀態(tài)2→狀態(tài)3的轉(zhuǎn)移有影響;空腹血糖、空腹胰島素、高密度脂蛋白、甘油三酯和游離脂肪酸對(duì)狀態(tài)3→狀態(tài)2的轉(zhuǎn)移有影響;年齡、高密度脂蛋白、甘油三酯、肌酐和游離脂肪酸對(duì)狀態(tài)3→狀態(tài)4的轉(zhuǎn)移有影響;甘油三酯、總膽固醇、肌酐和空腹胰島素對(duì)狀態(tài)4→狀態(tài)3對(duì)的轉(zhuǎn)移有影響;空腹血糖、空腹胰島素和甘油三酯對(duì)狀態(tài)4→狀態(tài)5的轉(zhuǎn)移有影響。多因素多狀態(tài)Markov模型分析結(jié)果顯示高血壓、糖化血紅蛋白、游離脂肪酸、脂蛋白a、尿白蛋白/尿肌酐比值和尿微量白蛋白對(duì)狀態(tài)1→狀態(tài)2的轉(zhuǎn)移有影響;高血壓、尿白蛋白/尿肌酐比值、年齡、空腹胰島素、低密度脂蛋白、載脂蛋白A1對(duì)狀態(tài)2→狀態(tài)1的轉(zhuǎn)移有影響;高血壓、尿白蛋白/尿肌酐比值、糖化血紅蛋白、游離脂肪酸、脂蛋白a、尿微量白蛋白對(duì)狀態(tài)2→狀態(tài)3的轉(zhuǎn)移有影響;高血壓、游離脂肪酸、載脂蛋白A1、高密度脂蛋白、肌酐、尿素對(duì)狀態(tài)3→狀態(tài)2的轉(zhuǎn)移有影響;高血壓、游離脂肪酸、載脂蛋白A1、入院時(shí)情況對(duì)狀態(tài)3→狀態(tài)4的轉(zhuǎn)移有影響;游離脂肪酸、肌酐、尿微量白蛋白、空腹胰島素、甘油三酯對(duì)狀態(tài)4→狀態(tài)3的轉(zhuǎn)移有影響;游離脂肪酸、肌酐、甘油三酯、低密度脂蛋白、空腹血糖對(duì)狀態(tài)4→狀態(tài)5的轉(zhuǎn)移有影響。結(jié)論多狀態(tài)Markov模型和傳統(tǒng)Logistic回歸模型均表明糖尿病慢性并發(fā)癥合并數(shù)量受到年齡、入院時(shí)情況、血糖、血脂、血壓和腎功能損害等指標(biāo)的影響,但不同變量對(duì)不同狀態(tài)轉(zhuǎn)移的影響程度有所差異。多狀態(tài)Markov模型對(duì)多結(jié)局事件的分析是基于全局的視角,并且考慮了時(shí)間對(duì)目標(biāo)結(jié)局出現(xiàn)的影響,因此相對(duì)于傳統(tǒng)Logistic回歸而言,其結(jié)果更為準(zhǔn)確、科學(xué)。故而,多狀態(tài)Markov模型完全可以作為傳統(tǒng)Logistic回歸模型的有益補(bǔ)充。同時(shí),轉(zhuǎn)移強(qiáng)度矩陣和轉(zhuǎn)移概率矩陣的研究結(jié)果表明,各狀態(tài)患者的病情均有進(jìn)一步加深的趨勢(shì),且患者在狀態(tài)3停留的時(shí)間較長(zhǎng),提示臨床上完全可利用該時(shí)間窗來(lái)逆轉(zhuǎn)疾病的進(jìn)展。
[Abstract]:Objective the purpose of this study was to apply the multi state "disease and prognosis" Markov model to the study of the number of chronic complications of type 2 diabetic patients, to explore the transfer rules between States, to estimate the transfer probability between States, to transfer the risk and the time of detention, and to combine it with Logistic regression in order to find the possible cause of the change of state. This provides a scientific basis for the prevention and treatment of chronic complications related to type 2 diabetes. Methods 3 Three A hospitals in Chongqing were collected from January 2011 to May 2015, and the discharge diagnosis included data of patients with type 2 diabetes and its chronic complications. According to the number of chronic complications, the study would be combined with different numbers. The patients with chronic complications of type 2 diabetes are divided into 5 different states and fit a continuous time, the homogeneous Markov process in a discrete state. Then the MSM software package in the R3.3.2 software is introduced to analyze the multi state Markov model, and the transfer of the 5 states, the transfer of risk, the transfer probability and the time of detention are estimated at the same time, The outcome variables of Logistic regression were selected according to the occurrence of state transfer, and the simple statistical analysis and Logistic regression analysis were carried out in SAS9.2 software. The results showed that the multi state Markov model had good fitting effect. The results of multi state Markov model showed that the patient was in the MSM software package. State 1, state 2, state 3 and state 4 of total retention time are 3.19 months, 14.51 months 23.87 and 15.21 months respectively. Transfer strength matrix shows that the transfer intensity of state 2 converted to state 3 is converted to 1.5 times of state 1. The degree of transfer is about 242 times that of the state 5. The transfer probability matrix shows that the individual in the state 1 will transfer to the state 2 for a long time, the individual in state 2 will transfer to the state 3, the individual of the state 3 will transfer to the state 4, and the individuals of the state 4 will be transferred to the state 3. The variables of the single factor Logistic regression will be included in the multiple factors. In the stepwise Logistic regression analysis, the results showed that hypertension, fasting blood glucose, urea, microalbuminuria and HDL had an influence on the state 1 to state 2; age, fasting blood glucose, fasting insulin, low density lipoprotein, total cholesterol, apolipoprotein A1 and urea had an effect on the state 2 to state 1; fasting blood glucose, empty Abdominal insulin, high density lipoprotein, triglyceride and free fatty acids have an effect on the transfer of state 2 to state 3; fasting blood glucose, fasting insulin, high-density lipoprotein, triglyceride and free fatty acids have an effect on the state 3 to state 2 transfer; age, high density lipoprotein, triglyceride, creatinine and free fatty acids are 3 to state. The transfer of state 4 was affected; triglyceride, total cholesterol, creatinine and fasting insulin have an effect on the transfer of state 4 to state 3; fasting blood glucose, fasting insulin and triglyceride have an effect on the transfer of state 4 to state 5. The results of multifactor and multi state Markov model analysis show hypertension, glycated hemoglobin, free fatty acid, lipoprotein. A, urinary albumin / urine creatinine ratio and urine microalbumin have an effect on the transfer of state 1 to state 2; hypertension, urine albumin / urine creatinine ratio, age, fasting insulin, low density lipoprotein, apolipoprotein A1 affect state 2 to state 1 transfer; hypertension, urinary albumin / urine creatinine ratio, glycosylated hemoglobin, free fatty acid, Lipoprotein a, urinary microalbumin has an effect on the transfer of state 2 to state 3; hypertension, free fatty acids, apolipoprotein A1, high density lipoprotein, creatinine, and urea have an influence on the transfer of state 3 to state 2; hypertension, free fatty acids, apolipoprotein A1, influence on state 3, state 4, free fatty acid, creatinine, Urinary microalbuminuria, fasting insulin and triglyceride have an effect on the state 4 - state 3 transfer. Free fatty acids, creatinine, triglycerides, low density lipoprotein and fasting blood glucose have an influence on the state of the state 4 to state 5. Conclusion the multistate Markov model and the traditional Logistic return model all indicate that the chronic complications of diabetes are affected by the number of diabetic complications. The influence of age, admission, blood sugar, blood lipid, blood pressure, and renal function damage, but different variables have different influence on different state transfer. The analysis of multi state Markov model is based on the global perspective, and the effect of time on the outcome of the target is considered, so relative to the traditional Logistic The result of the regression is more accurate and scientific. Therefore, the multistate Markov model can be used as a useful supplement to the traditional Logistic regression model. At the same time, the results of the transfer intensity matrix and the transfer probability matrix show that the patient's condition in each state has a further deepening trend, and the patient's stay in the state 3 is longer. This time window can be used clinically to reverse the progression of the disease.
【學(xué)位授予單位】：重慶醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O212.1;R587.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李亞青;;人口老齡化是否決定了醫(yī)療衛(wèi)生支出增長(zhǎng)?——理論爭(zhēng)鳴和中國(guó)啟示[J];電子科技大學(xué)學(xué)報(bào)(社科版);2017年01期

2 王志寶;孫鐵山;張杰斐;;人口老齡化區(qū)域類型劃分與區(qū)域演變分析——以中美日韓四國(guó)為例[J];地理科學(xué);2015年07期

3 周吉;周芳華;石健;葉琳;楊紹湖;江珊;梁寶送;;南寧市35歲以上2型糖尿病患者血糖控制現(xiàn)狀及影響因素分析[J];中華疾病控制雜志;2014年09期

4 李英華;李莉;聶雪瓊;孫思偉;黃相剛;石名菲;李方波;衛(wèi)薇;;中國(guó)6省糖尿病患者糖尿病慢性并發(fā)癥自報(bào)率及影響因素研究[J];中國(guó)健康教育;2014年01期

5 王紅杰;汪曉霞;房澤慧;劉歡;林穎慧;;2型糖尿病發(fā)生大血管并發(fā)癥的危險(xiǎn)因素分析[J];現(xiàn)代生物醫(yī)學(xué)進(jìn)展;2013年20期

6 劉石平;張志;周智廣;;284例糖尿病足患者的臨床分析[J];中國(guó)動(dòng)脈硬化雜志;2013年03期

7 李敏州;高彥彬;馬鳴飛;朱智耀;鄒大威;李勤;;糖尿病腎病發(fā)病機(jī)制研究進(jìn)展[J];中國(guó)實(shí)驗(yàn)方劑學(xué)雜志;2012年22期

8 李永昆;林謙平;韓凜;黃麗媛;王翠;;社區(qū)糖尿病危險(xiǎn)因素與慢性并發(fā)癥的關(guān)聯(lián)性[J];中國(guó)實(shí)用醫(yī)藥;2012年24期

9 崔穎;郭海科;韓云飛;孟倩麗;張良;尹東明;;2型糖尿病住院患者糖尿病視網(wǎng)膜病變患病率及危險(xiǎn)因素分析[J];眼科新進(jìn)展;2012年08期

10 樓雪勇;;2型糖尿病患者慢性并發(fā)癥患病率及危險(xiǎn)因素分析[J];中國(guó)現(xiàn)代醫(yī)生;2011年04期

相關(guān)博士學(xué)位論文 前3條

1 方家追;2型糖尿病住院原因和慢性并發(fā)癥患病率及其危險(xiǎn)因素分析[D];浙江大學(xué);2015年

2 李偉芳;老年2型糖尿病及其慢性并發(fā)癥的相關(guān)研究[D];鄭州大學(xué);2015年

3 宋慶芳;游離脂肪酸與2型糖尿病周?chē)窠?jīng)病變的關(guān)系及其機(jī)制探討[D];河北醫(yī)科大學(xué);2009年

相關(guān)碩士學(xué)位論文 前10條

1 宋澤婧;貝葉斯半?yún)?shù)多狀態(tài)模型在老年人認(rèn)知與抑郁關(guān)系研究中的應(yīng)用[D];山西醫(yī)科大學(xué);2016年

2 張思恒;多狀態(tài)競(jìng)爭(zhēng)風(fēng)險(xiǎn)模型在2型糖尿病與痛風(fēng)發(fā)病關(guān)系研究中的應(yīng)用[D];暨南大學(xué);2015年

3 孔盼盼;時(shí)間轉(zhuǎn)換Markov模型在阿爾茨海默病進(jìn)程研究中的應(yīng)用[D];山西醫(yī)科大學(xué);2015年

4 王月娟;2型糖尿病患者患病情況及慢性并發(fā)癥的相關(guān)因素分析[D];吉林大學(xué);2015年

5 李妍;糖尿病視網(wǎng)膜病變的相關(guān)危險(xiǎn)因素分析[D];吉林大學(xué);2014年

6 章峻鈞;2型糖尿病患者微血管病變的影響因素研究[D];廣西醫(yī)科大學(xué);2014年

7 杜亞玲;2型糖尿病患者胰島素強(qiáng)化治療的住院費(fèi)用和成本效果分析研究[D];石河子大學(xué);2014年

8 陸艷艷;肥胖型2型糖尿病與腦血管病變的相關(guān)性研究[D];蘇州大學(xué);2014年

9 宋艷龍;競(jìng)爭(zhēng)風(fēng)險(xiǎn)模型在阿爾茨海默病轉(zhuǎn)歸研究中的應(yīng)用[D];山西醫(yī)科大學(xué);2014年

10 高進(jìn);糖尿病前期人群危險(xiǎn)因素分析[D];中國(guó)人民解放軍軍事醫(yī)學(xué)科學(xué)院;2013年

，

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