本文選題：原發(fā)性閉角型青光眼 + 原發(fā)性開角型青光眼��； 參考：《復(fù)旦大學(xué)》2012年博士論文

【摘要】：目的：通過對(duì)PACG、POAG、正常對(duì)照組的臨床資料進(jìn)行單因素和Logistic多因素回歸分析,以明確PACG、POAG的相關(guān)獨(dú)立危險(xiǎn)因素。 方法：在2009.12-2011.11期間,共篩出在復(fù)旦大學(xué)附屬眼耳鼻喉科醫(yī)院住院的PACG患者200例、POAG患者100例、正常對(duì)照組200例。通過病史回顧性收集這些患者的相關(guān)臨床資料變量,個(gè)人一般情況包括：年齡、性別、是否有青光眼家族史、是否有高血壓史、是否有糖尿病史、是否有全身其他病史。臨床相關(guān)資料包括：矯正視力、屈光情況、眼內(nèi)壓、前房深度、杯盤比、中央角膜厚度、角膜曲率、眼軸、晶狀體厚度、晶狀體位置、相對(duì)晶狀體位置。同時(shí),比較這些因素在PACG組、POAG組和正常對(duì)照組三組上的分布有無差異性,并進(jìn)行相關(guān)的Logistic多因素回歸分析。 結(jié)果：在單因素分析結(jié)果的基礎(chǔ)上,對(duì)相關(guān)變量進(jìn)行Logistic多因素回歸分析后發(fā)現(xiàn),最終入組預(yù)測(cè)PACG模型的變量有糖尿病、杯/盤比、眼軸、角膜曲率、中央角膜厚度,其中除角膜曲率外,其他4個(gè)變量是預(yù)測(cè)PACG的獨(dú)立危險(xiǎn)因素(p0.05)。整個(gè)Logistic多因素回歸模型的C-index為0.956。最終入組POAG模型的變量有性別、高度近視、杯/盤比、眼軸,其中除性別外,其他3個(gè)變量是預(yù)測(cè)POAG的獨(dú)立危險(xiǎn)因素(p0.05)。整個(gè)Logistic多因素回歸模型的C-index為0.975。 結(jié)論：糖尿病、杯／盤比、眼軸、中央角膜厚度是PACG的獨(dú)立危險(xiǎn)因素,而高度近視、杯／盤比、眼軸是POAG的獨(dú)立危險(xiǎn)因素。 目的：建立并驗(yàn)證預(yù)測(cè)PACG發(fā)生風(fēng)險(xiǎn)的CART及列線圖模型,并通過與其他模型或標(biāo)準(zhǔn)比較,以明確最佳的模型或標(biāo)準(zhǔn),從而根據(jù)該最佳模型或標(biāo)準(zhǔn)以減少不必要的干預(yù)措施。 方法：CART模型的建立及評(píng)估：對(duì)相關(guān)變量進(jìn)行CART統(tǒng)計(jì)分析以建立用于預(yù)測(cè)PACG發(fā)生風(fēng)險(xiǎn)的CART模型,并采用10倍交叉驗(yàn)證方法對(duì)此CART模型進(jìn)行內(nèi)部驗(yàn)證以減少過度擬合偏倚。列線圖模型的建立及評(píng)估：根據(jù)PACG的Logistic多因素回歸分析確定模型入組變量,并依據(jù)相關(guān)變量的回歸系數(shù)畫出相應(yīng)的列線圖模型,并采用Bootstrap自抽樣方法對(duì)列線圖模型進(jìn)行內(nèi)部驗(yàn)證以減少過度擬合偏倚,同時(shí)評(píng)價(jià)列線圖模型預(yù)測(cè)PACG發(fā)生風(fēng)險(xiǎn)的符合度。最后,采用AUC. C-index、DCA統(tǒng)計(jì)方法比較列線圖模型、CART模型和前房深度指標(biāo)在預(yù)測(cè)PACG的準(zhǔn)確性及臨床應(yīng)用價(jià)值上的優(yōu)劣性。 結(jié)果：CART模型4個(gè)節(jié)點(diǎn)上的PACG發(fā)生率分別為99.3%、92.9%、87.5%及8.8%,并且在經(jīng)過內(nèi)部驗(yàn)證后得到的C-index為0.965,表現(xiàn)出較好的預(yù)測(cè)準(zhǔn)確性。PACG的列線圖模型輸入變量包含糖尿病、杯/盤比、眼軸、角膜曲率、中央角膜厚度,在經(jīng)過內(nèi)部驗(yàn)證后C-index為0.953。根據(jù)AUC、C-index、DCA統(tǒng)計(jì)方法,CART及列線圖模型均優(yōu)于前房深度指標(biāo),而CART及列線圖模型按閾值概率范圍的不同,在臨床應(yīng)用價(jià)值上各有其優(yōu)勢(shì)。 結(jié)論：在預(yù)測(cè)PACG發(fā)生風(fēng)險(xiǎn)的準(zhǔn)確性及臨床應(yīng)用價(jià)值上,CART及列線圖模型均優(yōu)于前房深度指標(biāo)。在臨床應(yīng)用價(jià)值上,可以在青光眼篩查中聯(lián)合應(yīng)用CART模型、列線圖模型和前房深度指標(biāo),不以單一模型篩選。 目的：建立并驗(yàn)證預(yù)測(cè)POAG發(fā)生風(fēng)險(xiǎn)的CART及列線圖模型,并通過與其他模型或標(biāo)準(zhǔn)比較,以明確最佳的模型或標(biāo)準(zhǔn),從而根據(jù)該最佳模型或標(biāo)準(zhǔn)以減少不必要的干預(yù)措施。 方法：CART模型的建立及評(píng)估：對(duì)相關(guān)變量進(jìn)行CART統(tǒng)計(jì)分析以建立用于預(yù)測(cè)POAG發(fā)生風(fēng)險(xiǎn)的CART模型,并采用10倍交叉驗(yàn)證方法對(duì)此回歸樹模型進(jìn)行內(nèi)部驗(yàn)證以減少過度擬合偏倚。列線圖模型的建立及評(píng)估：根據(jù)POAG的Logistic多因素回歸分析確定模型入組變量,并依據(jù)相關(guān)變量的回歸系數(shù)畫出相應(yīng)的列線圖模型,并采用Bootstrap自抽樣方法對(duì)列線圖模型進(jìn)行內(nèi)部驗(yàn)證以減少過度擬合偏倚,同時(shí)評(píng)價(jià)列線圖模型預(yù)測(cè)POAG發(fā)生風(fēng)險(xiǎn)的合度。最后,采用C-index、DCA統(tǒng)計(jì)方法比較兩模型在預(yù)測(cè)POAG的準(zhǔn)確性及臨床應(yīng)用價(jià)值上的優(yōu)劣性。 結(jié)果：CART模型2個(gè)節(jié)點(diǎn)上的PACG發(fā)生率分別為98.9%和5.2%,并且在經(jīng)過內(nèi)部驗(yàn)證后得到的C-index為0.973,表現(xiàn)出較好的預(yù)測(cè)準(zhǔn)確性。POAG列線圖模型輸入變量包含性別、高度近視、杯/盤比、眼軸,在經(jīng)過內(nèi)部驗(yàn)證后C-index為0.970。DCA統(tǒng)計(jì)結(jié)果顯示,列線圖模型在臨床應(yīng)用價(jià)值上優(yōu)于CART模型。 結(jié)論：CART模型及列線圖模型在預(yù)測(cè)POAG發(fā)生風(fēng)險(xiǎn)的準(zhǔn)確性上相似,但在臨床應(yīng)用價(jià)值上,列線圖模型優(yōu)于CART模型。
[Abstract]:Objective: to analyze the clinical data of PACG, POAG and normal controls by single factor and Logistic multivariate regression analysis in order to determine the independent risk factors of PACG and POAG.
Methods: during the period of 2009.12-2011.11, 200 cases of PACG patients hospitalized in Otolaryngological Hospital Affiliated to Fudan University, 100 cases of POAG and 200 cases of normal control group were collected, and the related clinical data of these patients were collected through the history of disease, and the general situation included: age, sex, family history of glaucoma, whether there were high History of blood pressure, whether there is a history of diabetes, or whether there are other systemic history. Clinical data include: corrected visual acuity, refraction, intraocular pressure, anterior chamber depth, cup disc ratio, central corneal thickness, corneal curvature, eye axis, lens thickness, lens position, relative crystalline body position. At the same time, these factors are compared in group PACG, POAG and normal control. There was no difference in the distribution of the three groups, and Logistic regression analysis was performed.
Results: on the basis of the results of the single factor analysis, after Logistic multiple regression analysis of the related variables, the final group to predict the variables of the PACG model were diabetes, cup / disc ratio, eye axis, corneal curvature, central corneal thickness, and the other 4 variables were independent risk factors (P0.05) for predicting PACG except the corneal curvature. The whole Logi was the whole Logi. The C-index of stic multi factor regression model was 0.956. final entry group POAG model variables: sex, high myopia, cup / disc ratio, eye axis, except for sex, the other 3 variables were independent risk factors (P0.05) for predicting POAG. The C-index of the whole Logistic multifactor regression model was 0.975.
Conclusion: diabetes, cup / disc ratio, axial length and central corneal thickness are independent risk factors for PACG, while high myopia, cup / disc ratio and axial length are independent risk factors for POAG.
Objective: to establish and verify the CART and line map models for predicting the risk of PACG occurrence, and to identify the best models or standards by comparing with other models or standards to reduce unnecessary interventions based on the best model or standard.
Method: the establishment and evaluation of the CART model: CART statistical analysis of related variables to establish the CART model for predicting the risk of PACG occurrence, and the 10 times cross validation method is used to verify the CART model to reduce the overfitting bias. The establishment and evaluation of the column graph model: according to the Logistic multiple factor regression of PACG Analyze the model into the group variables, and draw the corresponding line graph model according to the regression coefficient of the related variables, and use the Bootstrap self sampling method to verify the model of the line graph in order to reduce the overfitting bias, and evaluate the line graph model to predict the coincidence of the risk of the occurrence of PACG. Finally, the AUC. C-index, DCA statistical method is used. Compared with the nomogram model, the CART model and the anterior chamber depth index, the accuracy and clinical application value of PACG were better.
Results: the incidence of PACG on the 4 nodes of the CART model was 99.3%, 92.9%, 87.5% and 8.8% respectively, and the C-index was 0.965 after the internal verification. The good predictive accuracy of the line graph model input variables included diabetes, cup / disc ratio, eye axis, corneal curvature, central corneal thickness, and C-in after internal verification. DEX for 0.953. according to AUC, C-index, DCA statistical methods, CART and line graph model are better than the front room depth index, and CART and line graph model are different in the range of threshold probability, each has its advantages in clinical application.
Conclusion: in predicting the accuracy and clinical value of the risk of PACG, both the CART and the line map model are superior to the anterior chamber depth index. In the clinical application, the CART model, the line map model and the anterior chamber depth index can be applied in the glaucoma screening.
Objective: to establish and verify the CART and line map models for predicting the risk of POAG occurrence, and to identify the best models or standards by comparing with other models or standards to reduce unnecessary interventions based on the best model or standard.
Methods: the establishment and evaluation of the CART model: CART statistical analysis of related variables to establish the CART model for predicting the risk of POAG occurrence, and the 10 times cross validation method is used to verify the regression tree model to reduce the overfitting bias. The establishment and evaluation of the line map model: Based on the Logistic multiple factor regression of POAG Analysis determines the model into the group variables, and draws the corresponding line graph model according to the regression coefficient of the related variables, and uses the Bootstrap self sampling method to verify the model of the line graph in order to reduce the overfitting bias. At the same time, we evaluate the alignment of the line graph model to predict the risk of POAG occurrence. Finally, the C-index and DCA statistical methods are used to compare two The model is used to predict the accuracy and clinical value of POAG.
Results: the incidence of PACG on the 2 nodes of the CART model was 98.9% and 5.2% respectively, and the C-index obtained after the internal verification was 0.973, which showed good prediction accuracy. The input variables of the.POAG line graph model included sex, high myopia, cup / disk ratio, eye axis, and C-index for 0.970.DCA statistics after the internal verification. The line model is superior to the CART model in clinical application.
Conclusion: the CART model and nomogram model are similar in predicting the accuracy of POAG risk, but the nomogram model is better than the CART model in clinical application.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2012
【分類號(hào)】：R775

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 項(xiàng)勇剛;夏凌云;張勇;曾憲濤;許玲;;中國(guó)人近視與原發(fā)性開角型青光眼相關(guān)性的Meta分析[J];臨床眼科雜志;2014年03期

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本文編號(hào)：2082978