Article(id=1241522854450230073, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202309033, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1693670400000, receivedDateStr=2023-09-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931714880, onlineDateStr=2026-03-19, pubDate=1707494400000, pubDateStr=2024-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931714880, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931714880, creator=13701087609, updateTime=1773931714880, updator=13701087609, issue=Issue{id=1241522846384583426, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773931712956, creator=13701087609, updateTime=1773931842201, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241523388544504301, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241523388544504302, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=557, endPage=563, ext={EN=ArticleExt(id=1241522856119563076, articleId=1241522854450230073, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of key influencing factors and risk prediction of diabetic retinopathy, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the key influencing factors of diabetic retinopathy (DR), analyze the current situation of DR, and construct a risk prediction model.

Methods

Based on the diabetic complication early warning data set published by the national population and health science data sharing platform, the key influencing factors of DR were obtained by univariate and multivariate Logistic regression analyses. The entropy weighting method, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the rank-sum ratio (RSR) were used to quantify the risk of DR development in patients and stratified into three levels: high, medium, and low. Logistic regression, random forest, and support vector machine models were constructed, respectively, and model fusion was performed using voting, averaging, and weighted averaging to evaluate the model predictive effect and obtain the best predictive model.

Results

Finally, 14 indexes including age and hyperlipidemia were extracted as key influencing factors. The stratification results showed that there were 50 diabetic patients without DR in this data set with a risk of about 82.99%, which was a high-risk group for DR and needed more attention. The best prediction effect was obtained from the voting machine fusion model (Acc: 80.18%, F1: 0.7868).

Conclusion

The key influencing factors of DR are analyzed, providing the direction of treatment and prevention. The low, medium, and high-risk groups of DR are classified for risk early warning. By comparing the effect between the models, the prediction model of morbidity risk of DR is constructed, providing insights for clinical early warning and data analysis.

, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Jin-yuan WU, Hong-qing AN), CN=ArticleExt(id=1241522860733297661, articleId=1241522854450230073, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=糖尿病并发视网膜病变的关键影响因素分析和风险预测研究, columnId=1228016570119680182, journalTitle=现代预防医学, columnName=临床与预防, runingTitle=null, highlight=null, articleAbstract=
目的

探究糖尿病并发视网膜病变(diabetic retinopathy,DR)的关键影响因素,进行发病现状分析,并构建发病风险预测模型。

方法

基于“国家人口与健康科学数据共享平台”公布的“糖尿病并发症预警数据集”,利用单因素和多因素logistic回归分析得到DR发病关键影响因素;运用熵权法、优劣解距离法(technique for order preferenceby similarity to ideal solution,TOPSIS)、联合秩和比方法(rank-sum ratio,RSR)进行发病风险分层;分别构建logistic回归、随机森林、支持向量机模型,使用投票、平均、加权平均三种方法进行模型融合,对模型预测效果进行评估,取得最佳预测模型。

结果

最终提取年龄、高脂血等14个指标作为关键影响因素;分层结果显示未患DR的糖尿病患者中存在50人患病风险较高,约为82.99%,需要重点关注;投票器融合模型预测效果最佳(Acc:80.18%,F1:0.786 8)。

结论

分析得到DR关键影响因素,提供了治疗与预防方向;进行发病风险现状分析,划分DR低中高风险人群,进行风险预警;通过模型间效果对比,构建DR发病风险预测模型,为其临床预警提供了数据分析思路与方法。

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安洪庆,E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点,除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=ffHCgplImLe2AIc4vHy9yA==, magXml=THVPc5Ning70d2QdT7VlTw==, pdfUrl=null, pdf=8ySnvMzC7cl2sNCsF1nIUA==, pdfFileSize=823769, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=XNW6zxh2Wycmh3e2YqXekA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Mpm7UVb1OMVD9g5AqjUGfA==, mapNumber=null, authorCompany=null, fund=null, authors=

武巾媛(2003—),女,本科在读,研究方向:大数据管理与应用

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Correlation of time in range and mean amplitude of glycemic excursions with diabetic retinopathy in type 2 diabetes mellitus[J]. Chinese Journal of Diabetes, 2021, 29(6): 443-447., articleTitle=Correlation of time in range and mean amplitude of glycemic excursions with diabetic retinopathy in type 2 diabetes mellitus, refAbstract=null), Reference(id=1241680458971411303, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, doi=null, pmid=null, pmcid=null, year=2023, volume=14, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[14], rfOrder=23, authorNames=Liu ZX, Li XL, Wang YL, journalName=Frontiers in Endocrinology, refType=null, unstructuredReference=Liu ZX, Li XL, Wang YL, et al. The concordance and discordance of diabetic kidney disease and retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study of 26,809 patients from 5 primary hospitals in China[J]. Frontiers in Endocrinology, 2023, 14:1133290., articleTitle=The concordance and discordance of diabetic kidney disease and retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study of 26,809 patients from 5 primary hospitals in China, refAbstract=null), Reference(id=1241680459185320818, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, doi=null, pmid=null, pmcid=null, year=2023, volume=13, issue=1, pageStart=9881, pageEnd=null, url=null, language=null, rfNumber=[15], rfOrder=24, authorNames=Zhong JB, Yao YF, Zeng GQ, journalName=Scientific Reports, refType=null, unstructuredReference=Zhong JB, Yao YF, Zeng GQ, et al. A closer association between blood urea Nitrogen and the probability of diabetic retinopathy in patients with shorter type 2 diabetes duration[J]. Scientific Reports, 2023, 13(1): 9881., articleTitle=A closer association between blood urea Nitrogen and the probability of diabetic retinopathy in patients with shorter type 2 diabetes duration, refAbstract=null), Reference(id=1241680459332121465, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, doi=null, pmid=null, pmcid=null, year=2022, volume=20, issue=4, pageStart=9, pageEnd=12, url=null, language=null, rfNumber=[16], rfOrder=25, authorNames=王璐瑶, 曾智, journalName=现代医院管理, refType=null, unstructuredReference=王璐瑶,曾智.基于TOPSIS法和RSR法的江苏省病床使用效率研究[J].现代医院管理202220(4):9-12., articleTitle=基于TOPSIS法和RSR法的江苏省病床使用效率研究, refAbstract=null), Reference(id=1241680459516670849, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, doi=null, pmid=null, pmcid=null, year=2022, volume=20, issue=4, pageStart=9, pageEnd=12, url=null, language=null, rfNumber=[16], rfOrder=26, authorNames=Wang LY, Zeng Z, journalName=Modern Hospital Management, refType=null, unstructuredReference=Wang LY, Zeng Z. Study on hospital bed efficiency in Jiangsu province based on TOPSIS and RSR[J]. Modern Hospital Management, 2022, 20(4):9-12., articleTitle=Study on hospital bed efficiency in Jiangsu province based on TOPSIS and RSR, refAbstract=null)], funds=[Fund(id=1241680454546420410, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, awardId=2020MSA105, language=CN, fundingSource=中国学位与研究生教育学会课题(2020MSA105), fundOrder=null, country=null), Fund(id=1241680454647083717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, awardId=23PG0411, language=CN, fundingSource=中国高等教育学会2023年度课题(23PG0411), fundOrder=null, country=null), Fund(id=1241680454739358411, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, awardId=YJKT202126, language=CN, fundingSource=山东省高等医学教育研究中心规划课题(YJKT202126), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241680447244136765, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, xref=null, ext=[AuthorCompanyExt(id=1241680447252525375, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, companyId=1241680447244136765, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Weifang Medical College, Weifang, Shandong 261053, China), AuthorCompanyExt(id=1241680447256719681, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, companyId=1241680447244136765, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=潍坊医学院,山东 潍坊 261053)])], figs=[ArticleFig(id=1241680451115479573, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Figure 1, caption=Random forest training process diagram, figureFileSmall=jvJs1RnYqKMNhdm0YkKmCQ==, figureFileBig=gSZc+pKz+9zAcCfml28RKQ==, tableContent=null), ArticleFig(id=1241680451253891613, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=图1, caption=随机森林训练过程图, figureFileSmall=jvJs1RnYqKMNhdm0YkKmCQ==, figureFileBig=gSZc+pKz+9zAcCfml28RKQ==, tableContent=null), ArticleFig(id=1241680451362943525, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Table 1, caption=

Basic information of variables

, figureFileSmall=null, figureFileBig=null, tableContent=
指标特征占比(%)指标特征占比(%)指标MinMax
患病种类0=糖尿病46.50心肌梗死0=未患病93.64血肌酐(μmol/L)29.61 300.8110.88±122.14
1=糖尿病并发
视网膜病变53.501=患病6.36年龄(岁)199358.04±11.02
性别0=男36.21心功能不全及心力衰竭0=未患病91.81身高(cm)112193166.80±7.85
1=女63.791=患病8.19体重(kg)3415673.39±12.74
民族0=汉族94.72心律失常0=未患病93.16收缩压(mm Hg)91250139.18±21.13
1=少数民族5.281=患病6.84舒张压(mm Hg)4514580.82±11.89
婚姻状态0=其他1.56呼吸系统疾病0=未患病81.63BMI(kg/m213.3555.5826.30±3.70
1=已婚98.441=患病18.37空腹血糖(mmol/L)2.1342.198.37±3.82
高血压0=未患病27.42下肢动脉病变0=未患病79.42糖化血红蛋白(mmol/L)3.915.37.83±1.80
1=患病72.581=患病20.58甘油三酯(mmol/L)0.2715.112.05±1.64
高脂血0=未患病75.97血液病0=未患病82.06总胆固醇(mmol/L)1.2120.854.65±1.42
1=患病24.031=患病17.94高密度脂蛋白胆固醇(mmol/L)0.073.091.06±0.32
动脉粥样硬化0=未患病43.53风湿免疫疾病0=未患病96.12低密度脂蛋白胆固醇(mmol/L)0.1617.312.87±1.16
1=患病56.471=患病3.88纤维蛋白原(g/L)1.08883.89.38±44.99
脑卒中0=未患病90.30妊娠哺乳期0=未患病99.78血尿素(mmol/L)1.4650.947.31±5.16
1=患病9.701=患病0.22血清尿酸(mol/L)11.51 153.4332.05±101.66
颈动脉狭窄0=未患病95.15其他内分泌疾病0=未患病60.08血红蛋白(g/L)42244131.57±8.89
1=患病4.851=患病39.92红细胞压积(红细胞比积测定,%)0.140.740.39±0.06
脂肪肝0=未患病62.93内分泌腺瘤0=未患病95.20血小板(109/L)2574215.59±68.69
1=患病37.071=患病4.80总胆红素(mmol/L)1.2278.510.95±10.91
肝硬化0=未患病97.84多囊卵巢综合征0=未患病99.89直接胆红素(μmol/L)0.1226.43.50±8.01
1=患病2.161=患病0.11总蛋白(g/L)32.194.365.32±7.57
其他慢性肝病0=未患病82.70消化系肿瘤0=未患病93.10血清白蛋白(g/L)11.755.639.23±5.96
1=患病17.301=患病6.90乳酸脱氢酶(U/L)76.3741.5172.84±58.76
胰腺外分泌疾病0=未患病97.90泌尿系肿瘤0=未患病98.81谷丙转氨酶(U/L)2.71 076.624.69±31.99
1=患病2.101=患病1.19谷草转氨酶(U/L)3.6756.820.66±24.86
胆道疾病0=未患病82.81妇科肿瘤0=未患病96.71谷氨酰胺转移酶(U/L)4.31 404.442.73±70.03
1=患病17.191=患病3.29碱性磷酸酶(U/L)9.591175.63±41.51
肾病0=未患病49.19乳腺肿瘤0=未患病99.52凝血酶原时间(s)9.535.313.09±1.22
1=患病50.811=患病0.48凝血酶原活动度(%)0.6316498.97±20.09
肾衰0=未患病92.08肺部肿瘤0=未患病97.84部分活化凝血酶原时间(s)20.318036.55±6.34
1=患病7.921=患病2.16肿瘤标志物CA199(U/ml)0.6703 229.67±227.18
神经系统疾病0=未患病93.37颅内肿瘤0=未患病99.35间接胆红素(μmol/L)0.483.77.44±4.71
1=患病6.631=患病0.65球蛋白(g/L)14.261.426.11±4.90
冠心病0=未患病66.86其他肿瘤0=未患病89.06
1=患病33.141=患病10.94
), ArticleFig(id=1241680451471995442, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=表1, caption=

变量基本情况表

, figureFileSmall=null, figureFileBig=null, tableContent=
指标特征占比(%)指标特征占比(%)指标MinMax
患病种类0=糖尿病46.50心肌梗死0=未患病93.64血肌酐(μmol/L)29.61 300.8110.88±122.14
1=糖尿病并发
视网膜病变53.501=患病6.36年龄(岁)199358.04±11.02
性别0=男36.21心功能不全及心力衰竭0=未患病91.81身高(cm)112193166.80±7.85
1=女63.791=患病8.19体重(kg)3415673.39±12.74
民族0=汉族94.72心律失常0=未患病93.16收缩压(mm Hg)91250139.18±21.13
1=少数民族5.281=患病6.84舒张压(mm Hg)4514580.82±11.89
婚姻状态0=其他1.56呼吸系统疾病0=未患病81.63BMI(kg/m213.3555.5826.30±3.70
1=已婚98.441=患病18.37空腹血糖(mmol/L)2.1342.198.37±3.82
高血压0=未患病27.42下肢动脉病变0=未患病79.42糖化血红蛋白(mmol/L)3.915.37.83±1.80
1=患病72.581=患病20.58甘油三酯(mmol/L)0.2715.112.05±1.64
高脂血0=未患病75.97血液病0=未患病82.06总胆固醇(mmol/L)1.2120.854.65±1.42
1=患病24.031=患病17.94高密度脂蛋白胆固醇(mmol/L)0.073.091.06±0.32
动脉粥样硬化0=未患病43.53风湿免疫疾病0=未患病96.12低密度脂蛋白胆固醇(mmol/L)0.1617.312.87±1.16
1=患病56.471=患病3.88纤维蛋白原(g/L)1.08883.89.38±44.99
脑卒中0=未患病90.30妊娠哺乳期0=未患病99.78血尿素(mmol/L)1.4650.947.31±5.16
1=患病9.701=患病0.22血清尿酸(mol/L)11.51 153.4332.05±101.66
颈动脉狭窄0=未患病95.15其他内分泌疾病0=未患病60.08血红蛋白(g/L)42244131.57±8.89
1=患病4.851=患病39.92红细胞压积(红细胞比积测定,%)0.140.740.39±0.06
脂肪肝0=未患病62.93内分泌腺瘤0=未患病95.20血小板(109/L)2574215.59±68.69
1=患病37.071=患病4.80总胆红素(mmol/L)1.2278.510.95±10.91
肝硬化0=未患病97.84多囊卵巢综合征0=未患病99.89直接胆红素(μmol/L)0.1226.43.50±8.01
1=患病2.161=患病0.11总蛋白(g/L)32.194.365.32±7.57
其他慢性肝病0=未患病82.70消化系肿瘤0=未患病93.10血清白蛋白(g/L)11.755.639.23±5.96
1=患病17.301=患病6.90乳酸脱氢酶(U/L)76.3741.5172.84±58.76
胰腺外分泌疾病0=未患病97.90泌尿系肿瘤0=未患病98.81谷丙转氨酶(U/L)2.71 076.624.69±31.99
1=患病2.101=患病1.19谷草转氨酶(U/L)3.6756.820.66±24.86
胆道疾病0=未患病82.81妇科肿瘤0=未患病96.71谷氨酰胺转移酶(U/L)4.31 404.442.73±70.03
1=患病17.191=患病3.29碱性磷酸酶(U/L)9.591175.63±41.51
肾病0=未患病49.19乳腺肿瘤0=未患病99.52凝血酶原时间(s)9.535.313.09±1.22
1=患病50.811=患病0.48凝血酶原活动度(%)0.6316498.97±20.09
肾衰0=未患病92.08肺部肿瘤0=未患病97.84部分活化凝血酶原时间(s)20.318036.55±6.34
1=患病7.921=患病2.16肿瘤标志物CA199(U/ml)0.6703 229.67±227.18
神经系统疾病0=未患病93.37颅内肿瘤0=未患病99.35间接胆红素(μmol/L)0.483.77.44±4.71
1=患病6.631=患病0.65球蛋白(g/L)14.261.426.11±4.90
冠心病0=未患病66.86其他肿瘤0=未患病89.06
1=患病33.141=患病10.94
), ArticleFig(id=1241680451602018874, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Table 2, caption=

Single-factor logistic regression results

, figureFileSmall=null, figureFileBig=null, tableContent=
序号指标OR值(95%CI P
1血肌酐(μmol/L)1.004(1.003 3~1.005 8)0.000 6<0.001
2年龄(岁)0.977(0.969 2~0.985 8)0.004 3<0.001
3性别0.977(0.808 2~1.181 4)0.096 90.811
4民族1.461(0.961 0~2.221 6)0.213 80.076
5婚姻状态0.809(0.384 5~1.704 7)0.379 90.578
6身高(cm)0.999(0.988 3~1.011 5)0.005 90.974
7体重(kg)1.006(0.999 7~1.014 2)0.003 70.061
8收缩压(mm Hg)1.015(1.010 5~1.019 8)0.002 3<0.001
9舒张压(mm Hg)1.022(1.014 5~1.030 8)0.004 1<0.001
10BMI(kg/m2)1.032(1.006 7~1.058 3)0.012 70.012
11高血压1.749(1.424 1~2.149 6)0.105 0<0.001
12高脂血0.484(0.390 3~0.602 4)0.110 7<0.001
13动脉粥样硬化1.011(0.841 5~1.215 8)0.093 90.903
14脑卒中1.944(1.401 9~2.695 9)0.166 80.000
15颈动脉狭窄1.61(1.035 8~2.505 3)0.225 30.034
16脂肪肝1.214(1.005 2~1.468 3)0.096 70.044
17肝硬化0.572(0.302 1~1.084 9)0.326 10.087
18其他慢性肝病0.903(0.709 9~1.149 0)0.122 90.406
19胰腺外分泌疾病0.913(0.484 0~1.722 4)0.323 80.778
20胆道疾病1.189(0.932 4~1.516 7)0.124 10.162
21肾病4.857(3.988 9~5.914 3)0.100 5<0.001
22肾衰7.009(4.241 9~11.581 3)0.256 2<0.001
23神经系统疾病0.735(0.509 6~1.061 2)0.187 10.1
24冠心病0.528(0.434 4~0.642 4)0.099 8<0.001
25心肌梗死0.643(0.441 5~0.937 0)0.192 00.021
26心功能不全及心力衰竭0.962(0.690 5~1.342 1)0.169 60.822
27心律失常0.845(0.589 8~1.212 8)0.183 90.362
28呼吸系统疾病0.979(0.773 9~1.239 4)0.120 10.862
29下肢动脉病变3.457(2.676 7~4.464 9)0.130 5<0.001
30血液病3.545(2.696 3~4.662 7)0.139 7<0.001
31风湿免疫疾病0.609(0.377 7~0.981 8)0.243 70.041
32妊娠哺乳期0.868(0.122 1~6.181 2)1.001 10.888
33其他内分泌疾病1.855(1.535 0~2.243 1)0.096 8<0.001
34内分泌腺瘤0.664(0.432 8~1.020 9)0.218 90.062
35多囊卵巢综合征0.869(0.054 3~13.914 0)1.415 00.92
36消化系肿瘤0.254(0.168 0~0.385 5)0.211 9<0.001
37泌尿系肿瘤0.598(0.254 4~1.405 9)0.436 10.238
38妇科肿瘤0.444(0.260 0~0.760 0)0.273 60.003
39乳腺肿瘤0.432(0.108 0~1.734 9)0.708 30.237
40肺部肿瘤0.178(0.078 6~0.405 7)0.418 7<0.001
41颅内肿瘤0.287(0.077 6~1.065 5)0.668 30.062
42其他肿瘤0.386(0.284 2~0.526 4)0.157 3<0.001
43空腹血糖(mmol/L)1.050(1.024 1~1.076 7)0.012 80.0001
44糖化血红蛋白(mmol/L)1.286(1.216 5~1.359 5)0.028 4<0.001
45甘油三酯(mmol/L)1.026(0.970 0~1.085 4)0.028 70.369
46总胆固醇(mmol/L)1.152(1.075 5~1.233 9)0.035 10.0001
47高密度脂蛋白胆固醇(mmol/L)1.305(0.980 5~1.738 2)0.146 10.068
48低密度脂蛋白胆固醇(mmol/L)1.213(1.113 1~1.322 5)0.044 0<0.001
49纤维蛋白原(g/L)1.001(0.999 1~1.003 5)0.001 10.242
50血尿素(mmol/L)1.133(1.102 2~1.165 2)0.014 2<0.001
51血清尿酸(μmol/L)1.002(1.001 2~1.003 1)0.000 5<0.001
52血红蛋白(g/L)0.981(0.976 8~0.985 2)0.002 2<0.001
53细胞压积(红细胞比积测定,%)0.000 4(0.000 1~0.001 9)0.800 9<0.001
54血小板(109/L)1.000(0.999 2~1.001 8)0.000 70.467
55总胆红素(mmol/L)0.95(0.934 8~0.966 7)0.008 5<0.001
56直接胆红素(μmol/L)0.804(0.761 8~0.848 6)0.027 5<0.001
57总蛋白(g/L)0.933(0.920 9~0.946 6)0.007 0<0.001
58血清白蛋白(g/L)0.912(0.896 6~0.929 2)0.009 1<0.001
59乳酸脱氢酶(U/L)1.003(1.001 3~1.004 7)0.000 90.0005
60谷丙转氨酶(U/L)0.982(0.976 6~0.987 5)0.002 8<0.001
61谷草转氨酶(U/L)0.977(0.969 5~0.985 7)0.004 2<0.001
62谷氨酰胺转移酶(U/L)0.995(0.994 0~0.997 9)0.001 0<0.001
63碱性磷酸酶(U/L)0.996(0.994 4~0.999 4)0.001 30.016
64凝血酶原时间(s)0.832(0.760 2~0.912 0)0.046 50.0001
65凝血酶原活动度(%)1.002(0.997 9~1.007 0)0.002 30.285
66部分活化凝血酶原时间(s)0.986(0.970 9~1.001 8)0.008 00.082
67肿瘤标志物CA199(U/ml)0.999(0.998 3~1.000 3)0.000 50.188
68间接胆红素(mol/L)0.952(0.931 1~0.973 3)0.011 3<0.001
69球蛋白(g/L)0.972(0.954 7~0.991 2)0.009 60.004
), ArticleFig(id=1241680451736236617, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=表2, caption=

单因素logistic回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
序号指标OR值(95%CI P
1血肌酐(μmol/L)1.004(1.003 3~1.005 8)0.000 6<0.001
2年龄(岁)0.977(0.969 2~0.985 8)0.004 3<0.001
3性别0.977(0.808 2~1.181 4)0.096 90.811
4民族1.461(0.961 0~2.221 6)0.213 80.076
5婚姻状态0.809(0.384 5~1.704 7)0.379 90.578
6身高(cm)0.999(0.988 3~1.011 5)0.005 90.974
7体重(kg)1.006(0.999 7~1.014 2)0.003 70.061
8收缩压(mm Hg)1.015(1.010 5~1.019 8)0.002 3<0.001
9舒张压(mm Hg)1.022(1.014 5~1.030 8)0.004 1<0.001
10BMI(kg/m2)1.032(1.006 7~1.058 3)0.012 70.012
11高血压1.749(1.424 1~2.149 6)0.105 0<0.001
12高脂血0.484(0.390 3~0.602 4)0.110 7<0.001
13动脉粥样硬化1.011(0.841 5~1.215 8)0.093 90.903
14脑卒中1.944(1.401 9~2.695 9)0.166 80.000
15颈动脉狭窄1.61(1.035 8~2.505 3)0.225 30.034
16脂肪肝1.214(1.005 2~1.468 3)0.096 70.044
17肝硬化0.572(0.302 1~1.084 9)0.326 10.087
18其他慢性肝病0.903(0.709 9~1.149 0)0.122 90.406
19胰腺外分泌疾病0.913(0.484 0~1.722 4)0.323 80.778
20胆道疾病1.189(0.932 4~1.516 7)0.124 10.162
21肾病4.857(3.988 9~5.914 3)0.100 5<0.001
22肾衰7.009(4.241 9~11.581 3)0.256 2<0.001
23神经系统疾病0.735(0.509 6~1.061 2)0.187 10.1
24冠心病0.528(0.434 4~0.642 4)0.099 8<0.001
25心肌梗死0.643(0.441 5~0.937 0)0.192 00.021
26心功能不全及心力衰竭0.962(0.690 5~1.342 1)0.169 60.822
27心律失常0.845(0.589 8~1.212 8)0.183 90.362
28呼吸系统疾病0.979(0.773 9~1.239 4)0.120 10.862
29下肢动脉病变3.457(2.676 7~4.464 9)0.130 5<0.001
30血液病3.545(2.696 3~4.662 7)0.139 7<0.001
31风湿免疫疾病0.609(0.377 7~0.981 8)0.243 70.041
32妊娠哺乳期0.868(0.122 1~6.181 2)1.001 10.888
33其他内分泌疾病1.855(1.535 0~2.243 1)0.096 8<0.001
34内分泌腺瘤0.664(0.432 8~1.020 9)0.218 90.062
35多囊卵巢综合征0.869(0.054 3~13.914 0)1.415 00.92
36消化系肿瘤0.254(0.168 0~0.385 5)0.211 9<0.001
37泌尿系肿瘤0.598(0.254 4~1.405 9)0.436 10.238
38妇科肿瘤0.444(0.260 0~0.760 0)0.273 60.003
39乳腺肿瘤0.432(0.108 0~1.734 9)0.708 30.237
40肺部肿瘤0.178(0.078 6~0.405 7)0.418 7<0.001
41颅内肿瘤0.287(0.077 6~1.065 5)0.668 30.062
42其他肿瘤0.386(0.284 2~0.526 4)0.157 3<0.001
43空腹血糖(mmol/L)1.050(1.024 1~1.076 7)0.012 80.0001
44糖化血红蛋白(mmol/L)1.286(1.216 5~1.359 5)0.028 4<0.001
45甘油三酯(mmol/L)1.026(0.970 0~1.085 4)0.028 70.369
46总胆固醇(mmol/L)1.152(1.075 5~1.233 9)0.035 10.0001
47高密度脂蛋白胆固醇(mmol/L)1.305(0.980 5~1.738 2)0.146 10.068
48低密度脂蛋白胆固醇(mmol/L)1.213(1.113 1~1.322 5)0.044 0<0.001
49纤维蛋白原(g/L)1.001(0.999 1~1.003 5)0.001 10.242
50血尿素(mmol/L)1.133(1.102 2~1.165 2)0.014 2<0.001
51血清尿酸(μmol/L)1.002(1.001 2~1.003 1)0.000 5<0.001
52血红蛋白(g/L)0.981(0.976 8~0.985 2)0.002 2<0.001
53细胞压积(红细胞比积测定,%)0.000 4(0.000 1~0.001 9)0.800 9<0.001
54血小板(109/L)1.000(0.999 2~1.001 8)0.000 70.467
55总胆红素(mmol/L)0.95(0.934 8~0.966 7)0.008 5<0.001
56直接胆红素(μmol/L)0.804(0.761 8~0.848 6)0.027 5<0.001
57总蛋白(g/L)0.933(0.920 9~0.946 6)0.007 0<0.001
58血清白蛋白(g/L)0.912(0.896 6~0.929 2)0.009 1<0.001
59乳酸脱氢酶(U/L)1.003(1.001 3~1.004 7)0.000 90.0005
60谷丙转氨酶(U/L)0.982(0.976 6~0.987 5)0.002 8<0.001
61谷草转氨酶(U/L)0.977(0.969 5~0.985 7)0.004 2<0.001
62谷氨酰胺转移酶(U/L)0.995(0.994 0~0.997 9)0.001 0<0.001
63碱性磷酸酶(U/L)0.996(0.994 4~0.999 4)0.001 30.016
64凝血酶原时间(s)0.832(0.760 2~0.912 0)0.046 50.0001
65凝血酶原活动度(%)1.002(0.997 9~1.007 0)0.002 30.285
66部分活化凝血酶原时间(s)0.986(0.970 9~1.001 8)0.008 00.082
67肿瘤标志物CA199(U/ml)0.999(0.998 3~1.000 3)0.000 50.188
68间接胆红素(mol/L)0.952(0.931 1~0.973 3)0.011 3<0.001
69球蛋白(g/L)0.972(0.954 7~0.991 2)0.009 60.004
), ArticleFig(id=1241680451866260048, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Table 3, caption=

Multi-factor logistic stepwise regression results

, figureFileSmall=null, figureFileBig=null, tableContent=
指标Estimatez valueOR值(95%CIStd.ErrorP
(Intercept)2.880 01.955 017.811(0.025 6-5.799 5)1.473 00.050
血肌酐(μmol/L)-0.001 8-1.701 00.998(-0.003 8~0.000 3)0.001 00.088
年龄(岁)-0.021 6-3.131 00.978(-0.035 2~-0.008 1)0.006 90.001
民族0.298 01.115 01.347(-0.221 9~0.828 0)0.267 40.265
体重(kg)-0.003 9-0.385 00.996(-0.023 8~0.015 7)0.010 10.700
收缩压(mm Hg)0.001 80.459 01.001(-0.005 7~0.009 3)0.003 80.646
舒张压(mm Hg)0.011 81.775 01.011(-0.001 2~0.024 8)0.006 60.075
BMI(kg/m20.034 01.064 01.034(-0.027 8~0.097 4)0.032 00.287
高血压0.206 21.367 01.229(-0.089 2~0.502 2)0.150 80.171
高脂血-0.574 3-3.898 00.563(-0.864 5~-0.286 5)0.147 3<0.001
脑卒中0.373 11.702 01.452(-0.052 6~0.807 9)0.219 30.088
颈动脉狭窄0.175 80.620 01.192(-0.374 9~0.739 1)0.283 60.535
脂肪肝0.275 41.958 01.317(0~0.551 7)0.140 70.050
肝硬化0.193 30.418 01.213(-0.718 7~1.103 8)0.462 80.676
肾病1.025 07.255 02.787(0.749 2~1.303 3)0.141 3<0.001
肾衰0.916 62.937 02.5(0.323 7~1.552 2)0.312 10.003
冠心病-0.384 8-2.580 00.68(-0.677 6~-0.092 7)0.149 10.009
心肌梗死0.005 30.020 01.005(-0.512 9~0.517 8)0.262 60.984
下肢动脉病变0.948 36.031 02.581(0.643 2~1.260 1)0.157 2<0.001
血液病0.209 11.009 01.232(-0.196 8~0.615 9)0.207 10.312
风湿免疫疾病-0.314 5-1.011 00.73(-0.932 2~0.290 5)0.311 20.312
其他内分泌疾病0.442 93.441 01.557(0.191 1~0.695 9)0.128 7<0.001
内分泌腺瘤-0.825 7-3.034 00.437(-1.363 4~-0.294 7)0.272 20.002
消化系肿瘤-0.321 5-1.245 00.725(-0.838 4~0.176 6)0.258 30.213
妇科肿瘤-0.843 0-2.404 00.43(-1.548 2~-0.169 4)0.350 70.016
肺部肿瘤-0.982 1-1.819 00.374(-2.121 6~0.013 4)0.539 90.068
颅内肿瘤-0.447 3-0.530 00.639(-2.256 9~1.125 9)0.844 10.596
其他肿瘤-0.510 1-2.429 00.6(-0.924 6~-0.100 4)0.210 00.015
空腹血糖(mmol/L)-0.024 5-1.317 00.975(-0.060 8~0.012 3)0.018 60.188
糖化血红蛋白(mmol/L)0.418 89.414 01.52(0.332 7~0.507 2)0.044 5<0.001
总胆固醇(mmol/L)-0.124 5-1.283 00.882(-0.315 4~0.067 0)0.097 00.199
高密度脂蛋白胆固醇(mmol/L)0.217 61.029 01.243(-0.194 6~0.635 5)0.211 50.303
低密度脂蛋白胆固醇(mmol/L)0.005 90.052 01.005(-0.218 2~0.229 7)0.113 70.958
血尿素(mmol/L)0.070 82.568 01.073(0.018 1~0.126 1)0.027 60.010
血清尿酸(μmol/L)0.000 0-0.058 01(-0.001 4~0.001 4)0.000 70.953
血红蛋白(g/L)0.021 51.493 01.021(-0.006 7~0.049 8)0.014 40.135
红细胞压积(红细胞比积测定)(%)-13.330 0-2.600 00(-23.430 3~-3.323 6)5.127 00.009
总胆红素(mmol/L)0.349 30.790 01.418(-0.327 5~1.801 2)0.441 90.429
直接胆红素(μmol/L)-0.392 2-0.884 00.675(-1.845 1~0.289 1)0.443 60.376
总蛋白(g/L)-0.012 9-0.098 00.987(-0.320 5~0.212 7)0.131 40.921
血清白蛋白(g/L)-0.020 4-0.154 00.979(-0.247 4~0.288 2)0.132 00.877
乳酸脱氢酶(U/L)0.001 00.807 01.001(0.001 5~0.003 6)0.001 30.419
谷丙转氨酶(U/L)-0.004 7-0.864 00.995(-0.015 2~0.006 1)0.005 40.387
谷草转氨酶(U/L)-0.010 2-1.239 00.989(-0.027 3~0.004 9)0.008 30.215
谷氨酰胺转移酶(U/L)0.000 10.065 01(-0.002 9~0.002 9)0.001 50.948
碱性磷酸酶(U/L)-0.002 0-0.814 00.998(-0.006 8~0.002 5)0.002 40.415
凝血酶原时间(s)-0.183 9-2.779 00.832(-0.318 2~-0.059 2)0.066 20.005
部分活化凝血酶原时间(s)-0.009 5-0.798 00.99(-0.033 8~0.012 2)0.012 00.424
间接胆红素(μmol/L)-0.326 2-0.738 00.721(-1.778 8~0.350 8)0.442 10.460
球蛋白(g/L)-0.006 2-0.047 00.993(-0.233 3~0.302 4)0.132 10.962
), ArticleFig(id=1241680453384598106, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=表3, caption=

多因素logistic逐步回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标Estimatez valueOR值(95%CIStd.ErrorP
(Intercept)2.880 01.955 017.811(0.025 6-5.799 5)1.473 00.050
血肌酐(μmol/L)-0.001 8-1.701 00.998(-0.003 8~0.000 3)0.001 00.088
年龄(岁)-0.021 6-3.131 00.978(-0.035 2~-0.008 1)0.006 90.001
民族0.298 01.115 01.347(-0.221 9~0.828 0)0.267 40.265
体重(kg)-0.003 9-0.385 00.996(-0.023 8~0.015 7)0.010 10.700
收缩压(mm Hg)0.001 80.459 01.001(-0.005 7~0.009 3)0.003 80.646
舒张压(mm Hg)0.011 81.775 01.011(-0.001 2~0.024 8)0.006 60.075
BMI(kg/m20.034 01.064 01.034(-0.027 8~0.097 4)0.032 00.287
高血压0.206 21.367 01.229(-0.089 2~0.502 2)0.150 80.171
高脂血-0.574 3-3.898 00.563(-0.864 5~-0.286 5)0.147 3<0.001
脑卒中0.373 11.702 01.452(-0.052 6~0.807 9)0.219 30.088
颈动脉狭窄0.175 80.620 01.192(-0.374 9~0.739 1)0.283 60.535
脂肪肝0.275 41.958 01.317(0~0.551 7)0.140 70.050
肝硬化0.193 30.418 01.213(-0.718 7~1.103 8)0.462 80.676
肾病1.025 07.255 02.787(0.749 2~1.303 3)0.141 3<0.001
肾衰0.916 62.937 02.5(0.323 7~1.552 2)0.312 10.003
冠心病-0.384 8-2.580 00.68(-0.677 6~-0.092 7)0.149 10.009
心肌梗死0.005 30.020 01.005(-0.512 9~0.517 8)0.262 60.984
下肢动脉病变0.948 36.031 02.581(0.643 2~1.260 1)0.157 2<0.001
血液病0.209 11.009 01.232(-0.196 8~0.615 9)0.207 10.312
风湿免疫疾病-0.314 5-1.011 00.73(-0.932 2~0.290 5)0.311 20.312
其他内分泌疾病0.442 93.441 01.557(0.191 1~0.695 9)0.128 7<0.001
内分泌腺瘤-0.825 7-3.034 00.437(-1.363 4~-0.294 7)0.272 20.002
消化系肿瘤-0.321 5-1.245 00.725(-0.838 4~0.176 6)0.258 30.213
妇科肿瘤-0.843 0-2.404 00.43(-1.548 2~-0.169 4)0.350 70.016
肺部肿瘤-0.982 1-1.819 00.374(-2.121 6~0.013 4)0.539 90.068
颅内肿瘤-0.447 3-0.530 00.639(-2.256 9~1.125 9)0.844 10.596
其他肿瘤-0.510 1-2.429 00.6(-0.924 6~-0.100 4)0.210 00.015
空腹血糖(mmol/L)-0.024 5-1.317 00.975(-0.060 8~0.012 3)0.018 60.188
糖化血红蛋白(mmol/L)0.418 89.414 01.52(0.332 7~0.507 2)0.044 5<0.001
总胆固醇(mmol/L)-0.124 5-1.283 00.882(-0.315 4~0.067 0)0.097 00.199
高密度脂蛋白胆固醇(mmol/L)0.217 61.029 01.243(-0.194 6~0.635 5)0.211 50.303
低密度脂蛋白胆固醇(mmol/L)0.005 90.052 01.005(-0.218 2~0.229 7)0.113 70.958
血尿素(mmol/L)0.070 82.568 01.073(0.018 1~0.126 1)0.027 60.010
血清尿酸(μmol/L)0.000 0-0.058 01(-0.001 4~0.001 4)0.000 70.953
血红蛋白(g/L)0.021 51.493 01.021(-0.006 7~0.049 8)0.014 40.135
红细胞压积(红细胞比积测定)(%)-13.330 0-2.600 00(-23.430 3~-3.323 6)5.127 00.009
总胆红素(mmol/L)0.349 30.790 01.418(-0.327 5~1.801 2)0.441 90.429
直接胆红素(μmol/L)-0.392 2-0.884 00.675(-1.845 1~0.289 1)0.443 60.376
总蛋白(g/L)-0.012 9-0.098 00.987(-0.320 5~0.212 7)0.131 40.921
血清白蛋白(g/L)-0.020 4-0.154 00.979(-0.247 4~0.288 2)0.132 00.877
乳酸脱氢酶(U/L)0.001 00.807 01.001(0.001 5~0.003 6)0.001 30.419
谷丙转氨酶(U/L)-0.004 7-0.864 00.995(-0.015 2~0.006 1)0.005 40.387
谷草转氨酶(U/L)-0.010 2-1.239 00.989(-0.027 3~0.004 9)0.008 30.215
谷氨酰胺转移酶(U/L)0.000 10.065 01(-0.002 9~0.002 9)0.001 50.948
碱性磷酸酶(U/L)-0.002 0-0.814 00.998(-0.006 8~0.002 5)0.002 40.415
凝血酶原时间(s)-0.183 9-2.779 00.832(-0.318 2~-0.059 2)0.066 20.005
部分活化凝血酶原时间(s)-0.009 5-0.798 00.99(-0.033 8~0.012 2)0.012 00.424
间接胆红素(μmol/L)-0.326 2-0.738 00.721(-1.778 8~0.350 8)0.442 10.460
球蛋白(g/L)-0.006 2-0.047 00.993(-0.233 3~0.302 4)0.132 10.962
), ArticleFig(id=1241680453514621540, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Table 4, caption=

Indicator weights

, figureFileSmall=null, figureFileBig=null, tableContent=
指标方向权重权重排序
肾衰0.361 01
下肢动脉病变0.225 12
其他内分泌疾病0.130 73
肾病0.096 44
冠心病0.057 35
高脂血0.039 16
血尿素(mmol/L)0.037 57
其他肿瘤0.016 58
糖化血红蛋白(mmol/L)0.014 49
年龄(岁)0.007 510
内分泌腺瘤0.007 011
妇科肿瘤0.004 812
红细胞压积(红细胞比积测定)(%)0.002 313
凝血酶原时间(s)0.000 314
权重总和1
), ArticleFig(id=1241680453632062058, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=表4, caption=

指标权重

, figureFileSmall=null, figureFileBig=null, tableContent=
指标方向权重权重排序
肾衰0.361 01
下肢动脉病变0.225 12
其他内分泌疾病0.130 73
肾病0.096 44
冠心病0.057 35
高脂血0.039 16
血尿素(mmol/L)0.037 57
其他肿瘤0.016 58
糖化血红蛋白(mmol/L)0.014 49
年龄(岁)0.007 510
内分泌腺瘤0.007 011
妇科肿瘤0.004 812
红细胞压积(红细胞比积测定)(%)0.002 313
凝血酶原时间(s)0.000 314
权重总和1
), ArticleFig(id=1241680453774668409, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Table 5, caption=

Grading results

, figureFileSmall=null, figureFileBig=null, tableContent=
分档总人数患病人数患病率(%)平均Ci
29424482.990.538 9
1 26770655.720.246 7
2954314.580.081 2
), ArticleFig(id=1241680453892108929, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=表5, caption=

分档结果

, figureFileSmall=null, figureFileBig=null, tableContent=
分档总人数患病人数患病率(%)平均Ci
29424482.990.538 9
1 26770655.720.246 7
2954314.580.081 2
), ArticleFig(id=1241680453984383626, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Table 6, caption=

Comparison of single-model prediction results

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模型Acc(%)P(%)R(%)FPR(%)F1AUCTenFold-ACC
Logistic78.5680.3471.3115.150.755 60.831 70.759 2
随机森林78.3780.5370.5414.810.814 70.752 10.752 7
支持向量机79.4679.4671.7113.800.814 80.764 50.762 3
), ArticleFig(id=1241680454152155802, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=表6, caption=

单模型预测效果对比

, figureFileSmall=null, figureFileBig=null, tableContent=
模型Acc(%)P(%)R(%)FPR(%)F1AUCTenFold-ACC
Logistic78.5680.3471.3115.150.755 60.831 70.759 2
随机森林78.3780.5370.5414.810.814 70.752 10.752 7
支持向量机79.4679.4671.7113.800.814 80.764 50.762 3
), ArticleFig(id=1241680454248624801, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=EN, label=Table 7, caption=

Comparison of prediction effects of combining models

, figureFileSmall=null, figureFileBig=null, tableContent=
模型Acc(%)P(%)R(%)FPR(%)F1
投票80.1878.6878.6818.520.786 8
平均79.8281.4773.2614.480.771 4
加权平均79.4681.5871.0914.140.765 4
), ArticleFig(id=1241680454353482407, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522854450230073, language=CN, label=表7, caption=

融合模型预测效果对比

, figureFileSmall=null, figureFileBig=null, tableContent=
模型Acc(%)P(%)R(%)FPR(%)F1
投票80.1878.6878.6818.520.786 8
平均79.8281.4773.2614.480.771 4
加权平均79.4681.5871.0914.140.765 4
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糖尿病并发视网膜病变的关键影响因素分析和风险预测研究
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武巾媛 , 安洪庆
现代预防医学 | 临床与预防 2024,51(3): 557-563
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现代预防医学 | 临床与预防 2024, 51(3): 557-563
糖尿病并发视网膜病变的关键影响因素分析和风险预测研究
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武巾媛, 安洪庆
作者信息
  • 潍坊医学院,山东 潍坊 261053
  • 武巾媛(2003—),女,本科在读,研究方向:大数据管理与应用

通讯作者:

安洪庆,E-mail:
Analysis of key influencing factors and risk prediction of diabetic retinopathy
Jin-yuan WU, Hong-qing AN
Affiliations
  • Weifang Medical College, Weifang, Shandong 261053, China
出版时间: 2024-02-10 doi: 10.20043/j.cnki.MPM.202309033
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目的

探究糖尿病并发视网膜病变(diabetic retinopathy,DR)的关键影响因素,进行发病现状分析,并构建发病风险预测模型。

方法

基于“国家人口与健康科学数据共享平台”公布的“糖尿病并发症预警数据集”,利用单因素和多因素logistic回归分析得到DR发病关键影响因素;运用熵权法、优劣解距离法(technique for order preferenceby similarity to ideal solution,TOPSIS)、联合秩和比方法(rank-sum ratio,RSR)进行发病风险分层;分别构建logistic回归、随机森林、支持向量机模型,使用投票、平均、加权平均三种方法进行模型融合,对模型预测效果进行评估,取得最佳预测模型。

结果

最终提取年龄、高脂血等14个指标作为关键影响因素;分层结果显示未患DR的糖尿病患者中存在50人患病风险较高,约为82.99%,需要重点关注;投票器融合模型预测效果最佳(Acc:80.18%,F1:0.786 8)。

结论

分析得到DR关键影响因素,提供了治疗与预防方向;进行发病风险现状分析,划分DR低中高风险人群,进行风险预警;通过模型间效果对比,构建DR发病风险预测模型,为其临床预警提供了数据分析思路与方法。

糖尿病视网膜病变  /  Logistic  /  TOPSIS  /  RSR  /  预测模型
Objective

To explore the key influencing factors of diabetic retinopathy (DR), analyze the current situation of DR, and construct a risk prediction model.

Methods

Based on the diabetic complication early warning data set published by the national population and health science data sharing platform, the key influencing factors of DR were obtained by univariate and multivariate Logistic regression analyses. The entropy weighting method, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the rank-sum ratio (RSR) were used to quantify the risk of DR development in patients and stratified into three levels: high, medium, and low. Logistic regression, random forest, and support vector machine models were constructed, respectively, and model fusion was performed using voting, averaging, and weighted averaging to evaluate the model predictive effect and obtain the best predictive model.

Results

Finally, 14 indexes including age and hyperlipidemia were extracted as key influencing factors. The stratification results showed that there were 50 diabetic patients without DR in this data set with a risk of about 82.99%, which was a high-risk group for DR and needed more attention. The best prediction effect was obtained from the voting machine fusion model (Acc: 80.18%, F1: 0.7868).

Conclusion

The key influencing factors of DR are analyzed, providing the direction of treatment and prevention. The low, medium, and high-risk groups of DR are classified for risk early warning. By comparing the effect between the models, the prediction model of morbidity risk of DR is constructed, providing insights for clinical early warning and data analysis.

Diabetic retinopathy  /  Logistic  /  TOPSIS  /  RSR  /  Predictive model
武巾媛, 安洪庆. 糖尿病并发视网膜病变的关键影响因素分析和风险预测研究. 现代预防医学, 2024 , 51 (3) : 557 -563 . DOI: 10.20043/j.cnki.MPM.202309033
Jin-yuan WU, Hong-qing AN. Analysis of key influencing factors and risk prediction of diabetic retinopathy[J]. Modern Preventive Medicine, 2024 , 51 (3) : 557 -563 . DOI: 10.20043/j.cnki.MPM.202309033
糖尿病发病率日益增高,统计显示2022年全球已拥有5.5亿糖尿病患者,其中中国糖尿病患者人数超1.4亿,位居世界首位。糖尿病并发视网膜病变(diabetic retinopathy,DR)是糖尿病的严重并发症之一,DR大多数早期症状不明显,目前临床诊断手段主要以荧光素眼底血管造影、超声检查或视网膜电图等眼底图像检测等眼底检查为主,所需费用较高、流程较为复杂[1]。相关研究多运用人工智能方法进行眼底图像识别[2],或仅对关键影响因素进行分析[3],缺乏基于数据的监测预测手段。本研究尝试建立基于病例健康数据的预测模型。相较于图像监测手段,数据监测具有快捷易操作、费用少、可批量操作等优点,有利于疾病病程监测,达到早发现早治疗的目标。
本研究数据来源于公开数据库中国人民解放军总医院国家人口健康科学数据中心数据仓储公开数据集“糖尿病并发症预警数据集”(2013年1月—2017年12月)[4],已通过潍坊医学院医学伦理委员的审查(编号2022YX057)。
选择符合以下条件的2型糖尿病(type 2 diabetic mellitus,T2DM)患者个体作为研究样本:
(1)诊断条件:研究样本为已被临床医生明确诊断为T2DM的患者。确认病例时要求患者至少具备以下两个条件之一:①空腹血糖水平≥7.0 mmol/L;②随机血糖水平≥11.1 mmol/L,并伴有典型的糖尿病症状,如多饮、多尿、疲劳等。
(2)具备与视网膜病变相关的生化指标检查数据,如血压、体重指数(BMI)等。
(3)具备与视网膜病变相关的疾病史数据,包括高血压、高脂血症等。
使用单因素logistic回归初步筛选因变量结局的影响因素,投入多因素logistic回归中矫正混杂因素的影响,筛选出DR关键影响因素;使用熵权TOPSIS联合RSR方法对发病现状进行分析,求得各病例的DR综合发病风险并进行高中低三分层;使用R4.2.2进行预测模型的构建,在1 856例研究对象中,按照7:3的比例随机抽取1 301例作为训练集剩余555例为测试集,分别建立logistic回归模型、随机森林模型、支持向量机模型,并进一步进行模型融合,对比各模型预测效果得到最优模型。
剔除缺失率达到30%以上的个变量,删除含有缺失值的病例,剩余有效样本共1 856例,其中共计993例DR患者,占比约为53.5%,总样本基本情况见表1
使用R4.2.2对进行单因素logistic回归分析,检验水准α=0.1,初步提取DR相关指标共49个,见表2
将DR相关指标投入多因素logistic回归中,其中21个定类变量以哑变量形式代入模型进行回归分析,以第一个值为参考,检验水准α=0.05,得到DR关键影响因素指标共14个,见表3
规定数值越大DR发病风险越高的指标为正向指标(危险因素),反之为负向指标(保护因素)。查阅相关文献并根据多因素logistic回归结果中estimate的值判断指标方向,便于归一化处理。确定年龄[5-7]、凝血酶原时间、冠心病[8]、高脂血[9]、红细胞压积、内分泌肿瘤、妇科肿瘤、其他肿瘤[10]8个指标为DR发病的负向指标,HbA1c[11-13]、其他内分泌疾病、肾病[14]、肾衰、下肢动脉病变、血尿素[15]6个指标为其正向指标。
熵权法指标权重分析。记原始矩阵为Xij,根据极差法归一化公式得到归一化矩阵Zij,正向指标,负向指标。对Zij进行运算得到其概率矩阵,对于每个指标,计算其信息熵pijln(pij)(j=1,2,…,m)与信息效用值dj=1-ej,将其信息效用值归一化得到每个指标对DR发病风险的贡献权重(j=1,2,…,m),指标权重计算结果见表4
TOPSIS联合RSR方法进行发病风险分层。利用如下公式求得各病例到正理想点1的距离、到负理想点0的距离。求得与正理想解的贴进度。用各Ci值替代RSR值,按照Ci值升序排列,计算累计秩次、平均秩次以及百分比数P(P=R/n,最后一项用1/4n矫正),并计算对应概率单位Probit值。根据《常用分档数及对应概率单位表》,依据概率单位的统计学中的正态分布原则,按照Probit值将结果分为患病风险高(Probit≥6)、中(4≤Probit<6)、低(Probit<4)三个档次,分档情况见表5
使用R 4.2.2,将Ci值作为因变量、Probit值作为自变量拟合线性回归方程Ci=0.149 2,Probit-0.478 8,方差分析F=14 958(on 1 and 1 854 DF),R2=0.889 7,拟合效果良好,P<0.001,认为三档间的Ci值差异具有统计学意义[16]。低档Ci均值为0.081 2、中档为0.246 7、高档为0.538 9,差异较大,说明高中低三档发病风险存在较大差异。根据分档结果,未患DR的863名T2DM中,有252人DR发病率约为14.58%,为低风险人群;561人患病风险约为55.72%,为中风险人群;50人患病风险约为82.99%,为高风险人群,需要重点关注。
对于logistic模型,采用默认的参数设置,包括L2正则化(默认设置)和逻辑回归损失函数;对于随机森林模型,由图1可知,ntree数量为300时,模型错误率稳定在27%左右,因此设置决策树的数量ntree=300以平衡模型的复杂性和性能;对于SVM模型,采用默认的参数设置,包括核函数类型(kernel)、正则化参数C以及核函数的其他参数(例如,多项式核的次数)。
使用准确率(Acc)、精确率(P)、召回率(R)、F1分数、假正率(FPR)、受试者工作曲线下面积(AUC)对模型预测效果进行评估,结果见表6。支持向量机模型的预测准确率要优于另外两个模型,同时其具有更高的召回率,在临床应用中误把DR患者诊断为正常的概率更小。使用trControl函数进行十折交叉验证,准确率平均值分别为0.759 2、0.752 7、0.762 3,支持向量机模型预测效果更为稳定。
投票,即单模型预测结果中占多数的为最终预测结果;平均,即将单模型预测结果为1的概率值取平均,大于0.5则分类为1。加权平均,即单模型预测准确率降序排序,依次为支持向量机、随机森林、logistic,降序编秩分别为3、2、1,秩和为6,分别赋权为1/2、1/3、6/1,对预测概率进行加权平均得到最终概率。三模型预测效果对比见表7,可知三个融合模型各方面预测效果相较于单模型均有所提高,投票器模型的准确与F1值最高,综合预测效果最好。
DR的发生受多种因素共同作用,本研究使用单因素和多因素logistic回归确定14个指标为DR关键影响因素:年龄、高脂血、肾病、肾衰、冠心病、下肢动脉病变、其他内分泌疾病、其他肿瘤、糖化血红蛋白、血尿素、红细胞压积、凝血酶原时间、内分泌腺瘤、妇科肿瘤;运用熵权TOPSIS联合RSR方法进行现状分析。将1 856条有效数据按照DR患病风险划分为三层,确定未患DR的T2DM患者中存在50人DR患病风险较高,约为82.99%,需要重点关注;进行logistic、随机森林、支持向量机预测模型对比融合,三者的投票器模型预测效果最佳,准确率达到80.18%,同时其他指标表现均较优。
本研究存在一定局限性。一是数据集中未收录患者糖尿病病程、遗传、环境等方面指标,所研究关键影响因素仅限于生化与其他病症指标。二是本研究所提取有效指标均为时点指标,不能反应指标波动情况对DR的影响,仍需进一步探索。本研究预测结果存在不确定性:(1)样本不确定性:样本数据的选择可能会对模型的泛化性能产生一定程度的不确定性,进一步研究可以包括更多地区、不同来源的样本,以增加模型的稳健性和泛化能力。(2)特征不确定性:模型的性能和效果通常受到所选特征的质量和相关性的影响。虽然我们已经通过单因素和多因素回归分析筛选了关键影响因素,但特征选择本身可能存在不确定性。未来的研究可以探索其他可能影响DR的因素,以提高模型的预测性能。(3)模型不确定性:模型的构建和融合过程中,不同机器学习算法和融合方法可能导致不同的结果。在研究中,我们使用了logistic回归、随机森林和支持向量机等多个模型,并进行了模型融合。虽然投票器模型在预测效果上表现最佳,但不同模型之间的选择也会引入一定程度的不确定性。(4)预测不确定性:模型的预测结果通常伴随着一定的不确定性,这取决于测试集的分布以及模型的性能。本研究使用十折交叉验证方法来评估模型的稳定性和预测不确定性,后续研究中可通过增加数据量、改进特征工程、使用更复杂的模型、改进数据质量控制等提高模型预测效果稳定性。
  • 中国学位与研究生教育学会课题(2020MSA105)
  • 中国高等教育学会2023年度课题(23PG0411)
  • 山东省高等医学教育研究中心规划课题(YJKT202126)
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doi: 10.20043/j.cnki.MPM.202309033
  • 接收时间:2023-09-03
  • 首发时间:2026-03-19
  • 出版时间:2024-02-10
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  • 收稿日期:2023-09-03
基金
中国学位与研究生教育学会课题(2020MSA105)
中国高等教育学会2023年度课题(23PG0411)
山东省高等医学教育研究中心规划课题(YJKT202126)
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    潍坊医学院,山东 潍坊 261053

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
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红菇属 Russula 17 8.13
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