Article(id=1322927402208330357, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924976893993108, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.16.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758988800000, receivedDateStr=2025-09-28, revisedDate=1785081600000, revisedDateStr=2026-07-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340071488, onlineDateStr=2026-10-30, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340071488, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340071488, creator=13701087609, updateTime=1793340071488, updator=13701087609, issue=Issue{id=1322924976893993108, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='16', pageStart='2769', pageEnd='2960', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793339493249, creator='13701087609', updateTime=1793343103544, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940119640047671, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924976893993108, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940119640047672, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924976893993108, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2824, endPage=2830, ext={EN=ArticleExt(id=1322927402459988598, articleId=1322927402208330357, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction of a predictive model for depression in patients with hepatitis B-related liver cirrhosis, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the risk factors for depression in patients with hepatitis B-related cirrhosis(HBC),to establish a decision tree prediction model,and to formulate nursing interventions to reduce the risk of depression in HBC patients.

Methods

A total of 200 HBC patients admitted to our hospital from January 2023 to January 2025 were selected as study subjects.Logistic regression analysis was used to identify risk factors influencing the occurrence of depression in HBC patients.A decision tree model for depression occurrence was constructed using the Chi-squared automatic interaction detection(CHAID) algorithm.The predictive performance of the decision tree model was evaluated by plotting the receiver operating characteristic(ROC) curve and calculating the area under the curve(AUC).

Results

According to the Hamilton Depression Scale(HAMD) scores,all patients were divided into the depression disorder group with 58 cases and the non-depression disorder group with 142 cases.Logistic regression analysis showed that the education level,family support,psychological resilience,disease duration,and Child-Pugh classification were all risk factors for depression in HBC patients(P<0.05).The decision tree model identified 5 explanatory variables,namely Child-Pugh classification,psychological resilience,family care level,disease duration,and educational attainment.Among them,the Child-Pugh classification was the most significant influencing factor for the occurrence of depressive disorders in HBC patients.The AUC of the decision tree model was 0.930.After ten-fold cross-validation, the average AUC of the model was 0.914,which showed a relatively small difference from the original model's AUC.There was no significant overfitting in the model.The generalization ability was good.The Hosmer-Lemeshow test results showed that there was no statistically significant difference between the predicted risk of depression disorder by the model and the actual disease status of the patients.The calibration was good.Using HAMD ≥17 as the diagnostic threshold for depression,the model showed a sensitivity of 87.93%,specificity of 91.55%,and Youden's index of 0.795.

Conclusions

Education level,family support,psychological resilience,disease duration,and Child-Pugh classification are important risk factors for depression in HBC patients.The decision tree model based on these factors demonstrates high predictive value.Clinical practice should develop targeted nursing interventions addressing the above risk factors to reduce the incidence of depression in HBC patients.

, authors=Li ZHAO, Chao LI, authorsList=Li ZHAO, Chao LI, authorCompany=null, correspAuthors=Chao LI, 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, fund=null), CN=ArticleExt(id=1322927403516953214, articleId=1322927402208330357, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=乙型肝炎肝硬化病人抑郁障碍预测模型的构建, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

分析乙型肝炎肝硬化病人发生抑郁障碍的危险因素,构建决策树预测模型,并制定护理对策以降低乙型肝炎肝硬化病人抑郁障碍发生风险。

方法

选取2023年1月—2025年1月我院收治的200例乙型肝炎肝硬化病人为研究对象。采用Logistic回归分析筛选乙型肝炎肝硬化病人抑郁障碍发生的危险因素,以卡方自动交互检验(CHAID)算法构建乙型肝炎肝硬化病人抑郁障碍发生的决策树模型,并绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC)评估决策树模型的预测效能。

结果

根据汉密尔顿抑郁量表(HAMD)评分将病人分为抑郁障碍组58例和非抑郁障碍组142例。Logistic回归分析显示,文化程度、家庭关怀度、心理弹性、病程、Child-Pugh分级为乙型肝炎肝硬化病人发生抑郁障碍的影响因素(P<0.05)。决策树模型筛选出5个解释变量,即Child-Pugh分级、心理弹性、家庭关怀度、病程、文化程度,其中Child-Pugh分级是乙型肝炎肝硬化病人抑郁障碍发生最重要的影响因素。决策树模型AUC为0.930,经十折交叉验证后,模型的平均AUC为0.914,与原始模型AUC差异较小,表明模型无明显过拟合现象,泛化能力良好。Hosmer-Lemeshow检验结果显示模型预测的抑郁障碍发生风险与病人实际发病情况比较差异无统计学意义,校准度良好。以HAMD≥17分为抑郁障碍阳性判定标准,模型灵敏度为87.93%,特异度为91.55%,Youden指数为0.795。

结论

文化程度、家庭关怀度、心理弹性、病程及Child-Pugh分级是乙型肝炎肝硬化病人抑郁障碍发生的影响因素,基于这些因素构建的决策树模型具有较高的预测价值。临床可针对危险因素制定相应护理对策,以降低乙型肝炎肝硬化病人抑郁障碍的发生风险。

, authors=赵丽, 李超*, *, authorsList=赵丽, 李超, authorCompany=null, correspAuthors=李超, authorNote=

赵丽,主管护师,本科

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李超,E-mail:
LI Chao, E-mail:
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predictive model for hope level in patients with primary liver cancer from a positive psychology perspective, refAbstract=null), Reference(id=1322927411288998586, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, doi=null, pmid=null, pmcid=null, year=2022, volume=17, issue=12, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=25, rfOrder=24, authorNames=Seo G H, Yoo J J, journalName=PLoS One, refType=null, unstructuredReference=Seo G H,Yoo J J.Incidence of major depressive disorder over time in patients with liver cirrhosis:a nationwide population-based study in Korea[J].PLoS One,2022,17(12):e0278924., articleTitle=Incidence of major depressive disorder over time in patients with liver cirrhosis:a nationwide population-based study in Korea, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322927403743445631, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, xref=1, 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tableContent=null), ArticleFig(id=1322927407002419865, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, language=CN, label=图1, caption=乙型肝炎肝硬化病人抑郁障碍风险预测的决策树模型, figureFileSmall=t8cEtMDcDReJZBZMb6XqdQ==, figureFileBig=gkdEyaPVV9pjBNsez8WXyQ==, tableContent=null), ArticleFig(id=1322927407199552154, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, language=EN, label=Figure 2, caption=Importance of predicting risk variables for depression in patients with hepatitis B-related liver cirrhosis, figureFileSmall=adLurWpKzeeRZ4bvylwjoQ==, figureFileBig=9u6uWpQuMwXLII4BWbEZcQ==, tableContent=null), ArticleFig(id=1322927407312798363, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, language=CN, label=图2, caption=乙型肝炎肝硬化病人抑郁障碍发生风险变量的重要性, figureFileSmall=adLurWpKzeeRZ4bvylwjoQ==, figureFileBig=9u6uWpQuMwXLII4BWbEZcQ==, tableContent=null), ArticleFig(id=1322927407388295836, 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Comparison of clinical data between depressive disorder group and non-depressive disorder group

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类抑郁障碍组(n=58)非抑郁障碍组(n=142)统计值P
性别男2052χ2=0.0820.775
女3890
年龄≥60岁3260χ2=2.7670.096
<60岁2682
BMI<18 kg/m21040Z=0.6700.500
18~24 kg/m23060
>24 kg/m21842
吸烟史是4189χ2=1.1620.281
否1753
饮酒史是2551χ2=0.9030.342
否3391
文化程度专科以下3356χ2=5.0830.024
专科及以上2586
婚姻状况有配偶56138χ2=0.0560.812
无配偶24
家庭人均月收入≥5 000元2376χ2=3.1670.075
<5 000元3566
居住地城镇1954χ2=0.4930.482
农村3988
家庭关怀度高1442Z=2.4000.016
中等1865
低2635
心理弹性良好2080χ2=7.8680.005
差3862
病程≤5年2185χ2=9.2480.002
>5年3757
合并高血压是2374χ2=2.5590.110
否3568
合并糖尿病是814χ2=0.6510.420
否50128
Child-Pugh分级轻度1150Z=3.2500.001
中度2060
重度2732
), ArticleFig(id=1322927407627371167, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, language=CN, label=表1, caption=

抑郁障碍组和非抑郁障碍组病人临床资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类抑郁障碍组(n=58)非抑郁障碍组(n=142)统计值P
性别男2052χ2=0.0820.775
女3890
年龄≥60岁3260χ2=2.7670.096
<60岁2682
BMI<18 kg/m21040Z=0.6700.500
18~24 kg/m23060
>24 kg/m21842
吸烟史是4189χ2=1.1620.281
否1753
饮酒史是2551χ2=0.9030.342
否3391
文化程度专科以下3356χ2=5.0830.024
专科及以上2586
婚姻状况有配偶56138χ2=0.0560.812
无配偶24
家庭人均月收入≥5 000元2376χ2=3.1670.075
<5 000元3566
居住地城镇1954χ2=0.4930.482
农村3988
家庭关怀度高1442Z=2.4000.016
中等1865
低2635
心理弹性良好2080χ2=7.8680.005
差3862
病程≤5年2185χ2=9.2480.002
>5年3757
合并高血压是2374χ2=2.5590.110
否3568
合并糖尿病是814χ2=0.6510.420
否50128
Child-Pugh分级轻度1150Z=3.2500.001
中度2060
重度2732
), ArticleFig(id=1322927407719645856, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, language=EN, label=Table 2, caption=

Logistic regression analysis of depression in patients with hepatitis B-related liver cirrhosis

, figureFileSmall=null, figureFileBig=null, tableContent=
变量回归系数标准误Wald χ2值POR值95%CI
文化程度(专科以下)1.4020.4509.7070.0124.063[2.558,5.569]
家庭关怀度(低)1.1660.29915.207<0.0013.209[1.039,5.379]
心理弹性(差)1.4820.38010.8900.0104.398[2.091,9.526]
病程(>5年)0.8540.3366.4600.0182.349[1.611,3.087]
Child-Pugh分级(重度)0.9210.25213.3570.0052.512[1.799,3.225]
), ArticleFig(id=1322927407824503457, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927402208330357, language=CN, label=表2, caption=

乙型肝炎肝硬化病人发生抑郁障碍的Logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量回归系数标准误Wald χ2值POR值95%CI
文化程度(专科以下)1.4020.4509.7070.0124.063[2.558,5.569]
家庭关怀度(低)1.1660.29915.207<0.0013.209[1.039,5.379]
心理弹性(差)1.4820.38010.8900.0104.398[2.091,9.526]
病程(>5年)0.8540.3366.4600.0182.349[1.611,3.087]
Child-Pugh分级(重度)0.9210.25213.3570.0052.512[1.799,3.225]
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乙型肝炎肝硬化病人抑郁障碍预测模型的构建
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赵丽 , 李超 *, *
护理研究 | 科研论著 2026,40(16): 2824-2830
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护理研究 |科研论著 2026 , 40 (16) : 2824 -2830
乙型肝炎肝硬化病人抑郁障碍预测模型的构建
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赵丽, 李超*, *
作者信息
  • 1沧州市人民医院,河北 061000
通讯作者:
李超,E-mail:
LI Chao, E-mail:
作者简介:

赵丽,主管护师,本科

Construction of a predictive model for depression in patients with hepatitis B-related liver cirrhosis
Li ZHAO, Chao LI
Affiliations
  • 1Cangzhou People's Hospital, Hebei 061000 China
出版时间: 2026-08-25 doi: 10.12102/j.issn.1009-6493.2026.16.008
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目的

分析乙型肝炎肝硬化病人发生抑郁障碍的危险因素,构建决策树预测模型,并制定护理对策以降低乙型肝炎肝硬化病人抑郁障碍发生风险。

方法

选取2023年1月—2025年1月我院收治的200例乙型肝炎肝硬化病人为研究对象。采用Logistic回归分析筛选乙型肝炎肝硬化病人抑郁障碍发生的危险因素,以卡方自动交互检验(CHAID)算法构建乙型肝炎肝硬化病人抑郁障碍发生的决策树模型,并绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC)评估决策树模型的预测效能。

结果

根据汉密尔顿抑郁量表(HAMD)评分将病人分为抑郁障碍组58例和非抑郁障碍组142例。Logistic回归分析显示,文化程度、家庭关怀度、心理弹性、病程、Child-Pugh分级为乙型肝炎肝硬化病人发生抑郁障碍的影响因素(P<0.05)。决策树模型筛选出5个解释变量,即Child-Pugh分级、心理弹性、家庭关怀度、病程、文化程度,其中Child-Pugh分级是乙型肝炎肝硬化病人抑郁障碍发生最重要的影响因素。决策树模型AUC为0.930,经十折交叉验证后,模型的平均AUC为0.914,与原始模型AUC差异较小,表明模型无明显过拟合现象,泛化能力良好。Hosmer-Lemeshow检验结果显示模型预测的抑郁障碍发生风险与病人实际发病情况比较差异无统计学意义,校准度良好。以HAMD≥17分为抑郁障碍阳性判定标准,模型灵敏度为87.93%,特异度为91.55%,Youden指数为0.795。

结论

文化程度、家庭关怀度、心理弹性、病程及Child-Pugh分级是乙型肝炎肝硬化病人抑郁障碍发生的影响因素,基于这些因素构建的决策树模型具有较高的预测价值。临床可针对危险因素制定相应护理对策,以降低乙型肝炎肝硬化病人抑郁障碍的发生风险。

乙型肝炎  /  肝硬化  /  抑郁  /  危险因素  /  决策树  /  预测模型
Objective

To analyze the risk factors for depression in patients with hepatitis B-related cirrhosis(HBC),to establish a decision tree prediction model,and to formulate nursing interventions to reduce the risk of depression in HBC patients.

Methods

A total of 200 HBC patients admitted to our hospital from January 2023 to January 2025 were selected as study subjects.Logistic regression analysis was used to identify risk factors influencing the occurrence of depression in HBC patients.A decision tree model for depression occurrence was constructed using the Chi-squared automatic interaction detection(CHAID) algorithm.The predictive performance of the decision tree model was evaluated by plotting the receiver operating characteristic(ROC) curve and calculating the area under the curve(AUC).

Results

According to the Hamilton Depression Scale(HAMD) scores,all patients were divided into the depression disorder group with 58 cases and the non-depression disorder group with 142 cases.Logistic regression analysis showed that the education level,family support,psychological resilience,disease duration,and Child-Pugh classification were all risk factors for depression in HBC patients(P<0.05).The decision tree model identified 5 explanatory variables,namely Child-Pugh classification,psychological resilience,family care level,disease duration,and educational attainment.Among them,the Child-Pugh classification was the most significant influencing factor for the occurrence of depressive disorders in HBC patients.The AUC of the decision tree model was 0.930.After ten-fold cross-validation, the average AUC of the model was 0.914,which showed a relatively small difference from the original model's AUC.There was no significant overfitting in the model.The generalization ability was good.The Hosmer-Lemeshow test results showed that there was no statistically significant difference between the predicted risk of depression disorder by the model and the actual disease status of the patients.The calibration was good.Using HAMD ≥17 as the diagnostic threshold for depression,the model showed a sensitivity of 87.93%,specificity of 91.55%,and Youden's index of 0.795.

Conclusions

Education level,family support,psychological resilience,disease duration,and Child-Pugh classification are important risk factors for depression in HBC patients.The decision tree model based on these factors demonstrates high predictive value.Clinical practice should develop targeted nursing interventions addressing the above risk factors to reduce the incidence of depression in HBC patients.

hepatitis B  /  liver cirrhosis  /  depression  /  risk factors  /  decision tree  /  prediction model
赵丽, 李超. 乙型肝炎肝硬化病人抑郁障碍预测模型的构建. 护理研究, 2026 , 40 (16) : 2824 -2830 . DOI: 10.12102/j.issn.1009-6493.2026.16.008
Li ZHAO, Chao LI. Construction of a predictive model for depression in patients with hepatitis B-related liver cirrhosis[J]. Chinese Nursing Research, 2026 , 40 (16) : 2824 -2830 . DOI: 10.12102/j.issn.1009-6493.2026.16.008
乙型肝炎肝硬化作为慢性乙型肝炎进展的终末阶段,全球每年新增约100万病例,我国乙型肝炎病毒携带者数量较多,使得该病防治形势尤为严峻[1]。乙型肝炎肝硬化的不可逆性、长期治疗负担及并发症风险,使病人普遍承受生理与心理的双重压力[2]。研究显示,乙型肝炎肝硬化病人抑郁障碍高发,这种心理状态不仅降低病人生活质量,还会干扰治疗依从性,加速疾病进展,形成恶性循环[3]。当前,对乙型肝炎肝硬化病人抑郁的相关研究多采用传统统计学方法[4],难以全面解析多因素交互作用下的复杂关联。决策树模型作为数据挖掘领域的经典算法,通过构建树形结构可视化呈现变量间逻辑关系,可有效识别关键影响因素,突破传统线性分析的局限性[5]。同时,精准定位危险因素是制定针对性护理干预策略的前提,对改善病人心理健康、提升临床结局具有重要意义。因此,本研究基于决策树模型,系统分析乙型肝炎肝硬化病人抑郁障碍发生的危险因素,并据此提出护理对策,旨在为临床早期识别高危人群、优化护理方案提供科学依据。
选取2023年1月—2025年1月我院收治的200例乙型肝炎肝硬化病人为研究对象。纳入标准:1)符合慢性乙型肝炎[6]和肝硬化[7]的诊断标准;2)年龄≥18岁;3)意识状态良好,具备基本的语言沟通与认知理解能力;4)在研究开展前已充分了解相关事项并签署知情同意书。排除标准:1)具有精神障碍遗传背景或曾确诊精神类疾病者;2)出现意识水平下降或认知功能损害者;3)长期乙醇滥用、毒品成瘾或存在药物依赖者;4)伴发重要器官功能不全或造血系统异常者;5)确诊肝细胞癌或其他恶性占位性病变者;6)同时感染人类免疫缺陷病毒(HIV)、丙型肝炎病毒(HCV)、丁型肝炎病毒(HDV)、戊型肝炎病毒(HEV)等其他类型肝炎病毒者。本研究通过沧州市人民医院医学伦理委员会审批[编号:K2021-批件-036(5.25)]。
基于多因素Logistic回归分析及决策树模型构建规范,采用PASS 15.0统计软件完成样本量估算。参考既往同类研究[4],乙型肝炎肝硬化病人抑郁障碍发生率为20%~30%,本研究预估值取29%(与最终实际发生率一致);结合前期预试验关于乙型肝炎肝硬化病人抑郁危险因素的研究结果,设定核心暴露因素(如Child-Pugh分级重度、心理弹性差)的预期比值比(OR)≥2.5,以反映因素与抑郁障碍间的中等强度关联;同时遵循医学流行病学研究常用标准,设定检验效能(1-β)为80%(确保能有效检测出设定的OR值关联),α=0.05(双侧检验),平衡Ⅰ类与Ⅱ类错误风险;此外,考虑多因素Logistic回归需满足暴露因素数量的10~20倍的样本量要求(本研究纳入5个独立危险因素,按最低10倍标准需至少50例病例),同时预留10%样本量应对可能的失访与数据缺失,最终计算得出所需最小样本量为182例,本研究实际纳入200例,满足统计学分析及模型构建的样本量要求。
病人入院24 h内采用汉密尔顿抑郁量表(HAMD)[8]进行标准化测评,HAMD包括17项评估指标,采用5级(0~4分)评分法,总分为0~68分,得分与抑郁症状的严重程度呈正相关。依据国际通用标准,将测评结果分为4个等级,得分<7分为正常,7~16分提示存在抑郁倾向,≥17分可确诊为抑郁障碍,≥24分为重度抑郁。基于上述诊断标准,本研究将200例研究对象分为抑郁障碍组(n=58)和非抑郁障碍组(n=142)。所有诊断均经过临床症状与量表评分的双重验证。
统计病人性别、年龄、体质指数(BMI)、吸烟史、饮酒史、文化程度、婚姻状况、家庭人均月收入、居住地、家庭关怀度、心理弹性、病程、合并症(高血压、糖尿病)、Child-Pugh分级。家庭关怀度采用家庭关怀指数问卷(APGAR)[9]评估,该量表总分10分,评分结果与家庭关怀水平呈正相关,0~3分代表家庭关怀度低,4~6分代表家庭关怀度中等,7~10分代表家庭关怀度高。心理弹性采用Connor-Davidson心理弹性量表(CD-RISC)[10]评估,该量表总分为100分,得分与心理弹性水平呈正相关,得分≥80分为心理弹性良好,<80分者视为心理弹性差。
实施前对所有参与人员进行了系统化培训。数据采集工作由2名专职人员共同完成,依据研究方案设定的纳入和排除条件筛选合格病例;通过医院信息系统批量提取选定病例的完整资料;对获取的数据进行完整性检查。在数据核查过程中,发现信息不完整或存在疑问时,由1名研究人员查阅原始电子病历记录进行补充确认,另1名研究人员则负责独立验证,确保数据真实可靠。
采用SPSS 28.0软件对数据进行统计分析。符合正态分布的定量资料以均数±标准差(x±s)表示,不符合正态分布的定量资料以中位数(四分位数)[M(P25,P75)]表示。定性资料用频数、百分比(%)表示,采用χ2检验或非参数检验。采用Logistic回归分析筛选影响因素,采用卡方自动交互检验(CHAID)算法构建决策树模型,构建过程中设定剪枝参数,父节点最小样本量为30,子节点最小样本量为15。绘制受试者工作特征(ROC)曲线,计算ROC曲线下面积(AUC),评价决策树模型的预测效能。
200例乙型肝炎肝硬化病人中,男72例,女128例;年龄≥60岁92例,<60岁108例;文化程度∶专科以下89例,专科及以上111例;有配偶194例。HAMD评分≥17分58例,为抑郁障碍组,其中重度抑郁10例;HAMD评分<17分142例,为非抑郁障碍组。
抑郁障碍组和非抑郁障碍组文化程度、家庭关怀度、心理弹性、病程、Child-Pugh分级比较,差异均有统计学意义(P<0.05)。见表1。
在进行Logistic回归分析前,为避免变量间多重共线性对结果准确性的干扰,先对纳入的5个自变量(文化程度、家庭关怀度、心理弹性、病程、Child-Pugh分级)进行多重共线性检验,通过计算方差膨胀因子(VIF)和容忍度评估共线性程度。结果显示,所有自变量的VIF值为1.02~1.35,远低于VIF>10的共线性判定阈值,容忍度为0.74~0.98,远高于容忍度<0.1的共线性判定阈值,表明各变量间不存在多重共线性,符合多因素Logistic回归分析的前提条件。将乙型肝炎肝硬化病人是否发生抑郁障碍为因变量(是=1,否=0),以单因素分析中差异有统计学意义的变量为自变量,自变量赋值为:文化程度(专科以下=1,专科及以上=0),家庭关怀度(低=2,中度=1,高=0),心理弹性(良好=0,差=1),病程(≤5年=0,>5年=1),Child-Pugh分级(轻度=0,中度=1,重度=2)。结果显示,文化程度、家庭关怀度、心理弹性、Child-Pugh分级、病程为乙型肝炎肝硬化病人抑郁障碍发生的危险因素(P<0.05)。见表2。
将单因素分析中差异有统计学意义的项目(文化程度、家庭关怀度、心理弹性、病程、Child-Pugh分级)纳入决策树模型,决策树模型见图1。该树形图包括3层,共10个节点,4个终结点,筛选出5个解释变量,即Child-Pugh分级、心理弹性、家庭关怀度、病程、文化程度,其中Child-Pugh分级是乙型肝炎肝硬化病人抑郁障碍发生最重要的影响因素,文化程度对乙型肝炎肝硬化病人抑郁障碍发生的影响最小。变量重要性计算采用χ²检验算法,通过量化各变量在决策树分支过程中对“抑郁障碍”结局变量的解释力,将重要性数值归一化为0~1,结果显示,Child-Pugh分级、心理弹性、家庭关怀度、病程、文化程度的预测变量重要性分别为0.30,0.27,0.25,0.22,0.11,见图2。
采用SPSS 28.0软件对决策树模型进行全面效能评估,结果显示,决策树模型的AUC为0.930(0.895,0.965),经十折交叉验证后,模型平均AUC为0.914(0.875,0.953),与原始模型AUC差异较小,表明模型无明显过拟合现象,泛化能力良好。Hosmer-Lemeshow检验结果显示,χ²=5.872,P=0.663,提示模型预测的抑郁障碍发生风险与病人实际发病情况比较差异无统计学意义,校准度良好。进一步以HAMD≥17分为抑郁障碍阳性判定标准,计算模型预测性能指标,灵敏度为87.93%(51/58),特异度为91.55%(130/142),Youden指数为0.795。见图3。
乙型肝炎肝硬化病人抑郁障碍的发生与多种因素密切相关,本研究通过Logistic回归和决策树模型系统分析了乙型肝炎肝硬化病人抑郁障碍发生的危险因素,结果显示,文化程度、家庭关怀度、心理弹性、病程及Child-Pugh分级是其主要影响因素,其中Child-Pugh分级的预测重要性最高。Child-Pugh分级作为评估肝硬化严重程度的经典指标,其重度提示肝功能损伤已进入失代偿期,病人常伴随腹水、黄疸、肝性脑病等并发症,躯体痛苦的加剧直接降低生活质量,进而诱发抑郁情绪[11-13]。从病理生理机制来看,Child-Pugh重度病人肝功能严重受损,肝脏清除炎症因子的能力显著下降,导致血清中白细胞介素-6(IL-6)、肿瘤坏死因子-α(TNF-α)等促炎因子大量蓄积,这些炎症因子可通过血液循环突破血脑屏障,激活中枢神经系统的炎症反应,干扰5-羟色胺、多巴胺等神经递质的合成与代谢,最终引发或加重抑郁症状[14-15]。同时,肝功能失代偿还会破坏肠黏膜屏障完整性,导致肠道菌群失调及内毒素入血,通过“肠-脑轴”通路进一步加剧中枢炎症反应与神经功能紊乱,形成“肝功能损伤—炎症激活—肠脑轴异常—抑郁情绪”的恶性循环[16]。与吴天军等[17]提出的肝硬化严重程度与心理障碍呈正相关的观点一致,同时本研究进一步通过决策树模型量化了其权重,为临床识别高风险人群提供了更精准的依据。
从社会心理层面分析,家庭关怀度和心理弹性的高权重值揭示了心理干预在乙型肝炎肝硬化病人护理中的核心地位。家庭作为病人最主要的支持系统,其关怀缺失会导致病人产生被抛弃感和孤独感,而心理弹性差则削弱了个体应对疾病应激的能力,二者共同构成抑郁障碍的重要诱因[18-19]。有研究表明,共情护理联合延续性护理可以改善病毒性肝炎肝硬化病人心理弹性和心理状态,进而提高其自我护理能力和生命质量[20]。本研究的创新点在于通过决策树模型,发现当病人同时存在家庭关怀度低和心理弹性差时,抑郁障碍发生率较高,这一交互效应提示临床护理需采用“家庭-个体”双维度干预策略。此外,文化程度的影响相对较弱,可能与高文化程度病人获取医疗信息的渠道更广泛、对疾病的认知更理性有关[21],但仍需注意其在低文化群体中的针对性干预。
本研究构建的决策树模型具有3层结构和10个节点,AUC值为0.930,经十折交叉验证后,模型的平均AUC为0.914,与原始模型AUC差异较小,表明模型无明显过拟合现象,泛化能力良好,具有优异的预测效能。Hosmer-Lemeshow检验结果显示,χ²=5.872,P=0.663,提示模型预测的抑郁障碍发生风险与病人实际发病情况差异无统计学意义,校准度良好。相较于传统的Logistic回归,决策树模型通过分层递进的节点设计,更直观地展示了各因素的交互作用[22-23]。如模型显示当Child-Pugh分级为重度时,无论其他因素如何,病人抑郁障碍发生风险已升至45.8%,而当分级为轻度但心理弹性差时,风险仍可达57.1%,这为临床制定阶梯式干预方案提供了清晰路径。该模型的高准确性与Sun等[24]构建肝病病人心理风险模型结果(AUC=0.931)相近。本研究将病程>5年作为独立节点纳入,揭示了长期疾病困扰对心理状态的累积效应。长期慢性病会通过持续的生理不适、治疗压力及社会功能受限等多重途径,逐步削弱病人心理耐受能力,增加负性情绪发生风险,这与慢性肝病领域相关研究结论一致。有研究指出,肝硬化病人病程越长,其对疾病的心理负担越重,焦虑、抑郁等情绪障碍发生率显著升高[25]。而此前针对乙型肝炎肝硬化病人抑郁障碍的研究多聚焦疾病严重程度、心理弹性等因素,较少将病程作为独立影响节点深入分析[4,17]。
在生理护理层面,建立每日肝功能指标动态监测表,重点跟踪胆红素、血清白蛋白及凝血功能,对出现腹水病人采用体位护理(床头抬高30°)+每日腹围测量+利尿剂剂量精准调整方案,减少躯体不适。在心理干预层面,每周开展2次一对一心理咨询(每次40 min),采用情绪疏导+疾病认知重构模式,同时教会病人腹式呼吸放松法(每日早晚各1次,每次15 min),通过生理放松缓解焦虑抑郁情绪,且每个月评估1次心理状态,根据HAMD评分调整干预强度。
分3个月实施系统化干预,第1个月开展心理弹性认知课程(每周1次集体课,每次60 min),帮助病人识别自身应对疾病的优势资源;第2个月引入认知行为疗法(CBT),每周1次个体辅导,针对“疾病无法治愈”“治疗无意义”等负性认知进行重构,每次辅导后布置“积极事件记录”作业(每日记录1件疾病相关的积极事件,如指标稳定、食欲改善);第3个月开展正念减压训练(MBSR),通过身体扫描、正念行走等练习(每日20 min),提升病人对负性情绪的接纳能力,全程采用CD-RISC量表每个月评估1次,确保CD-RISC评分提升至60分以上。
首先,开展家属专项培训(每个月1次,每次90 min),内容包括乙型肝炎肝硬化疾病知识、抑郁情绪识别方法(如观察病人睡眠、食欲变化及情绪低落持续时间)、家庭沟通技巧(如“倾听-共情-鼓励”三步沟通法)。其次,建立家庭关怀任务清单,明确家属每日需完成的关怀行为,如“与病人交流15 min疾病外话题”“协助病人完成1次放松训练”。最后,每个月组织1次家庭会议,由护士参与指导,帮助病人与家属表达需求、化解矛盾,通过APGAR问卷每个月评估家庭关怀度,目标提升至7分以上(高关怀水平)。
针对长病程病人,制作疾病病程阶段手册,用时间轴形式标注不同病程阶段的常见症状、应对方法及复查时间,3个月更新1次手册内容,同时建立长期治疗激励机制,如连续6个月规律服药、按时复查者,给予“健康积分”(可兑换体检项目或健康咨询服务)。针对文化程度专科以下病人,采用图文+视频形式制作健康宣教材料(如将抗病毒药物服用注意事项制成漫画手册,将饮食禁忌拍成3 min短视频),每个月开展1次一对一宣教问答(每次30 min),通过提问、反馈确保病人掌握核心知识,同时采用服药提醒卡(贴于家中显眼位置)、手机闹钟提醒等方式,提高治疗依从性,2个月评估1次依从性(采用Morisky服药依从性量表),目标达到高依从性水平(量表评分≥6分)。
本研究存在一定的局限性。首先,HAMD量表评估由本研究的研究者执行,虽经过统一培训,但评估过程未设盲法,即研究者在进行抑郁评分时已知晓病人的Child-Pugh分级等临床资料,此种情况可能导致评估偏倚。未来研究可采用盲法设计,由不知晓病人其他临床信息的研究人员进行心理量表评估,以减小潜在偏倚。其次,本研究为单中心研究,样本量有限,结论的外推性需更多大样本、多中心研究进一步验证。
本研究显示,文化程度、家庭关怀度、心理弹性、病程及Child-Pugh分级是乙型肝炎肝硬化病人发生抑郁障碍的重要危险因素,基于这些因素构建的决策树模型具有较高的预测价值。本研究为临床护理提供了科学依据,建议采取多维度干预策略,包括肝功能优化、心理支持、家庭干预及健康教育等,以降低乙型肝炎肝硬化病人抑郁障碍的发生风险。本研究的局限性在于样本量较小,且为单中心研究,未来可扩大样本量并开展多中心研究,进一步验证模型的普适性。此外,可探索更多潜在影响因素(如经济状况、社会支持网络等),以优化预测模型的准确性,为临床护理提供更精准的决策支持。

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2026年第40卷第16期
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doi: 10.12102/j.issn.1009-6493.2026.16.008
  • 接收时间:2025-09-28
  • 首发时间:2026-10-30
  • 出版时间:2026-08-25
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  • 收稿日期:2025-09-28
  • 修回日期:2026-07-27
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    1沧州市人民医院,河北 061000

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2种不同金属材料的力学参数
科
Family
属数
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genus
种数
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species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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