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Article(id=1322931504967463484, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322931411006665649, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.13.013, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757606400000, receivedDateStr=2025-09-12, revisedDate=1774886400000, revisedDateStr=2026-03-31, acceptedDate=null, acceptedDateStr=null, onlineDate=1793341049663, onlineDateStr=2026-10-30, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793341049663, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793341049663, creator=13701087609, updateTime=1793341049663, updator=13701087609, issue=Issue{id=1322931411006665649, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='13', pageStart='2193', pageEnd='2384', issueExtLink='null', onlineDate='null', pubDate='1783612800000', pubDateStr='2026-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793341027261, creator='13701087609', updateTime=1793343114456, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940165425082661, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322931411006665649, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940165425082662, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322931411006665649, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2287, endPage=2292, ext={EN=ArticleExt(id=1322931505214927421, articleId=1322931504967463484, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction of a dynamic nomogram model for postoperative recurrence in patients with glioma and nursing strategies, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct a dynamic nomogram (DynNom) model for predicting postoperative recurrence in patients with glioma based on LASSO-logistic regression analysis.

Methods

Patients with glioma who underwent surgical treatment at The First Affiliated Hospital of Nanjing Medical University from August 2022 to December 2024 were selected as the study subjects.Their clinical data were collected.Univariate and multivariate LASSO-Logistic regression analyses were used to identify influencing factors for postoperative recurrence of glioma.A DynNom scoring model was established.

Results

Among the 211 patients with glioma,57 experienced recurrence within 12 months after surgery,resulting in a recurrence rate of 27.01%.Regression analysis showed that Karnofsky Performance Status(KPS) score,disease stage,maximum tumor diameter,surgical resection,and postoperative concurrent chemoradiotherapy were influencing factors for postoperative recurrence in patients with glioma(P<0.05).The DynNom scoring model,established based on the regression analysis results,had an area under the receiver operating characteristic(ROC) curve(AUC) of 0.807.The calibration curve was close to the ideal curve.

Conclusions

Multiple factors influence postoperative recurrence in patients with glioma.The DynNom scoring model constructed based on these risk factors demonstrates good predictive performance for the risk of postoperative recurrence in patients with glioma.

, authors=Jinru CHEN, Huayu CHEN, An'na GUO, Zongya XIA, Huiyun WANG, authorsList=Jinru CHEN, Huayu CHEN, An'na GUO, Zongya XIA, Huiyun WANG, authorCompany=null, correspAuthors=Jinru CHEN, 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=1322931506389332554, articleId=1322931504967463484, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=脑胶质瘤病人术后复发动态评分模型的构建及护理对策, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

基于LASSO-Logistic回归分析构建脑胶质瘤病人术后复发的动态评分模型。

方法

选取2022年8月—2024年12月南京医科大学第一附属医院行手术治疗的脑胶质瘤病人为研究对象，收集其临床资料，采用单因素和多因素LASSO-Logistic回归分析筛选脑胶质瘤术后复发的影响因素，并建立DynNom评分模型。

结果

211例脑胶质瘤病人中术后12个月内复发57例，复发率为27.01%。回归分析显示，卡氏功能状态评分（KPS）、疾病分期、肿瘤最大直径、手术切除范围、术后同步放化疗是脑胶质瘤病人术后复发的影响因素（P<0.05）。基于回归分析结果建立DynNom评分模型的受试者工作特征（ROC）曲线下面积（AUC）为0.807，校准曲线趋近于理想曲线。

结论

影响脑胶质瘤病人术后复发的因素较多，基于危险因素构建的DynNom评分模型对脑胶质瘤病人术后复发风险具有较好的预测效能。

, authors=陈金如, 陈华玉, 郭安娜, 夏宗雅, 王慧云, authorsList=陈金如, 陈华玉, 郭安娜, 夏宗雅, 王慧云, authorCompany=null, correspAuthors=陈金如, authorNote=null, correspAuthorsNote=
CHEN Jinru, E-mail:
陈金如，护师，E-mail：
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Univariate analysis of factors influencing postoperative recurrence in patients with glioma

, figureFileSmall=null, figureFileBig=null, tableContent=
项目类别复发组（n=57）非复发组（n=154）χ2值P
性别男39931.1460.284
女1861
年龄<40岁921
40~<60岁31840.1890.910
≥60岁1749
合并糖尿病7170.0640.801
合并高血压13420.4300.512
分化程度低分化26522.5060.113
中高分化31102
KPS评分<70分253112.016<0.001
≥70分32123
病变部位额叶24660.2100.976
颞叶1024
顶叶27
其他2157
疾病分期Ⅰ期/Ⅱ期251039.2410.002
Ⅲ期/Ⅳ期3251
肿瘤最大直径<6 cm2411923.557<0.001
≥6 cm3335
手术切除范围部分切除355710.0640.002
全切2297
手术时间<5 h418
5~<10 h34742.4840.289
≥10 h1962
术后瘤腔出血无出血2381
渗血13262.5720.276
血肿2147
术后同步放化疗117213.141<0.001
), ArticleFig(id=1322931509761553022, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931504967463484, language=CN, label=表1, caption=

脑胶质瘤病人术后复发影响因素的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
项目类别复发组（n=57）非复发组（n=154）χ2值P
性别男39931.1460.284
女1861
年龄<40岁921
40~<60岁31840.1890.910
≥60岁1749
合并糖尿病7170.0640.801
合并高血压13420.4300.512
分化程度低分化26522.5060.113
中高分化31102
KPS评分<70分253112.016<0.001
≥70分32123
病变部位额叶24660.2100.976
颞叶1024
顶叶27
其他2157
疾病分期Ⅰ期/Ⅱ期251039.2410.002
Ⅲ期/Ⅳ期3251
肿瘤最大直径<6 cm2411923.557<0.001
≥6 cm3335
手术切除范围部分切除355710.0640.002
全切2297
手术时间<5 h418
5~<10 h34742.4840.289
≥10 h1962
术后瘤腔出血无出血2381
渗血13262.5720.276
血肿2147
术后同步放化疗117213.141<0.001
), ArticleFig(id=1322931509845439103, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931504967463484, language=EN, label=Table 2, caption=

Multivariate Logistic regression analysis of factors influencing postoperative recurrence in patients with glioma

, figureFileSmall=null, figureFileBig=null, tableContent=
变量回归系数标准误Wald χ2值POR值95%CI
KPS评分0.8960.3885.3350.0212.449[1.145,5.237]
疾病分期0.9240.3646.4420.0112.519[1.234,5.142]
肿瘤最大直径1.5640.37017.876<0.0014.777[2.314,9.864]
手术切除范围0.8110.3625.0130.0252.250[1.106,4.576]
术后同步放化疗-1.1820.4168.0830.0040.307[0.136,0.693]
), ArticleFig(id=1322931509941908096, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931504967463484, language=CN, label=表2, caption=

脑胶质瘤病人术后复发影响因素的多因素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
KPS评分0.8960.3885.3350.0212.449[1.145,5.237]
疾病分期0.9240.3646.4420.0112.519[1.234,5.142]
肿瘤最大直径1.5640.37017.876<0.0014.777[2.314,9.864]
手术切除范围0.8110.3625.0130.0252.250[1.106,4.576]
术后同步放化疗-1.1820.4168.0830.0040.307[0.136,0.693]
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Scoring details for the risk of postoperative recurrence in patients with glioma

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影响因素类别评分
KPS评分≥70分42
<70分76
疾病分期Ⅰ期/Ⅱ期42
Ⅲ期/Ⅳ期77
肿瘤最大直径<6 cm42
≥6 cm100
手术切除范围全切42
部分切除72
术后同步放化疗是0
否42
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脑胶质瘤病人术后复发风险评分细则

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影响因素类别评分
KPS评分≥70分42
<70分76
疾病分期Ⅰ期/Ⅱ期42
Ⅲ期/Ⅳ期77
肿瘤最大直径<6 cm42
≥6 cm100
手术切除范围全切42
部分切除72
术后同步放化疗是0
否42
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脑胶质瘤病人术后复发动态评分模型的构建及护理对策
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陈金如 , 陈华玉 , 郭安娜 , 夏宗雅 , 王慧云
护理研究 | 科研论著 2026,40(13): 2287-2292
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护理研究 |科研论著 2026 , 40 (13) : 2287 -2292
脑胶质瘤病人术后复发动态评分模型的构建及护理对策
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陈金如 , 陈华玉, 郭安娜, 夏宗雅, 王慧云
作者信息
  • 1南京医科大学第一附属医院，江苏 210003
通讯作者:
CHEN Jinru, E-mail:
陈金如，护师，E-mail：
Construction of a dynamic nomogram model for postoperative recurrence in patients with glioma and nursing strategies
Jinru CHEN , Huayu CHEN, An'na GUO, Zongya XIA, Huiyun WANG
Affiliations
  • 1The First Affiliated Hospital of Nanjing Medical University, Jiangsu 210003 China
出版时间: 2026-07-10 doi: 10.12102/j.issn.1009-6493.2026.13.013
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摘要
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目的

基于LASSO-Logistic回归分析构建脑胶质瘤病人术后复发的动态评分模型。

方法

选取2022年8月—2024年12月南京医科大学第一附属医院行手术治疗的脑胶质瘤病人为研究对象，收集其临床资料，采用单因素和多因素LASSO-Logistic回归分析筛选脑胶质瘤术后复发的影响因素，并建立DynNom评分模型。

结果

211例脑胶质瘤病人中术后12个月内复发57例，复发率为27.01%。回归分析显示，卡氏功能状态评分（KPS）、疾病分期、肿瘤最大直径、手术切除范围、术后同步放化疗是脑胶质瘤病人术后复发的影响因素（P<0.05）。基于回归分析结果建立DynNom评分模型的受试者工作特征（ROC）曲线下面积（AUC）为0.807，校准曲线趋近于理想曲线。

结论

影响脑胶质瘤病人术后复发的因素较多，基于危险因素构建的DynNom评分模型对脑胶质瘤病人术后复发风险具有较好的预测效能。

脑胶质瘤  /  复发  /  同步放化疗  /  影响因素  /  预测模型
Objective

To construct a dynamic nomogram (DynNom) model for predicting postoperative recurrence in patients with glioma based on LASSO-logistic regression analysis.

Methods

Patients with glioma who underwent surgical treatment at The First Affiliated Hospital of Nanjing Medical University from August 2022 to December 2024 were selected as the study subjects.Their clinical data were collected.Univariate and multivariate LASSO-Logistic regression analyses were used to identify influencing factors for postoperative recurrence of glioma.A DynNom scoring model was established.

Results

Among the 211 patients with glioma,57 experienced recurrence within 12 months after surgery,resulting in a recurrence rate of 27.01%.Regression analysis showed that Karnofsky Performance Status(KPS) score,disease stage,maximum tumor diameter,surgical resection,and postoperative concurrent chemoradiotherapy were influencing factors for postoperative recurrence in patients with glioma(P<0.05).The DynNom scoring model,established based on the regression analysis results,had an area under the receiver operating characteristic(ROC) curve(AUC) of 0.807.The calibration curve was close to the ideal curve.

Conclusions

Multiple factors influence postoperative recurrence in patients with glioma.The DynNom scoring model constructed based on these risk factors demonstrates good predictive performance for the risk of postoperative recurrence in patients with glioma.

glioma  /  recurrence  /  concurrent chemoradiotherapy  /  influencing factors  /  prediction model
陈金如, 陈华玉, 郭安娜, 夏宗雅, 王慧云. 脑胶质瘤病人术后复发动态评分模型的构建及护理对策. 护理研究, 2026 , 40 (13) : 2287 -2292 . DOI: 10.12102/j.issn.1009-6493.2026.13.013
Jinru CHEN, Huayu CHEN, An'na GUO, Zongya XIA, Huiyun WANG. Construction of a dynamic nomogram model for postoperative recurrence in patients with glioma and nursing strategies[J]. Chinese Nursing Research, 2026 , 40 (13) : 2287 -2292 . DOI: 10.12102/j.issn.1009-6493.2026.13.013
正文
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脑胶质瘤是一种起源于中枢神经系统神经胶质细胞的恶性肿瘤，临床表现多样，常见症状包括头痛、癫痫发作、认知障碍、视力和听力问题以及肢无力等[1]。目前，脑胶质瘤的发病率在全球范围内呈现上升趋势[2]。尽管现代医学在手术技术、放射治疗和化学治疗近几年内取得了显著进展，但术后复发仍然是脑胶质瘤病人面临的重大问题[3]。术后复发定义为在手术切除肿瘤后，通过影像学检查或病理检查发现肿瘤重新生长[4]。复发通常发生在术后几个月，导致病人的生存期显著缩短。因此，识别和管理脑胶质瘤术后复发的风险因素对于改善病人预后具有重要临床意义。当前，尽管针对脑胶质瘤术后复发研究已有较多报道，但部分研究局限于危险因素探讨[5]，未能进一步构建能够定量化评估相关风险的预测模型，还有部分学者构建的模型存在特征选择不充分、使用过于复杂等问题[6]。LASSO（least absolute shrinkage and selection operator）回归是一种用于特征选择和模型简化的统计方法，能够有效地从大量变量中筛选出与结果相关性较强的变量[7]。因此，本研究拟基于LASSO-Logistic回归分析建立用于评估脑胶质瘤术后复发风险的动态评分模型，以为临床更好地评估和管理脑胶质瘤手术病人、优化护理决策提供科学依据。
1　对象与方法
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1.1　研究对象
选取2022年8月—2024年7月在我院行手术治疗的脑胶质瘤病人作为研究对象。纳入标准：年龄≥18岁，符合《脑胶质瘤诊疗指南(2022版)》[8]中脑胶质瘤的诊断标准；既往无脑胶质瘤手术、化疗、放疗史；具有完整的临床资料；病人或其家属签署知情同意书。排除标准：非原发性脑胶质瘤；合并严重的心脏病、肝肾功能不全等；合并其他恶性肿瘤、血液系统疾病、神经系统疾病、免疫系统疾病等；临床资料模糊或不全者。本研究已通过南京医科大学第一附属医院伦理委员会批准同意（伦审号：2022-SR-027）。
本研究样本量估算基于文献回顾和统计公式。根据既往研究，脑胶质瘤术后复发率约为30%[9]。采用Logistic回归分析样本量估算公式：n=[（Zα/2+Zβ）2×p（1-p）]/δ2，其中α=0.05（双侧），β=0.20（功效80%），p为预期复发率（取30%），δ为容许误差（取0.05）。计算得最小样本量为196例，考虑到5%的失访率，最终确定样本量应不低于207例。
1.2　随访和分组
采用电话、微信、面访、门诊复查等多种形式于病人出院后进行随访，随访时间为术后12个月，随访终点事件为脑胶质瘤复发。脑胶质瘤复发的判定由2名神经外科副主任医师及1名影像科医师共同评估确定。复发需满足以下至少1条标准：1）MRI T1加权像上增强病灶垂直径乘积之和较基线或最低值增加≥25%；2）出现任何新发的增强病灶；3）在稳定或增加的类固醇剂量下，T2加权/液体抑制反转恢复序列影像（T2/FLAIR）的非增强病灶出现显著增大[8]。
为确保数据完整，本研究采取以下措施进行质量控制：1)入院时即登记病人本人及至少2名家属的联系方式（电话、微信及住址）；2)设立专职随访护士，在术后1、3、6、9、12个月等多个时间点进行主动联系；3）将随访完成率纳入科室补助考核。
1.3　资料收集
采用医院信息系统（HIS）收集病人的临床资料，包括性别、年龄、糖尿病、高血压、分化程度、卡氏评分（Karnofsky Performance Status,KPS）评分、病变部位、世界卫生组织（WHO）疾病分期、肿瘤最大直径、手术切除范围、手术时间、术后瘤腔出血和术后同步放化疗。所有数据均由2名经过培训的护理人员独立录入，并进行交叉核对，以确保数据的准确性和完整性。
1.4　统计学分析
采用SPSS 22.0软件进行数据分析，定性资料组间比较采用χ2检验。采用LASSO和Logistic回归分析筛选影响因素，以P<0.05为差异有统计学意义。采用R（R4.3.3）构建动态评分模型。
2　结果
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2.1　脑胶质瘤病人术后复发率及单因素分析
共纳入211例脑胶质瘤，无失访病人，满足样本量要求，其中术后12个月内复发57例，发生率为27.01%。基于此将病人分为复发组（n=57）和非复发组（n=154）。结果显示，两组性别、年龄、糖尿病、高血压、分化程度、病变部位、手术时间和术后瘤腔出血资料比较差异均无统计学意义（P>0.05）；KPS评分、疾病分期、肿瘤最大直径、手术切除范围和术后同步放化疗资料比较差异均有统计学意义（P<0.05），见表1。
2.2　脑胶质瘤病人术后复发影响因素的LASSO回归分析
采用LASSO回归分析筛选参数，采用10倍交叉验证法选择LASSO回归模型中参数λ的最佳值，结果显示当Log(λ)=-4.11时，λ值最佳，见图1、图2。筛选出的变量包括KPS评分、疾病分期、肿瘤最大直径、手术切除范围和术后同步放化疗。
2.3　脑胶质瘤病人术后复发影响因素的多因素Logistic回归分析
以复发情况为因变量（发生=1，未发生=0），以LASSO回归分析筛选出的KPS评分、疾病分期、肿瘤最大直径、手术切除范围和术后同步放化疗特征变量为自变量，进行多因素Logistic回归分析。自变量赋值：疾病分期，Ⅰ/Ⅱ期=1，Ⅲ/Ⅳ期=2；肿瘤最大直径，<6 cm=1，≥6 cm=2；手术切除范围，全切=1，部分切除=2；术后同步放化疗，是=1，否=2；KPS评分，≥70分=1，<70分=2。结果显示，KPS评分<70分、疾病分期Ⅲ期/Ⅳ期、肿瘤最大直径≥6 cm、手术部分切除、术后未同步放化疗是脑胶质瘤病人术后复发的影响因素（P<0.05），见表2。
2.4　脑胶质瘤病人术后复发风险的动态评分模型的建立与验证
基于筛选出的影响因素构建动态评分模型见图3，评分细则见表3。为进一步评估模型在内部样本中的预测性能，采用Bootstrap法进行内部验证，重复抽样1 000次，模型验证结果显示，Hosmer-Lemeshow检验显示χ2=4.906，P=0.161，校准曲线趋近于理想曲线，见图4；受试者工作特征（ROC）曲线下面积（AUC）为0.807[95%CI（0.741，0.873）]，见图5；模型在10%~87%预测范围内，净获益值均>0，见图6。
3　讨论
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脑胶质瘤术后复发是病人预后不良的重要原因。本研究发现，脑胶质瘤术后复发率为27.01%，介于朱一硕等[10]报道的26.6%和Wang等[9]报道的30%之间，这可能是由于不同研究的样本特征和治疗方式存在差异，但均凸显了脑胶质瘤术后高复发率，这也提示在术后应采取更为积极的随访和干预措施。关于脑胶质瘤术后复发的潜在机制，目前尚未完全查明，这可能是因为脑胶质瘤具有高度侵袭性，能够侵入周围的正常脑组织，这使得完全切除变得困难，术后剩余的肿瘤细胞可能成为复发的根源[11]。此外，肿瘤周围的微环境（如血管生成和免疫抑制状态）也可能支持肿瘤细胞的存活和增殖，从而导致复发[12]。
本研究筛选出KPS评分<70分、疾病分期Ⅲ/Ⅳ期、肿瘤最大直径≥6 cm、手术部分切除是脑胶质瘤病人术后复发的独立危险因素，术后同步放化疗是其保护因素。程慧冉[13]报道非全切、高级别、肿瘤直径>6 cm是脑胶质瘤术后复发危险因素，术后放化疗、术前KPS评分>70分是脑胶质瘤术后复发保护因素。张林娟等[14]报道病理分级高、次全切、肿瘤>6 cm、术后无放疗或化疗为术后复发的危险因素，均与本研究结果一致。KPS评分<70分表明病人功能状态较差，可能存在日常生活能力受限、体力活动受限、营养较差等状况，这可能导致术后恢复缓慢和身体抵抗力下降，影响伤口愈合、免疫功能和整体代谢状态，从而增加术后复发的风险[15]。此外，机体功能状态较差还可导致术后辅助治疗的耐受性下降，影响治疗效果。高分期肿瘤具有快速生长和高度增殖的特性，相较于低分期肿瘤能够迅速侵入周围正常脑组织，形成多个肿瘤细胞团块，即使进行了手术切除，剩余的肿瘤细胞仍可能存在于手术边缘或周围组织中，从而导致复发[16-17]。而大体积肿瘤往往具有更强的侵袭性，能够侵入周围正常脑组织，这使得完全切除变得极为困难，即使进行了手术切除，剩余的肿瘤细胞仍可能存在于手术边缘或周围组织中。部分切除可能无法有效清除肿瘤的多中心起源区域，这些区域可能包含多个独立的肿瘤细胞团块。即使进行了部分切除，剩余的肿瘤细胞仍可能成为复发的根源[18]。此外，部分切除还可能导致术后肿瘤细胞的再分布，这些细胞可能在术后环境中扩散到其他部位，形成新的肿瘤病灶，导致复发。放化疗能够通过诱导肿瘤细胞凋亡和抑制血管生成直接杀灭肿瘤细胞或抑制其增殖，而术后未同步放化疗可能导致残留的肿瘤细胞在术后环境中存活并增殖，从而增加复发风险。此外，术后未同步放化疗可能导致肿瘤周围的微环境未能得到有效改善，从而促进肿瘤细胞的存活和增殖[19]。
LASSO回归通过引入L1正则化，能够自动筛选出对预测结果较为重要的变量，从而减少模型的复杂性和过拟合的风险。在脑胶质瘤术后复发的预测中，涉及大量的临床变量，LASSO回归能够将不重要的变量系数压缩至零，从而简化模型结构，提高模型的解释性和可操作性，有助于在实际护理工作中进行风险评估和决策制定[20]。通过LASSO回归筛选出的关键变量，结合Logistic回归进行建模，能够提高模型的预测准确性。Logistic回归作为一种经典的二分类模型，具有良好的统计学基础和广泛的应用经验，能够有效处理二分类问题。本研究基于LASSO-Logistic回归分析结果构建了预测脑胶质瘤病人术后复发风险的动态评分模型。模型验证结果显示，Hosmer-Lemeshow检验结果为χ2 =4.906，P =0.161，校准曲线趋近理想曲线，表明模型拟合度和预测准确性良好；ROC曲线的AUC为0.807，表明模型区分度良好；模型在10%~87%预测范围内，净获益值均>0，表明临床实用性良好。模型通过可视化的方式展示复发风险的评分过程，提高了临床决策的透明度和可操作性。
基于本研究构建的动态评分模型及所识别的独立危险因素，可提出一套系统性的护理对策，其核心在于利用模型预测结果进行精准风险分层，并据此实施个体化干预。对于高风险组（如风险>60%），护理重点在于建立预警强化管理机制，由专职护士牵头实施每个月1次的高频随访，严密监测头痛、癫痫等复发早期症状，并严格督导其完成每3个月1次的影像学复查。同时，针对KPS评分低、未规范接受同步放化疗等关键危险因素进行深度干预，如联合营养与康复部门制定个性化方案以改善病人功能状态，并通过深入沟通与教育降低其治疗依从性障碍。对于中风险组（如风险30%~60%），护理策略以监督教育为主，通过每3个月1次的系统化随访，巩固其放化疗等规范治疗，并利用健康手册与专题讲座强化其对危险因素的自我管理能力，防止其向高风险转化。对于低风险组（如风险<30%），则侧重于支持维护，通过每6个月的常规随访进行健康生活方式指导与年度复查提醒，促进其全面康复。整个护理体系应是一个动态循环过程，需根据病人术后不同时间点的再次评估结果实时调整干预策略，从而将有限的护理资源优先用于最需要的病人，最终实现延缓复发、改善预后的目标。
4　小结
收起
综上所述，临床应重视KPS评分<70分、疾病分期Ⅲ期/Ⅳ期、肿瘤最大直径≥6 cm、手术部分切除和术后未同步放化疗的脑胶质瘤病人，警惕其术后复发。本研究基于上述危险因素构建的动态评分模型具有良好的预测效能，且有助于优化护理流程。本研究的不足在于样本量和收集的临床资料有限，随访时间较短，后续应扩充样本量和变量数、延长随访时间进一步优化模型。

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doi: 10.12102/j.issn.1009-6493.2026.13.013
  • 接收时间：2025-09-12
  • 首发时间：2026-10-30
  • 出版时间：2026-07-10
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  • 收稿日期：2025-09-12
  • 修回日期：2026-03-31
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    1南京医科大学第一附属医院，江苏 210003

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CHEN Jinru, E-mail:
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