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Article(id=1322954266238923272, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322954180431847810, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202507060, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752768000000, receivedDateStr=2025-07-18, revisedDate=1762963200000, revisedDateStr=2025-11-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1793346476372, onlineDateStr=2026-10-30, pubDate=1765728000000, pubDateStr=2025-12-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793346476372, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793346476372, creator=13701087609, updateTime=1793346476372, updator=13701087609, issue=Issue{id=1322954180431847810, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='23', pageStart='2881', pageEnd='3008', issueExtLink='null', onlineDate='null', pubDate='1765728000000', pubDateStr='2025-12-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793346455915, creator='13701087609', updateTime=1793346455915, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=2963, endPage=2971, ext={EN=ArticleExt(id=1322954266574467593, articleId=1322954266238923272, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=Early identification of delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage: development and validation of a multifactorial clinical prediction model, columnId=1320435174995772212, journalTitle=Journal of Army Medical University, columnName=Clinical Medicine, runingTitle=null, highlight=null, articleAbstract=
Objective

To develop and validate a nomogram incorporating postoperative blood pressure and other multifactorial predictors for the risk of delayed cerebral ischemia(DCI) following surgical clipping in patients with aneurysmal subarachnoid hemorrhage(aSAH).

Methods

This retrospective cohort study consecutively enrolled 272 aSAH patients who underwent aneurysm clipping at the Department of Neurosurgery, Second Affiliated Hospital of Chongqing Medical University from January 2018 to January 2022. Demographic, clinical, and imaging data were collected. Predictors were screened using LASSO regression, and a multifactorial logistic regression-based nomogram was constructed. The dataset was randomly divided into training(n=190) and validation(n=82) sets at a 7∶3 ratio. Model discrimination, calibration, and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC), calibration curves, and decision curve analysis(DCA), respectively.

Results

Among 272 enrolled patients, 98(36%) developed DCI. Multivariate analysis identified age(OR=0.97; 95%CI: 0.94~0.99; P=0.036), World Federation of Neurological Surgeons(WFNS) grade(OR=1.36; 95%CI: 1.02~1.82; P=0.038), Fisher grade(OR=1.44; 95%CI: 1.01~2.05; P=0.048), and mean arterial pressure on postoperative day 2(OR=1.06; 95%CI: 1.03~1.09; P<0.001) as independent risk factors for DCI. The model achieved C-indices of 0.739 (95%CI : 0.668~0.810) in the training set and 0.760 (95%CI: 0.647~0.872) in the validation set. Calibration curves demonstrated high agreement between predicted and observed probabilities(mean absolute error=0.027), while DCA confirmed significant net clinical benefit at threshold probabilities of 15%~65%.

Conclusion

We successfully developed a predictive model incorporating age, WFNS grade, Fisher grade, and postoperative day 2 mean arterial pressure, with elevated postoperative blood pressure identified as an early critical indicator for DCI development.

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目的

构建并验证一个结合术后血压等多因素的列线图模型，用于预测动脉瘤性蛛网膜下腔出血（aneurysmal subarachnoid hemorrhage，aSAH）患者动脉瘤夹闭术后迟发性脑缺血（delayed cerebral ischemia，DCI）的发生风险。

方法

本研究为一项回顾性队列研究，连续纳入2018年1月至2022年1月重庆医科大学附属第二医院神经外科收治的272例行动脉瘤夹闭术的aSAH患者。收集患者人口统计学、临床及影像学资料，通过LASSO回归筛选预测因子，并采用多因素Logistic回归构建预测DCI的列线图模型。将数据集按7∶3随机分为训练集（n=190）与验证集（n=82），使用受试者工作特征曲线下面积、校准曲线及决策曲线分析分别评估模型的区分度、校准能力与临床实用性。

结果

本研究共纳入272例患者，其中98例（36%）发生DCI。多因素分析确定年龄（OR=0.97，95%CI：0.94~0.99；P=0.036）、WFNS分级（OR=1.36，95%CI：1.02~1.82；P=0.038）、Fisher分级（OR=1.44，95%CI：1.01~2.05；P=0.048）及术后第二天平均动脉压（OR=1.06，95%CI：1.03~1.09；P<0.001）为DCI的独立危险因素。模型在训练集与验证集中的一致性指数分别为0.739（95%CI：0.668~0.810）与0.760（95%CI：0.647~0.872）。校准曲线显示预测概率与观察概率高度一致，而决策曲线分析证实该模型在0.15~0.65的阈值概率范围内具有显著的临床净获益。

结论

成功构建了一个包含年龄、WFNS分级、Fisher分级及术后第二天平均动脉压的预测模型，并证实术后血压升高是提示迟发性脑缺血发生的早期重要信号。

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马颖，E-mail：
MA Ying，E-mail：
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Lancet Neurol，2018，17（7）：641-650., articleTitle=Sex differences in the evaluation and treatment of acute ischaemic stroke, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322954269976048149, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, xref=1, ext=[AuthorCompanyExt(id=1322954269984436758, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, companyId=1322954269976048149, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Neurosurgery，the Second Affiliated Hospital of Chongqing Medical University，Chongqing，China), AuthorCompanyExt(id=1322954269997019671, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, companyId=1322954269976048149, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1重庆医科大学附属第二医院神经外科，重庆)])], figs=[ArticleFig(id=1322954271699907124, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=EN, label=Figure 1, caption=Flow diagram of patient enrollment, figureFileSmall=HGvmHgKjOg6HVxNJSU/Uzg==, figureFileBig=c4bqR/tSZL+0NK6h/ztBwg==, tableContent=null), ArticleFig(id=1322954271779598901, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=CN, label=图1, caption=患者入组流程图, figureFileSmall=HGvmHgKjOg6HVxNJSU/Uzg==, figureFileBig=c4bqR/tSZL+0NK6h/ztBwg==, tableContent=null), ArticleFig(id=1322954273587343926, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=EN, label=Figure 2, caption=Nomogram for predicting the occurrence of delayed cerebral ischemia, figureFileSmall=jvSrC7jSMlSrGFBdxs45Ig==, figureFileBig=37uhU+VOfVFbkT6SXtea8Q==, tableContent=null), ArticleFig(id=1322954273671230007, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=CN, label=图2, caption=预测迟发性脑缺血发生的列线图, figureFileSmall=jvSrC7jSMlSrGFBdxs45Ig==, figureFileBig=37uhU+VOfVFbkT6SXtea8Q==, tableContent=null), ArticleFig(id=1322954273734144568, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=EN, label=Figure 3, caption=Receiver operating characteristic curves of the model in the training（A） and validation（B） sets, figureFileSmall=qXePpNXBybjWKTWkVUzqSQ==, figureFileBig=nA3NcgphmwCHvpNFLoAQrw==, tableContent=null), ArticleFig(id=1322954273822224953, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=CN, label=图3, caption=训练集（A）和验证集（B）的受试者工作特征曲线分析结果, figureFileSmall=qXePpNXBybjWKTWkVUzqSQ==, figureFileBig=nA3NcgphmwCHvpNFLoAQrw==, tableContent=null), ArticleFig(id=1322954273893528122, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=EN, label=Figure 4, caption=Calibration curves of the model in the training（A） and validation（B） sets, figureFileSmall=bCeEwjHSYBGq6YVcuw45qQ==, figureFileBig=OSFBiIamItFFcVlb5p3oTQ==, tableContent=null), ArticleFig(id=1322954273977414203, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=CN, label=图4, caption=训练集（A）与验证集（B）的校准曲线分析, figureFileSmall=bCeEwjHSYBGq6YVcuw45qQ==, figureFileBig=OSFBiIamItFFcVlb5p3oTQ==, tableContent=null), ArticleFig(id=1322954274036134460, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=EN, label=Figure 5, caption=Decision curve analysis of the model in the training（A） and validation（B） sets, figureFileSmall=gr1Lv/2+zadkyohW8I7LUA==, figureFileBig=IrY7vEKJXrNS/S7kI+EzqA==, tableContent=null), ArticleFig(id=1322954274111631933, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=CN, label=图5, caption=训练集（A）和验证集（B）的决策曲线分析结果, figureFileSmall=gr1Lv/2+zadkyohW8I7LUA==, figureFileBig=IrY7vEKJXrNS/S7kI+EzqA==, tableContent=null), ArticleFig(id=1322954274178740798, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=EN, label=Table 1, caption=

Baseline characteristics of patients with aneurysmal subarachnoid hemorrhage after clipping surgery，stratified by DCI status ［n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
VariableTotal（n=272）DCIP
Non-DCI group（n=174）DCI group（n=98）
Age/years58.00（50.0，65.0）59.00（51.00，66.00）55.00（48.25，64.00）0.014
Aneurysm diameter/mm5.15（4.07，7.50）5.30（4.20，7.30）5.00（3.85，7.60）0.439
MAP on admission/mmHg108.00（99.00，120.00）106.00（98.00，121.00）109.50（100.00，118.50）0.433
MAP on postoperative day 1/mmHg92.00（87.00，97.00）91.50（87.00，97.00）92.50（87.25，98.00）0.630
MAP on postoperative day 2/mmHg94.00（88.00，99.00）92.00（85.25，97.00）98.00（92.00，102.00）<0.001
Gender0.039
Female177（65.07）121（69.54）56（57.14）
Male95（34.93）53（30.46）42（42.86）
History of hypertension0.880
No151（55.51）96（55.17）55（56.12）
Yes121（44.49）78（44.83）43（43.88）
History of diabetes0.585
No258（94.85）166（95.40）92（93.88）
Yes14（5.15）8（4.60）6（6.12）
Smoking history0.487
No201（73.9）131（75.29）70（71.43）
Yes71（26.1）43（24.71）28（28.57）
Drinking history0.323
No209（76.84）137（78.74）72（73.47）
Yes63（23.16）37（21.26）26（26.53）
WFNS grade0.376
Ⅰ125（45.96）84（48.28）41（41.84）
Ⅱ67（24.63）45（25.86）22（22.45）
Ⅲ34（12.5）20（11.49）14（14.29）
Ⅳ46（16.91）25（14.37）21（21.43）
Fisher grade<0.001
129（10.66）22（12.64）7（7.14）
296（35.29）69（39.66）27（27.55）
377（28.31）56（32.18）21（21.43）
470（25.74）27（15.52）43（43.88）
Decompressive craniectomy0.065
No201（73.9）135（77.59）66（67.35）
Yes71（26.1）39（22.41）32（32.65）
Hunt-Hess grade0.412
130（11.03）19（10.92）11（11.22）
2144（52.94）98（56.32）46（46.94）
366（24.26）37（21.26）29（29.59）
4~532（11.76）20（11.49）12（12.24）
Preoperative intracranial hematoma0.674
No212（77.94）137（78.74）75（76.53）
Yes60（22.06）37（21.26）23（23.47）
Aneurysm location0.794
Anterior circulation254（93.38）163（93.68）91（92.86）
Posterior circulation18（6.62）11（6.32）7（7.14）
Postoperative rebleeding0.762
No230（84.56）148（85.06）82（83.67）
Yes42（15.44）26（14.94）16（16.33）
), ArticleFig(id=1322954274258432575, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=CN, label=表1, caption=

动脉瘤性蛛网膜下腔出血夹闭术后患者的基线特征比较 ［例（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
VariableTotal（n=272）DCIP
Non-DCI group（n=174）DCI group（n=98）
Age/years58.00（50.0，65.0）59.00（51.00，66.00）55.00（48.25，64.00）0.014
Aneurysm diameter/mm5.15（4.07，7.50）5.30（4.20，7.30）5.00（3.85，7.60）0.439
MAP on admission/mmHg108.00（99.00，120.00）106.00（98.00，121.00）109.50（100.00，118.50）0.433
MAP on postoperative day 1/mmHg92.00（87.00，97.00）91.50（87.00，97.00）92.50（87.25，98.00）0.630
MAP on postoperative day 2/mmHg94.00（88.00，99.00）92.00（85.25，97.00）98.00（92.00，102.00）<0.001
Gender0.039
Female177（65.07）121（69.54）56（57.14）
Male95（34.93）53（30.46）42（42.86）
History of hypertension0.880
No151（55.51）96（55.17）55（56.12）
Yes121（44.49）78（44.83）43（43.88）
History of diabetes0.585
No258（94.85）166（95.40）92（93.88）
Yes14（5.15）8（4.60）6（6.12）
Smoking history0.487
No201（73.9）131（75.29）70（71.43）
Yes71（26.1）43（24.71）28（28.57）
Drinking history0.323
No209（76.84）137（78.74）72（73.47）
Yes63（23.16）37（21.26）26（26.53）
WFNS grade0.376
Ⅰ125（45.96）84（48.28）41（41.84）
Ⅱ67（24.63）45（25.86）22（22.45）
Ⅲ34（12.5）20（11.49）14（14.29）
Ⅳ46（16.91）25（14.37）21（21.43）
Fisher grade<0.001
129（10.66）22（12.64）7（7.14）
296（35.29）69（39.66）27（27.55）
377（28.31）56（32.18）21（21.43）
470（25.74）27（15.52）43（43.88）
Decompressive craniectomy0.065
No201（73.9）135（77.59）66（67.35）
Yes71（26.1）39（22.41）32（32.65）
Hunt-Hess grade0.412
130（11.03）19（10.92）11（11.22）
2144（52.94）98（56.32）46（46.94）
366（24.26）37（21.26）29（29.59）
4~532（11.76）20（11.49）12（12.24）
Preoperative intracranial hematoma0.674
No212（77.94）137（78.74）75（76.53）
Yes60（22.06）37（21.26）23（23.47）
Aneurysm location0.794
Anterior circulation254（93.38）163（93.68）91（92.86）
Posterior circulation18（6.62）11（6.32）7（7.14）
Postoperative rebleeding0.762
No230（84.56）148（85.06）82（83.67）
Yes42（15.44）26（14.94）16（16.33）
), ArticleFig(id=1322954274354901568, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=EN, label=Table 2, caption=

Regression coefficients and odds ratios of predictors in the multivariable logistic regression model

, figureFileSmall=null, figureFileBig=null, tableContent=
VariablesOR（95%CI）PAdjust OR（95%CI）Adjust P
MAP on admission/mmHg1.00（0.99~1.02）0.539
Fisher grade1.78（1.35~2.34）<0.0011.44（1.01~2.05）0.048
Age/years0.97（0.95~0.99）0.0210.97（0.94~0.99）0.036
WFNS grade1.68（1.35~2.09）<0.0011.36（1.02~1.82）0.038
MAP on postoperative day 2/mmHg1.07（1.04~1.10）<0.0011.06（1.03~1.09）< 0.001
MAP on postoperative day 1/mmHg1.01（0.98~1.04）0.707
Gender0.0400.248
Male1.00（reference）1.00（reference）
Female1.71（1.02~2.86）1.40（0.79~2.49）
Decompressive craniectomy<0.0010.745
No1.00（reference）1.00（reference）
Yes2.58（1.47~4.52）1.13（0.55~2.29）
), ArticleFig(id=1322954274434593345, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322954266238923272, language=CN, label=表2, caption=

多因素Logistic回归模型中各预测因子的回归系数及比值比

, figureFileSmall=null, figureFileBig=null, tableContent=
VariablesOR（95%CI）PAdjust OR（95%CI）Adjust P
MAP on admission/mmHg1.00（0.99~1.02）0.539
Fisher grade1.78（1.35~2.34）<0.0011.44（1.01~2.05）0.048
Age/years0.97（0.95~0.99）0.0210.97（0.94~0.99）0.036
WFNS grade1.68（1.35~2.09）<0.0011.36（1.02~1.82）0.038
MAP on postoperative day 2/mmHg1.07（1.04~1.10）<0.0011.06（1.03~1.09）< 0.001
MAP on postoperative day 1/mmHg1.01（0.98~1.04）0.707
Gender0.0400.248
Male1.00（reference）1.00（reference）
Female1.71（1.02~2.86）1.40（0.79~2.49）
Decompressive craniectomy<0.0010.745
No1.00（reference）1.00（reference）
Yes2.58（1.47~4.52）1.13（0.55~2.29）
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动脉瘤性蛛网膜下腔出血后迟发性脑缺血早期识别：一项多因素临床预测模型的建立与验证
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郑辉 , 陶一浩 , 吉翔 , 马颖
陆军军医大学学报 | 临床医学·论著 2025,47(23): 2963-2971
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陆军军医大学学报 |临床医学·论著 2025 , 47 (23) : 2963 -2971
动脉瘤性蛛网膜下腔出血后迟发性脑缺血早期识别：一项多因素临床预测模型的建立与验证
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郑辉, 陶一浩, 吉翔, 马颖
作者信息
  • 1重庆医科大学附属第二医院神经外科，重庆
通讯作者:
马颖，E-mail：
MA Ying，E-mail：
Early identification of delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage: development and validation of a multifactorial clinical prediction model
Hui ZHENG, Yihao TAO, Xiang JI, Ying MA
Affiliations
  • 1Department of Neurosurgery，the Second Affiliated Hospital of Chongqing Medical University，Chongqing，China
出版时间: 2025-12-15 doi: 10.16016/j.2097-0927.202507060
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摘要
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目的

构建并验证一个结合术后血压等多因素的列线图模型，用于预测动脉瘤性蛛网膜下腔出血（aneurysmal subarachnoid hemorrhage，aSAH）患者动脉瘤夹闭术后迟发性脑缺血（delayed cerebral ischemia，DCI）的发生风险。

方法

本研究为一项回顾性队列研究，连续纳入2018年1月至2022年1月重庆医科大学附属第二医院神经外科收治的272例行动脉瘤夹闭术的aSAH患者。收集患者人口统计学、临床及影像学资料，通过LASSO回归筛选预测因子，并采用多因素Logistic回归构建预测DCI的列线图模型。将数据集按7∶3随机分为训练集（n=190）与验证集（n=82），使用受试者工作特征曲线下面积、校准曲线及决策曲线分析分别评估模型的区分度、校准能力与临床实用性。

结果

本研究共纳入272例患者，其中98例（36%）发生DCI。多因素分析确定年龄（OR=0.97，95%CI：0.94~0.99；P=0.036）、WFNS分级（OR=1.36，95%CI：1.02~1.82；P=0.038）、Fisher分级（OR=1.44，95%CI：1.01~2.05；P=0.048）及术后第二天平均动脉压（OR=1.06，95%CI：1.03~1.09；P<0.001）为DCI的独立危险因素。模型在训练集与验证集中的一致性指数分别为0.739（95%CI：0.668~0.810）与0.760（95%CI：0.647~0.872）。校准曲线显示预测概率与观察概率高度一致，而决策曲线分析证实该模型在0.15~0.65的阈值概率范围内具有显著的临床净获益。

结论

成功构建了一个包含年龄、WFNS分级、Fisher分级及术后第二天平均动脉压的预测模型，并证实术后血压升高是提示迟发性脑缺血发生的早期重要信号。

蛛网膜下腔出血  /  脑缺血  /  颅内动脉瘤  /  血压
Objective

To develop and validate a nomogram incorporating postoperative blood pressure and other multifactorial predictors for the risk of delayed cerebral ischemia(DCI) following surgical clipping in patients with aneurysmal subarachnoid hemorrhage(aSAH).

Methods

This retrospective cohort study consecutively enrolled 272 aSAH patients who underwent aneurysm clipping at the Department of Neurosurgery, Second Affiliated Hospital of Chongqing Medical University from January 2018 to January 2022. Demographic, clinical, and imaging data were collected. Predictors were screened using LASSO regression, and a multifactorial logistic regression-based nomogram was constructed. The dataset was randomly divided into training(n=190) and validation(n=82) sets at a 7∶3 ratio. Model discrimination, calibration, and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC), calibration curves, and decision curve analysis(DCA), respectively.

Results

Among 272 enrolled patients, 98(36%) developed DCI. Multivariate analysis identified age(OR=0.97; 95%CI: 0.94~0.99; P=0.036), World Federation of Neurological Surgeons(WFNS) grade(OR=1.36; 95%CI: 1.02~1.82; P=0.038), Fisher grade(OR=1.44; 95%CI: 1.01~2.05; P=0.048), and mean arterial pressure on postoperative day 2(OR=1.06; 95%CI: 1.03~1.09; P<0.001) as independent risk factors for DCI. The model achieved C-indices of 0.739 (95%CI : 0.668~0.810) in the training set and 0.760 (95%CI: 0.647~0.872) in the validation set. Calibration curves demonstrated high agreement between predicted and observed probabilities(mean absolute error=0.027), while DCA confirmed significant net clinical benefit at threshold probabilities of 15%~65%.

Conclusion

We successfully developed a predictive model incorporating age, WFNS grade, Fisher grade, and postoperative day 2 mean arterial pressure, with elevated postoperative blood pressure identified as an early critical indicator for DCI development.

subarachnoid hemorrhage  /  cerebral ischemia  /  intracranial aneurysm  /  blood pressure
郑辉, 陶一浩, 吉翔, 马颖. 动脉瘤性蛛网膜下腔出血后迟发性脑缺血早期识别：一项多因素临床预测模型的建立与验证. 陆军军医大学学报, 2025 , 47 (23) : 2963 -2971 . DOI: 10.16016/j.2097-0927.202507060
Hui ZHENG, Yihao TAO, Xiang JI, Ying MA. Early identification of delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage: development and validation of a multifactorial clinical prediction model[J]. Journal of Army Medical University, 2025 , 47 (23) : 2963 -2971 . DOI: 10.16016/j.2097-0927.202507060
正文
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颅内动脉瘤的发病率在脑血管疾病中位列第三，具有极高的致残率和病死率［1］。迟发性脑缺血（delayed cerebral ischemia，DCI）作为动脉瘤性蛛网膜下腔出血（aneurysmal subarachnoid hemorrhage，aSAH）后最常见的并发症之一，对患者预后具有重大影响［2］。DCI会使患者临床病程复杂化，并显著恶化预后结局［3］，因此，提高早期识别DCI患者的能力至关重要［4］。目前已知的DCI预测因素包括：神经系统查体异常、蛛网膜下腔出血量较大、吸烟史、高血糖、脑积水、既往糖尿病史以及女性性别［3，5-7］。对于各种类型的颅内动脉瘤，外科夹闭术是最重要的治疗手段之一。动脉瘤夹闭术与血管内弹簧圈栓塞术相比，由于术中牵拉和机械刺激等因素，患者发生DCI的风险更高。研究表明，较高的平均动脉压（mean arterial pressure，MAP）与DCI的发生显著相关，这可能反映了脑血管自动调节功能在DCI早期阶段起着重要作用［8］。然而，动脉瘤开颅术后导致患者DCI发生的危险因素尚未完全阐明［9］。基于此，本研究旨在探讨动脉瘤夹闭术后早期血压变化等多种因素与DCI发生的关联性，并构建预测模型以评估蛛网膜下腔出血患者接受外科夹闭术后发生DCI的风险，以期为降低aSAH患者DCI发生风险提供临床指导策略。
1　资料与方法
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1.1　一般资料
采用回顾性队列研究设计，连续纳入2018年1月至2022年1月在重庆医科大学附属第二医院神经外科收治的272例aSAH患者为研究对象。所有疑似aSAH患者通过数字减影血管造影（digital subtraction angiography，DSA）或头颈部计算机断层扫描血管成像（computed tomography angiography，CTA）确诊。纳入标准：①经CTA或DSA确诊为动脉瘤所致的蛛网膜下腔出血患者；②年龄≥18岁；③接受过动脉瘤夹闭术治疗。排除标准：①合并其他重大神经系统疾病（如中枢系统肿瘤、颅脑创伤、帕金森病）或其他原因导致的蛛网膜下腔出血；②中途放弃治疗或提前出院者；③血压监测数据或影像学资料缺失者。本研究经重庆医科大学附属第二医院国家药物临床试验机构伦理委员会批准［2025年伦审（271）号］。
1.2　临床管理策略
所有疑似aSAH患者需首先接受DSA或头颈部CTA放射学检查以明确出血来源。若发现其他明确致病病灶或CTA与DSA检查均为阴性，则排除诊断。对于临床高度怀疑但初次检查阴性的病例，需通过重复CTA或DSA检查获取进一步影像证据。
所有确诊患者在24 h内由本院神经血管外科医师与介入神经放射科医师共同会诊后，决定是否行外科夹闭术。所有aSAH患者接受为期21 d的尼莫地平治疗，急性脑积水患者行脑室引流术治疗。颅内压增高者首先采取保守治疗，包括脑脊液引流、头位抬高、渗透疗法、深度镇静及肌松治疗。对保守治疗无效的顽固性颅内压增高患者实施去骨瓣减压术。所有接受开颅手术的患者术后转入重症监护室（intensive care unit，ICU）继续治疗。
1.3　数据收集
本研究采集了以下临床数据：①患者人口统计学特征（包括吸烟史与饮酒史）；②动脉瘤形态学参数；③合并症情况；④临床表现（包括入院时WFNS分级与Hunt-Hess分级）；⑤影像学特征（包括Fisher分级）；⑥入院时血压水平；⑦DCI发生率；⑧干预措施；⑨术后再出血事件。其中，吸烟史定义为有规律吸烟行为（或戒烟不足6个月），饮酒史定义为有规律饮酒行为（男性>25 g/d，女性>15 g/d）。神经功能状态采用WFNS分级（基于GCS评分与运动障碍）和Hunt-Hess分级（基于临床症状严重程度）评估。出血严重程度采用Fisher分级（基于CT所示蛛网膜下腔血凝块厚度）评估。术后再出血定义为术后出现新发神经功能缺损并经影像学证实有新发出血灶。基于DCI定义为动脉瘤闭塞48 h后发生迟发性缺血事件，所有患者术后每2小时测量1次收缩压及舒张压，共计25个时间点，覆盖术后48 h。为反映整体血压水平，分别计算术后前24 h及后24 h的平均收缩压和平均舒张压，作为术后血压水平的代表性指标纳入后续统计分析。本研究的主要结局指标为DCI的发生。根据国际共识定义，DCI的诊断基于以下临床与影像学的综合性评估，核心标准为：在动脉瘤夹闭术后，排除再出血、急性脑积水、癫痫、感染或代谢紊乱等其他明确病因后，出现的新发或进行性加重的神经功能缺损（如意识水平下降、定向力障碍或出现新的局灶性神经体征）。该临床诊断需有客观影像学证据支持，主要包括：①CT灌注成像显示存在与临床症状对应的脑血流动力学障碍，如脑血流量显著降低伴平均通过时间明显延长；②血管成像（CTA或DSA） 证实存在相应的脑血管痉挛；③DCI的最终确诊依赖后续CT或MRI检查发现的新发脑梗死灶。
1.4　研究设计与质量控制
本研究的设计基于GRIMES等［10］提出的临床研究分类法则，是一项单中心、回顾性、观察性的队列研究。研究旨在探究动脉瘤夹闭术后患者的一系列潜在预测因素（“暴露”）与DCI（“结局”）之间的关联。所有患者均来源于同一时期接受外科夹闭术的aSAH患者队列，研究者未对任何临床干预措施进行分配。
为保障研究质量与结果的可靠性，实施了以下质量控制措施，①明确的患者队列：制定了严格且客观的纳入与排除标准（见“1.1”），减少选择偏倚；②标准化的变量与结局定义：对关键变量，如WFNS分级、Fisher分级、DCI等，均采用国际共识定义，确保数据采集的规范性与一致性，降低信息偏倚；③盲法终点判定：DCI结局事件由不知患者详细暴露信息（如具体血压测量值）的一个神经外科专家小组，根据预先设定的标准进行裁定，以最大限度减少确认偏倚；④控制混杂因素：在统计分析中，采用多因素Logistic回归模型，对识别出的潜在混杂因素进行统计学校正，以更准确地评估各预测因子与DCI的独立关联；⑤内部验证：通过数据集随机分割及Bootstrap重采样技术，对建立的预测模型进行内部验证，评估其泛化能力和稳定性。
1.5　统计学分析
本研究采用R（4.2.1）软件进行统计分析。连续变量以M（P25，P75）表示，采用Mann-Whitney U检验进行比较；分类变量以频数表示，采用χ²检验或Fisher确切概率法分析。为构建预测模型，将aSAH患者按7∶3比例随机分为训练集和验证集。在训练集中，首先通过LASSO回归（二项分布，分类测量）筛选潜在预测因子，随后将筛选出的变量纳入Logistic回归分析，保留P<0.05的变量建立多因素模型。采用R软件构建列线图预测模型，并通过一致性统计量（C-statistic）评估模型区分度。使用1 000次Bootstrap重采样绘制校准曲线，分析预测概率与实际观测频率的一致性，同时计算受试者工作特征曲线下面积（area under the curve，AUC）评估模型预测效能。P<0.05为差异具有统计学意义。最后通过决策曲线分析（decision curve analysis，DCA）评估该模型的临床适用性。
2　结果
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2.1　人群特征
本研究采用描述性统计与组间比较方法，分析了纳入患者的基线临床特征。最终共纳入272例行动脉瘤夹闭术的aSAH患者，其中98例（36%）发生DCI，队列筛选流程见图1。与非DCI组相比，DCI组患者年龄更轻（中位数：55.00 vs 59.00岁，P=0.014）、女性占比更低（57.14% vs 69.54%，P=0.039），且术后第二天MAP更高（中位数：98.00 mmHg vs 92.00 mmHg，P<0.001），各组详细特征见表1。提示发生DCI的患者群体更为年轻，且术后早期血压水平更高。
2.2　单因素与多因素回归分析结果
本研究采用LASSO回归（λmin=0.018）结合10折交叉验证筛选变量，并运用多因素Logistic回归分析识别DCI的独立预测因素。LASSO回归初步筛选出8个潜在预测因子（表2）。单因素分析显示，性别（P=0.040）、年龄（P=0.021）、WFNS分级Ⅰ~Ⅱ级（P<0.001）、Fisher分级（P<0.001）、去骨瓣减压术（P<0.001）以及术后第二天MAP（P<0.001）与DCI显著相关。多因素Logistic回归分析最终确定4个独立预测因素：年龄（OR=0.97，95%CI：0.94~0.99； P=0.036）、WFNS分级（OR=1.36，95%CI：1.02~1.82； P=0.038）、Fisher分级（OR=1.44，95%CI：1.01~2.05； P=0.048）及术后第二天MAP（OR=1.06，95%CI：1.03~1.09； P<0.001）。上述结果提示，术后早期的血流动力学变化（表现为MAP升高）以及入院时的病情严重程度评分（WFNS和Fisher分级）是预测DCI发生的独立危险因素。
2.3　预测列线图的构建与验证
本研究采用随机分组方法将272例患者分为训练集（190例）和验证集（82例）。基于多因素Logistic回归分析结果，选取年龄、WFNS分级、Fisher分级及术后第二天MAP 4个独立预测因子构建预测模型。利用训练集数据建立了整合上述因素的列线图，各因素对应分值总和越高，DCI发生风险越大（图2）。模型在训练集中的预测一致性指数为0.739（95%CI：0.668~0.810），在验证集中为0.760（95%CI：0.647~0.872），见图3。通过Bootstrap法绘制的校准曲线显示，实际预测曲线与理想曲线偏差较小（图4），表明模型预测概率与实际观察值具有良好的一致性，提示该列线图模型对DCI风险具有良好的预测区分度与校准度。
2.4　列线图的临床应用
本研究通过绘制DCA评估模型的临床净获益。结果显示，在0.15~0.65的阈值概率范围内，该模型的净获益率均高于“全部治疗”与“全部不治疗”策略，且在0.3~0.5的中等风险区间，其净获益显著优于单独应用WFNS分级（提高12.7%，P<0.01）或Fisher分级（提高9.3%，P<0.05），见图5。模型诊断的敏感性为82.3%（95%CI：76.5~87.1），特异性为74.6%（95%CI：68.2~80.1）。提示本预测模型在广泛的决策阈值内具有显著的临床实用价值。
3　讨论
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上述结果证实，aSAH后DCI仍是常见的严重并发症。其发生率与入院时病情严重程度密切相关，即WFNS分级（Ⅳ~Ⅴ级）与改良Fisher分级（3~4级）越高，DCI发生风险显著增加，此结果与多数既往研究结论一致。传统上，脑血管痉挛被视为DCI的核心病理机制，即aSAH后血液代谢产物引发血管收缩与狭窄［3］。然而，临床观察发现仅部分血管痉挛患者会进展为DCI。目前普遍认为，DCI是由多种机制共同参与的结果，包括脑血管痉挛、早期脑损伤、微循环功能障碍伴自动调节能力丧失以及皮质扩散性去极化等［11］。这些机制在时空上交织作用，其中微循环障碍可在出血后72 h内出现。DCI的早期识别对干预至关重要［12-14］，对于临床表现较轻的低级别aSAH患者而言，此任务尤为困难［15］。
开颅动脉瘤夹闭术作为aSAH的常规疗法，其DCI风险相对较高。本研究中DCI发生率为36%（98/272例），与既往报道相符，可能与外科操作对血管的机械刺激及更显著的组织炎症反应有关［8，16］。术后早期通常严格监测血压以防范术区再出血，然而动脉瘤术后的最佳血压管理策略目前尚无定论。虽有研究提示诱导性高血压或可预防DCI，但随机对照试验未能得出确定性结论［16］。有研究报道，病程中持续性血压升高与DCI风险及不良预后相关［17-18］。当缺血发生时，脑灌注压常偏离最佳水平，且脑自动调节功能进一步受损［19-20］。本研究推测，由血管痉挛等因素触发的早期缺血持续加重，最终超越脑组织代偿极限，导致DCI临床发生。因此，早期血压升高或可视为DCI的预警信号，本研究结果支持了这一假设：多因素Logistic回归分析确认术后第二天MAP升高是DCI的独立危险因素，这可能反映了早期缺血进程的启动，也与“48 h内超早期血管痉挛与DCI相关”的观点相互印证［21］。
本研究中，年龄是与常规认知不同的一个影响因素。与多数研究认为老年患者风险更高相反，本研究数据显示年轻患者DCI风险相对更高［22-25］。有研究指出老年患者症状性血管痉挛发生率较低，亦有研究认为年龄并非血管痉挛的关键预测因素［26-27］。年轻患者动脉硬化程度较轻，血管对刺激可能更为敏感，这或是其更易发生痉挛的潜在原因［28］。较高的WFNS分级和Fisher评分通常意味着更严重的病情。蛛网膜下腔出血的出血量越大，预后往往越差［29-30］。WFNS与Fisher高分级通常代表更严重的初始出血与神经功能损伤，已被广泛证实是DCI的独立预测因素［3，31］，本研究结果与此一致。单因素分析中，去骨瓣减压术与女性性别亦显示与DCI相关，前者可能源于患者本身病情更重，后者则与部分研究结论相符［5，32］。
基于上述发现，本研究成功构建了DCI预测列线图模型。在DCI高发的夹闭术人群中，该模型显示出良好的预测效能。列线图直观提示，高出血严重程度评分结合术后早期血压上升，常预示缺血事件的发生。临床医生借助本模型早期识别高危患者并及时干预，有望改善其预后。但本研究为回顾性队列设计，存在一定局限性：首先，病历回顾可能存在DCI误分类或合并症漏记；其次，样本量有限且缺乏外部验证队列。
综上所述，本研究基于临床易获指标成功构建并验证了一个可用于预测aSAH夹闭术后DCI风险的列线图模型。该模型不仅再次确认了入院时神经功能与出血严重程度（WFNS分级与Fisher评分）的关键预测价值，更重要的是，揭示了术后早期血压动态变化，特别是术后第二天MAP的升高，可作为DCI早期预警的潜在血流动力学标志。这一整合了患者基线特征与术后即时生理参数的实用工具，为临床医生早期识别高危患者、实施针对性监护与预防性干预提供了客观依据。

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2025年第47卷第23期
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doi: 10.16016/j.2097-0927.202507060
  • 接收时间：2025-07-18
  • 首发时间：2026-10-30
  • 出版时间：2025-12-15
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  • 收稿日期：2025-07-18
  • 修回日期：2025-11-13
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    1重庆医科大学附属第二医院神经外科，重庆

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马颖，E-mail：
MA Ying，E-mail：
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
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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