Article(id=1322931548483363830, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322931411006665649, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.13.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766678400000, receivedDateStr=2025-12-26, revisedDate=1779120000000, revisedDateStr=2026-05-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1793341060037, onlineDateStr=2026-10-30, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793341060037, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793341060037, creator=13701087609, updateTime=1793341060037, 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=2204, endPage=2209, ext={EN=ArticleExt(id=1322931549691323383, articleId=1322931548483363830, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Current status of cardiovascular and cerebrovascular comorbidity management in cancer survivors and comprehensive prevention and control strategies, columnId=1322931449141276825, journalTitle=Chinese Nursing Research, columnName=Chronic Disease Topic, runingTitle=null, highlight=null, articleAbstract=

This paper reviewed the epidemiology,comorbidity mechanisms,risk factors, prediction models,and prevention and control strategies for cardiovascular and cerebrovascular comorbidity in cancer survivors.The aim is to provide insights for the co-management,co-prevention,and co-treatment of these comorbidity in cancer survivors,thereby supporting the enhancement of chronic disease prevention and control effectiveness under the Healthy China strategy.

, authors=Ruzhen LUO1, 2, Cailian JI3, Xinyu REN3, Jiahuan GUO3, Hongyu SUN4, Yanhui LIU3, authorsList=Ruzhen LUO, Cailian JI, Xinyu REN, Jiahuan GUO, Hongyu SUN, Yanhui LIU, authorCompany=null, correspAuthors=Hongyu SUN, Yanhui LIU, 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=1322931550668596217, articleId=1322931548483363830, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=癌症生存者心脑血管共病管理现状与综合防控策略, columnId=1322931450881912987, journalTitle=护理研究, columnName=慢病专题, runingTitle=null, highlight=null, articleAbstract=

从癌症生存者心脑血管共病流行病学现状、共病机制、风险因素、预测模型及防控策略方面进行综述,旨在为实现癌症生存者心脑血管共病共管、同防同治,助力“健康中国”战略下慢性病防控效能提升。

, authors=罗如珍1, 2, 吉彩莲3, 任鑫雨3, 郭佳欢3, 孙宏玉4, *, *, 刘彦慧3, *, *, authorsList=罗如珍, 吉彩莲, 任鑫雨, 郭佳欢, 孙宏玉, 刘彦慧, authorCompany=null, correspAuthors=孙宏玉, 刘彦慧, authorNote=

罗如珍,讲师

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孙宏玉,E-mail:
刘彦慧,E-mail:
SUN Hongyu, E-mail:
LIU Yanhui, E-mail:
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癌症生存者心脑血管共病管理现状与综合防控策略
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罗如珍 1, 2 , 吉彩莲 3 , 任鑫雨 3 , 郭佳欢 3 , 孙宏玉 4, *, * , 刘彦慧 3, *, *
护理研究 | 慢病专题 2026,40(13): 2204-2209
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护理研究 |慢病专题 2026 , 40 (13) : 2204 -2209
癌症生存者心脑血管共病管理现状与综合防控策略
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罗如珍1, 2, 吉彩莲3, 任鑫雨3, 郭佳欢3, 孙宏玉4, *, * , 刘彦慧3, *, *
作者信息
  • 1天津医科大学护理学院,天津 300070
  • 2中南大学湘雅护理学院
  • 3天津中医药大学护理学院
  • 4北京大学护理学院
通讯作者:
孙宏玉,E-mail:
刘彦慧,E-mail:
SUN Hongyu, E-mail:
LIU Yanhui, E-mail:
作者简介:

罗如珍,讲师

Current status of cardiovascular and cerebrovascular comorbidity management in cancer survivors and comprehensive prevention and control strategies
Ruzhen LUO1, 2, Cailian JI3, Xinyu REN3, Jiahuan GUO3, Hongyu SUN4 , Yanhui LIU3
Affiliations
  • 1School of Nursing, Tianjin Medical University, Tianjin 300070 China
  • 2Xiangya School of Nursing, Central South University
  • 3School of Nursing, Tianjin University of Traditional Chinese Medicine
  • 4School of Nursing, Peking University
出版时间: 2026-07-10 doi: 10.12102/j.issn.1009-6493.2026.13.002
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从癌症生存者心脑血管共病流行病学现状、共病机制、风险因素、预测模型及防控策略方面进行综述,旨在为实现癌症生存者心脑血管共病共管、同防同治,助力“健康中国”战略下慢性病防控效能提升。

癌症  /  心脑血管疾病  /  共病  /  防控  /  风险因素  /  预测模型  /  综述

This paper reviewed the epidemiology,comorbidity mechanisms,risk factors, prediction models,and prevention and control strategies for cardiovascular and cerebrovascular comorbidity in cancer survivors.The aim is to provide insights for the co-management,co-prevention,and co-treatment of these comorbidity in cancer survivors,thereby supporting the enhancement of chronic disease prevention and control effectiveness under the Healthy China strategy.

cancer  /  cardiovascular and cerebrovascular diseases  /  comorbidity  /  prevention and control  /  risk factors  /  prediction models  /  review
罗如珍, 吉彩莲, 任鑫雨, 郭佳欢, 孙宏玉, 刘彦慧. 癌症生存者心脑血管共病管理现状与综合防控策略. 护理研究, 2026 , 40 (13) : 2204 -2209 . DOI: 10.12102/j.issn.1009-6493.2026.13.002
Ruzhen LUO, Cailian JI, Xinyu REN, Jiahuan GUO, Hongyu SUN, Yanhui LIU. Current status of cardiovascular and cerebrovascular comorbidity management in cancer survivors and comprehensive prevention and control strategies[J]. Chinese Nursing Research, 2026 , 40 (13) : 2204 -2209 . DOI: 10.12102/j.issn.1009-6493.2026.13.002
心脑血管疾病与癌症分别居全球疾病死因首位和第2位,严重危害人类健康。全球心血管疾病负担报告显示,2022年,全球心脑血管疾病死亡病例1 980万例[1];2023中国心血管健康与疾病报告显示,心脑血管疾病是我国居民疾病死亡的首要原因,每5例死亡病例中就有2例死于心脑血管疾病[2]。2024全球癌症统计报告(global cancer statistics)[3]显示,2022年,全球癌症新发病例1 996万例,死亡病例974万例,中国新发病例482.5万例,死亡257.4万例,其发病率和死亡率居全球首位。心脑血管疾病和癌症的高发病率和高死亡率,加重家庭和社会经济负担,已成为亟待解决的重大公共卫生问题。
癌症诊疗技术的进步显著改善了生存者预后,其生存期不断延长。然而,癌症治疗相关毒性和癌症本身可能加速细胞衰老,增加心脑血管疾病风险,包括脑卒中、心力衰竭、心肌梗死、缺血性心脏病、外周血管疾病和瓣膜疾病。英国癌症登记数据(2022)显示[4],肺癌生存者心脑血管共病率达43.6%。一项对15 792例美国癌症生存者前瞻性队列研究发现,癌症生存者心脑血管疾病风险增加42%[5]。我国一项纳入710 170例癌症生存者的队列研究发现,18%的癌症生存者存在心脑血管危险因素或患有心脑血管疾病[6]。一项来自上海社区17 948例老年癌症生存者的队列研究显示,60.1%的老年肿瘤生存者合并有心脑血管疾病[7]。心脑血管疾病已成为所有成年癌症生存者癌症相关死亡以外最常见的死因。
癌症与心脑血管疾病共存显著加重了癌症生存者的疾病负担。一项对4 519 243例中位随访时间为11.8年的大规模回顾性研究发现,癌症生存者伴随脑组织、心力衰竭导致的死亡风险分别增加44%和62%[8]。英国癌症登记数据显示,癌症生存者因心脑血管疾病的再入院率增加23.2%[4],面临更高的过早死亡风险[9]。美国流行病学监测数据发现,前列腺癌、甲状腺癌、睾丸癌以及霍奇金淋巴瘤的心脑血管疾病死亡率已超过癌症死亡率[10]。癌症生存者合并心脑血管疾病的5年和8年生存率分别为75%、60%,低于未合并心脑血管疾病的癌症生存者87%、81%[11]。由此可见,癌症生存者心脑血管共病严重影响癌症生存率。加强癌症生存者心脑血管疾病综合防治,能够有效降低其发病率和死亡率,减少再入院率,减轻疾病负担。
癌症生存者心脑血管共病风险不断升高,这种关联存在共同的疾病机制、危险因素等。明确癌症与心脑血管疾病之间的关系,对于疾病的预防、筛查、诊断、治疗、康复和护理至关重要,对公共卫生管理意义重大。
癌症生存者心脑血管共病的发生并非单一因素作用,而是肿瘤本身、治疗干预与宿主因素形成的复杂交互网络共同驱动的结果,其机制研究已从宏观关联深入到分子调控层面。肿瘤自身的病理生理改变构成重要协同因素。肿瘤可通过直接侵犯、转移、副肿瘤综合征、高凝状态及恶病质等多种途径直接或间接损伤心脑血管系统。恶性肿瘤引发的慢性炎症状态可促进动脉粥样硬化斑块形成,其释放的血管活性肽能直接调节血管张力,而血液高凝状态则增加血栓性疾病风险。老年、肥胖、吸烟等传统心血管风险因素与肿瘤相关因素存在叠加效应。
肿瘤治疗引起的毒性反应,尤其放疗、化疗和靶向治疗引起的心脏毒性和血管损伤,导致不可逆的高血压、冠心病、心力衰竭和脑卒中等心脑血管疾病,使治疗暂停或终止,严重影响癌症生存者预后。诸多抗肿瘤药物均会诱导癌症生存者出现高血压,以血管内皮生长因子抑制剂为例,使用该药物病人的高血压发生率最高可达90%[12-13]。美国心脏协会数据显示,癌症治疗相关高血压在不同药物之间的发生率为8%~90%,且其中约10%会发展成为重度高血压[12],既增加了心血管毒性(如心力衰竭、肺动脉高压等)风险,也会导致癌症治疗的中断或停止。化疗药物中,蒽环类药物(如阿霉素)通过诱导心肌细胞氧化应激和线粒体损伤,直接导致不可逆心肌纤维化,且风险随累积剂量增加而呈指数级上升。放射治疗的心血管毒性具有迟发性特点,胸部放疗可损伤颈动脉窦压力感受器及冠状动脉内皮,腹部放疗则易引发肾动脉狭窄,二者均通过激活肾素-血管紧张-醛固酮系统诱发高血压。靶向药物中,血管内皮生长因子抑制剂可导致血管舒缩因子失衡,而酪氨酸激酶抑制剂的血压升高效应常延迟数月出现且持续数年。
多种因素复杂交织、协同作用,导致癌症生存者心脑血管共病不良健康结局。随着肿瘤心脏学的发展,癌症与心脑血管疾病的共同风险因素逐渐受到关注,通过健康的生活方式、药物治疗等干预措施控制危险因素,从而降低心脑血管疾病和癌症的发病风险。
高血压既是心血管疾病的重要危险因素,也是多种癌症的重要危险因素。高脂血症与结直肠癌、乳腺癌风险升高存在关联。英国一项纳入384 862名参与者的研究显示,三酰甘油水平与结直肠癌风险呈正相关,高密度脂蛋白水平与结直肠癌风险呈负相关[14]。一项纳入26项前瞻性研究的Meta分析发现,高密度脂蛋白水平与乳腺癌风险呈负相关,而其他血脂指标相关性无统计学意义[15]。一项纳入超过86 000名参与者的前瞻性队列研究显示,高血压与癌症发病率和死亡率升高均相关[16]。数据显示,收缩压每升高10 mmHg,乳腺癌生存者的缺血性心脏病风险升高23%,脑卒中发生风险升高45%[17]。一项纳入86项队列研究的Meta分析结果显示,高血压与肾细胞癌、结直肠癌和乳腺癌之间存在关联[18]。
糖尿病是癌症发病的风险因素之一。美国糖尿病协会共识指出,糖尿病与结肠直肠癌、乳腺癌、子宫内膜癌、肝癌、胰腺癌和膀胱癌有关[19]。糖尿病与心脑血管疾病发生关系密切。一项针对963 648例2型糖尿病病人的回顾性队列研究发现,2型糖尿病与心血管死亡率及全因死亡率升高相关[20]。一项纳入129项研究的Meta分析显示,糖耐量异常、糖耐量减低或空腹血糖异常与复合心血管疾病、冠状动脉疾病、脑卒中及全因死亡率风险升高相关[21]。
肥胖是癌症、心脑血管疾病危险因素,同时加剧高血压、高脂血症和糖尿病等癌症心脑血管共病危险因素的出现。我国一项对93 606名参与者平均15.01年的随访研究结果显示,腰围、腰臀比、腰高比是结直肠癌的危险因素,发病风险随着腰围的增加而增加[22]。一项对47 739例无癌症女性的随访发现,体质指数、腰围和腰臀比与甲状腺癌发病率呈正相关[23]。一项对英国生物库31 855份遗传数据分析发现,高体质指数的遗传易感性与心脑血管疾病负担较高有关[24]。一项对373例病人平均随访6.2年研究发现,体质指数<25 kg/m2者疑似心肌梗死发生率和心血管死亡率高于25~30 kg/m2组和>30 kg/m2组[25]。
吸烟是导致肺癌死亡的主要原因[26],也是急性心肌梗死过早发展的独立风险因素[27]。癌症发病率随饮酒量增加呈剂量依赖性上升,轻度饮酒与心血管疾病风险的关联尚不明确,中重度饮酒与癌症风险升高相关[28]。
我国一项纳入242 095名研究对象的前瞻性队列研究Meta分析表明,不吃早餐与心脑血管疾病和癌症生存者高死亡风险有关[29]。高盐饮食是肿瘤和心脑血管疾病的重要风险因素之一[30]。高饮食摄入量和/或血液中维生素C、类胡萝卜素和α-生育酚(作为水果和蔬菜摄入量的标志物)与心脑血管疾病、癌症和全因死亡率有关。水果、蔬菜、坚果或种子、全谷物、鱼类或海鲜可降低心血管疾病和癌症死亡风险;加工肉类、红肉和含糖饮料会增加心血管疾病风险[28]。
久坐不动、低水平的身体活动与癌症和心脑血管疾病密切相关[31]。增加体力活动与心脑血管疾病和癌症风险降低相关。与心肺适能水平高的人群相比,心肺适能水平低的人群全因死亡率和心血管疾病事件风险均升高。与持续久坐、体力活动水平低或无体力活动的人群相比,坚持活动或从久坐转为活动的病人心血管疾病死亡率降低,长期增加或维持体力活动至关重要。与此同时,增加体力活动也与癌症风险降低相关。体力活动水平高的人群,食管癌、肺癌和肾癌发病风险降低,且癌症特异性死亡率也降低[28]。
年龄、性别和种族/民族是影响癌症和心脑血管疾病发病率的不可改变因素。年龄是心脑血管疾病和癌症的稳定独立影响因素,发达国家78%的癌症诊断年龄在55岁以上。美国黑人男性前列腺癌和女性乳腺癌发病率最高,黑人因脑卒中和心血管疾病的过早死亡率也更高[32]。慢性炎症是结直肠癌的风险因素,合并心脑血管疾病病人的血小板与淋巴细胞比值较低[33]。社会心理压力与癌症、动脉粥样硬化性心血管疾病密切相关[31];烟草暴露、空气污染也是癌症与心脑血管疾病的共同风险因素[34]。
风险预测模型考虑多种风险因素的累积效应,为制定关口前移、预防为主、防治结合的综合防控策略提供精准靶点,从而达到预防共病发生、控制共病发展、提高生存质量的目的。目前,国内外学者已建立较为成熟的癌症和心脑血管单一病种风险预测模型,针对癌症生存者心脑血管共病风险预测较少。
在癌症风险预测方面,哈佛癌症指数法最为经典。Pera等[35]纳入了2014—2021年西班牙6个地区39所机构的3 182例和260例胃癌病人作为测试队列和验证队列,利用机器学习构建胃癌根治性切除术后90 d死亡率的风险预测模型,受试者工作特征曲线下面积(AUC)为0.844,具有良好的预测效能,可用于预测胃癌手术后90 d死亡率的风险。Yang等[36]利用美国癌症监测及流行病学数据库,纳入了2010—2020年确诊的9 739例三阴型乳腺癌病人,通过单因素和多因素Cox回归分析筛选风险因素,构建包含年龄、肺转移和脑转移等因素的列线图模型,校准曲线显示模型预测效果良好,能够准确预测远端转移浸润性导管癌相关死亡风险。国内学者Shi等[37]基于247例乳腺癌术后化疗病人构建心理困扰列线图模型以预测病人心理困扰的发生,发现经济、外形、远处转移、疼痛评分和疲劳是乳腺癌手术后化疗病人心理困扰的预测因素,构建的模型具有良好的预测效能。
在心脑血管疾病风险预测方面,Heart SCORE评分系统、Framingham心脏病危险评分及基于“China-PAR”的中国心脑血管疾病发病风险预测模型较为典型。侯玉梅等[38]采用决策树和Logistic回归方法构建脑卒中患病风险预测模型,决策树模型AUC为0.840,灵敏度为0.877,特异度为0.873;Logistic回归模型中,AUC为0.914,灵敏度为0.861,特异度为0.907。决策树模型与Logistic模型对预测缺血性脑卒中的患病风险具有一定的应用价值。You等[39]利用英国生物银行473 611名参与者12.2年的中位随访数据,采用多种机器学习分类器建立包含年龄、性别、胆固醇和血压药物等10个预测因子的首发心脑血管疾病风险预测模型,AUC为0.762,性能优于多个现有临床模型,Brier得分为0.057,具备良好的校准度。在心肌梗死(AUC=0.774)和缺血性脑卒中(AUC=0.730)预测性能良好,但对出血性脑卒中(AUC=0.644)预测较差。Aswath等[40]采用QRESEARCH风险估算器(QRISK3)和弗雷明汉风险评分(FRS)估算18 837例10~30岁1型和2型糖尿病病人10年心脑血管疾病发病风险,QRISK3和FRS预测模型的AUC分别为0.788和0.746,能很好地预测糖尿病病人10年的心血管疾病风险。
在癌症心脑血管共病风险预测方面,Kim等[41]利用韩国4所医疗中心1 256例女性乳腺癌病人队列数据,采用Cox比例风险回归模型构建乳腺癌生存者心脑血管不良事件预测评分模型,预测因素包括充血性心力衰竭、高血压、老年人、心肌梗死/外周动脉闭塞性疾病、肥胖、肾衰竭、脂质异常、糖尿病、左乳房放疗、蒽环类药物剂量和短暂性缺血性发作/脑卒中,3年和7年的时间依赖性C指数分别为0.876和0.842,用于乳腺癌生存者主要心血管事件预测效果良好。Chi等[42]采用Cox回归模型对28 539例女性乳腺癌生存者平均6.1年随访数据进行分析,构建列线图,建立肿瘤特征(肿瘤大小和分期)对心血管疾病生存的预测模型,内部验证和外部验证的C指数分别为0.780和0.809,校准曲线显示了实际观察和预测结果之间的一致性,能够预测化疗或放疗乳腺癌生存者心脑血管疾病死亡风险。Menotti等[43]利用1 611例男性的61年随访数据,通过Cox回归模型构建癌症和心脑血管死亡风险预测模型。在癌症预测模型中,预测因素包括年龄、吸烟习惯、母亲早逝、角膜弧、黄褐斑、糖尿病、臂围和健康饮食。在主要心血管疾病及其亚群的相应模型中,只有年龄和吸烟习惯有统计学意义。使用Fine-Gray方法进行竞争性风险分析后,癌症和心脑血管死亡的共同风险因素有年龄、吸烟和黄褐斑。Qi等[44]利用国家健康与营养调查10 064名参与者数据,通过回归树、随机森林、贝叶斯和光梯度提升机等机器学习方法构建癌症生存者心脑血管共病预测模型,钠、镁、黄素、角酚、橙皮素、硒、马尔维丁和维生素C是预测心血管疾病-癌症合并症的重要因素,光梯度提升机实现了较高的预测精度,为87.9%,分类误差率为12.1%,AUC为0.951,灵敏度和特异性均接近88%。
癌症生存者心脑血管共病的发生发展是一个涉及多因素、多阶段、多维度的动态过程,目前疾病风险预测模型依赖于个体某一时间点的变量值,基于静态数据预测动态风险会发生偏倚甚至失效。动态风险预测模型是精准防控策略的核心、解析癌症生存者心脑血管共病变化趋势、构建精准的动态风险预测模型是癌症心脑血管共病精准防控的迫切需要。
目前,癌症或心脑血管疾病单一病种的管理已较为成熟,国家卫生健康委联合多部门制定了《健康中国行动-癌症防治行动实施方案(2023—2030年)》和《健康中国行动-心脑血管疾病防治行动实施方案(2023—2030年)》,围绕癌症和心脑血管疾病的综合防治核心,分别从危险因素综合防控、癌症防治服务体系、癌症筛查和早诊早治能力等方面细化癌症防治工作任务;从危险因素控制、加大健康教育力度、心脑血管疾病综合检测、创新同防同治路径等方面明确心脑血管疾病防治工作。强调关口前移、共建共享、同防同治策略,实现以治病为中心向以健康为中心转变。
上海自2018年逐步开展慢性病健康管理支持中心(chronic disease management center,CDMC)建设,以“共病管理”为切口,以社区慢性病健康管理信息系统为支撑,覆盖高血压、糖尿病、癌症等多种常见慢性病的联合风险评估、筛查、干预及随访等共病管理项目[45]。福建三明、浙江宁波等地以居民健康档案为核心,整合公共卫生服务、临床诊疗、物联网等健康管理数据,形成综合管理数据库和平台,运用人工智能等技术支持人群的风险评估和分类管理、慢性病防治效果评价以及个性化健康服务提供,打造整合型信息平台支持紧密型医疗联合体、医共体的分级诊疗以及院内、院外一体化慢性病的预防、诊疗和管理[46]。
癌症心脑血管共病管理错综复杂,其共同的风险因素及关联机制为共病协同防控提供靶点。《欧洲心脏病学会肿瘤心脏病学指南2022版》[47]指出,应在癌症治疗开始前评估心脑血管风险,控制各项风险因素,为存在心脑血管疾病风险的癌症生存者提供全生命周期的健康管理。对于接受蒽环类化疗或胸部放疗的高风险人群,治疗前进行全面基线评估,包括左心室射血分数、心肌应变参数等影像学指标,以及肌钙蛋白、脑钠肽等生物标志物检测。
《中国肿瘤共病防治专家共识(2025版)》[48]指出,对拟接受具有心血管毒性风险的抗肿瘤治疗生存者,应建立贯穿治疗全过程的系统性心血管监测体系。治疗前,所有病人均需完成包括详细病史采集、体格检查、心电图、心肌生物标志物及心脏超声在内的基线评估,控制各项风险因素至稳定状态。治疗中,需依据风险分层与方案毒性,定期、动态复查上述指标。抗肿瘤治疗中需严密监测,并依据心功能实施分级干预。同时,注重心血管药物的合理调整,在确保肿瘤治疗有效性的前提下,最大程度地保护心血管功能,并优化共病药物治疗的剂量。同时,建立以肿瘤内科医生、放射肿瘤科医生、心脏科医生、药师、影像科医生、病理科医生、全科医生及其他相关专科医生组成的多学科协作团队为核心的全流程动态管理路径。《肿瘤相关高血压整合管理中国专家共识(2024年版)》[49]特别强调肿瘤相关高血压的主动监测策略,建议在抗肿瘤治疗前、中、后进行全生命周期血压监测,对使用高风险药物生存者采用动态血压监测,评估血压变异性,推动建立“降压治疗-抗肿瘤治疗-长期随访”的整合管理路径。
癌症生存者心脑血管共病的防控逐步实现从“疾病治疗”向“健康维护”的转变。尽管目前研究已关注到癌症生存者心脑血管共病防控,但仍面临诸多挑战,如缺乏大型前瞻性临床研究证据、肿瘤生存者心脑血管风险评估体系尚未完善、基层医疗机构的筛查技术与规范执行度有待提升、癌症生存者对自身心血管风险认知不足、卫生经济负担仍然较重。未来可建立标准化的长期随访体系与指南,进一步深化机制研究,通过多组学技术挖掘肿瘤-心脑血管交互的关键分子靶点;借助大数据、物联网等新一代信息技术,利用机器学习算法整合影像学、生物标志物及临床数据,构建更精准的动态风险预测模型;通过可穿戴设备实现心率、血压等指标的远程监测,创新健康管理模式。
癌症生存者心脑血管共病负担沉重,已成为危及人类生命健康的重要公共卫生问题。在癌症防控方面,我国癌症防控体系不断完善,但目前研究多集中于对癌症或心脑血管疾病单一病种防控,尚未形成成熟的癌症心脑血管共病智能管理模式,高质量推进癌症生存者心脑血管共病协同管理是有效降低癌症疾病负担的重要途径。因此,以《“健康中国2030”规划纲要》和《健康中国行动(2019-2030)》为指导,聚焦癌症生存者心脑血管共病共管、同防同控问题,借助新质生产力,实现对癌症生存者心脑血管共病的智能管理,以期降低癌症生存者心脑血管共病发生率和死亡率,减少再入院率,提高长期生存率;同时,提高医疗服务效率和针对性,提升卫生经济学效益。

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2026年第40卷第13期
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doi: 10.12102/j.issn.1009-6493.2026.13.002
  • 接收时间:2025-12-26
  • 首发时间:2026-10-30
  • 出版时间:2026-07-10
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  • 收稿日期:2025-12-26
  • 修回日期:2026-05-19
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    1天津医科大学护理学院,天津 300070
    2中南大学湘雅护理学院
    3天津中医药大学护理学院
    4北京大学护理学院

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孙宏玉,E-mail:
刘彦慧,E-mail:
SUN Hongyu, E-mail:
LIU Yanhui, E-mail:
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2种不同金属材料的力学参数
科
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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