Constructing a Risk Prediction Model of Breast Cancer-related Lymphedema Based on a Meta-analysis of Prospective Cohort Studies

doi:10.12114/j.issn.1007-9572.2022.0827

Chinese General Practice ›› 2023, Vol. 26 ›› Issue (17): 2078-2088.DOI: 10.12114/j.issn.1007-9572.2022.0827

Special Issue: 乳腺癌最新文章合集；肿瘤最新文章合集

• Original Research • Previous Articles Next Articles

Constructing a Risk Prediction Model of Breast Cancer-related Lymphedema Based on a Meta-analysis of Prospective Cohort Studies

1. Division of Medical & Surgical Nursing, School of Nursing, Peking University/Evidence-based Nursing Center, Peking University Health Science Center, Beijing 100191, China
2. Department of Nursing, Tianjin Medical University Cancer Institute & Hospital/National Clinical Research Center for Cancer/Key Laboratory of Cancer Prevention and Therapy, Tianjin/Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China

Received:2022-08-08 Revised:2022-12-10 Published:2023-06-15 Online:2023-01-18
Contact: LU Qian

基于前瞻性队列研究的Meta分析构建乳腺癌相关淋巴水肿风险预测模型研究

1．100191　北京市，北京大学护理学院内外科教研室　北京大学循证护理研究中心2．300060　天津市，天津医科大学肿瘤医院护理部　国家肿瘤临床医学研究中心　天津市"肿瘤防治"重点实验室　天津市恶性肿瘤临床医学研究中心

通讯作者: 路潜
作者简介:作者贡献：沈傲梅负责研究设计、文献检索、筛选、评价、资料提取分析及原稿写作；符鑫、韦小夏、卞静如、张丽媛负责文献检索、筛选、评价、资料提取及数据分析；路潜、强万敏、庞冬负责研究设计及方法、论文审阅及修订；所有作者确认论文终稿。
基金资助:
国家自然科学基金面上项目（72174011）——健康生态学视角下乳腺癌淋巴水肿风险预测与精准干预策略研究; 天津医科大学肿瘤医院院级课题（H2001）——基于Triangle模型的乳腺癌患者淋巴水肿分层支持管理标准的制定; '未名护理'领军人才科研创新孵化基金项目（LJRC21ZD03）——基于ITHBC的乳腺癌患者淋巴水肿自我管理行为状况及机制研究

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Abstract

Abstract: Background Lymphedema is a common chronic complication bothering breast cancer patients. Early assessment and prediction of the risk for developing breast cancer-related lymphedema (BCRL) is particularly important. However, there is still a lack of an authoritatively recognized and suitably promoted risk prediction model.Objective To construct and validate a risk prediction model for BCRL based on the results of a meta-analysis.Methods Electronic databases including PubMed, Embase, CINAHL, Scopus, Web of Science, Cochrane Library, CNKI, CBM, and Wanfang Data, were searched for prospective cohort studies on risk factors of BCRL from inception to November 2021. Two systematically trained researchers independently screened the literature, extracted data, and assessed the study quality using the Newcastle-Ottawa Scale. Stata 17.0 was used for meta-analysis. The risk factors with significant pooled effect size and their combined risk value were extracted to construct the Logistic risk prediction model. The Logistic and additive risk scoring models were constructed based on regression coefficients and pooled risk values, respectively. The data of 486 breast cancer patients recruited in the breast cancer research center of Peking University People's Hospital, from April 2017 to December 2018, were selected as the validation set. The area under the ROC curve (AUC) and the Hosmer-Lemeshow test were used to evaluate the prediction performance of the risk scoring model. Decision curve analysis was used to evaluate the clinical practicability.Results A total of 49 prospective cohort studies involving 32 543 breast cancer patients were included. Meta-analysis showed that the incidence of BCRL was 20.6%〔95%CI (17.9%, 23.3%) 〕. Among 49 studies, five risk factors with significant pooled effect sizes were reported more than five times, including body mass index (BMI) 〔RR=1.777, 95%CI (1.515, 2.085) 〕, type of breast surgery〔RR=1.320, 95%CI (1.125, 1.549) 〕, type of axillary surgery〔RR=3.058, 95%CI (2.325, 4.020) 〕, radiotherapy〔RR=1.620, 95%CI (1.214, 2.160) 〕, and postoperative complications〔RR=2.373, 95%CI (1.278, 4.405) 〕. The total score for the Logistic risk scoring model ranged from 0 to 34, and that for the additive risk scoring model ranged from 5 to11. The AUCs of Logistic and additive risk scoring models were 0.748〔95%CI (0.701, 0.794) 〕and 0.737〔95%CI (0.691, 0.784) 〕, respectively. The values of Hosmer-Lemeshow test were 0.185 and 0.763, respectively. The optimal cut-off value of the Logistic risk scoring model was 18, with a sensitivity of 81.7%, and a specificity of 43.1%. The optimal cut-off value of the additive risk scoring model was 8.5, the sensitivity was 80.9%, and the specificity was 42.8%. When the probability threshold ranged from 20% to 30%, the model achieved higher net clinical benefit. Conclusion The BCRL risk prediction model based on this meta-analysis has good predictive performance. It can be used as a risk assessment tool for BCRL to guide the hierarchical monitoring and management of BCRL. However, prediction performance and clinical practicability of the model still needs to be validated and optimized in future research.

Key words: Breast cancer lymphedema, Breast neoplasms, Risk factors, Prediction model, Meta-analysis, Prospective cohort studies

摘要： 背景乳腺癌相关淋巴水肿（BCRL）是困扰乳腺癌患者术后常见的慢性并发症，早期评估和预测BCRL风险尤为重要，但目前仍缺乏权威公认、适宜推广的风险预测模型。目的本研究拟基于Meta分析构建并验证乳腺癌患者BCRL风险预测模型。方法计算机检索PubMed、Embase、CINAHL、Scopus、Web of Science、Cochrane Library、中国知网、中国生物医学数据库、万方数据知识服务平台自建库至2021年11月发表的有关BCRL危险因素的前瞻性队列研究。由2名经过系统培训的研究者分别独立筛选文献、提取资料，并采用纽卡斯尔-渥太华量表（NOS）进行质量评价。采用Stata 17.0软件进行Meta分析。根据Meta分析结果提取合并效应量具有显著性的风险因素及合并风险值，构建Logistic回归预测模型。基于回归系数及合并风险值构建Logistic和Additive评分模型。选取2017年4月至2018年12月在北京大学人民医院乳腺中心招募的486例乳腺癌术后患者为模型验证集，采用受试者工作特征（ROC）曲线下面积（AUC）、Hosmer-Lemeshow检验评价评分模型的预测性能，采用决策曲线分析评价预测模型的临床实用性。结果共纳入49项前瞻性队列研究、32 543例乳腺癌患者。Meta分析结果显示，乳腺癌患者BCRL发生率为20.6%〔95%CI（17.9%，23.3%）〕。49项研究中报告次数>5次且Meta分析合并效应量结果具有显著性的危险因素共5个，分别为：体质指数（BMI）〔RR=1.777，95%CI（1.515，2.085）〕、乳腺手术类型〔RR=1.320，95%CI（1.125，1.549）〕、腋窝手术类型〔RR=3.058，95%CI（2.325，4.020）〕、放疗〔RR=1.620，95%CI（1.214，2.160）〕、术后并发症〔RR=2.373，95%CI（1.278，4.405）〕。Logistic及Additive评分模型总分分别为0~34分、5~11分。Logistic及Additive评分模型的AUC分别为0.748〔95%CI（0.701，0.794）〕、0.737〔95%CI（0.691，0.784）〕，Hosmer-Lemeshow检验P值分别为0.185、0.763。Logistic评分模型最佳截断值为18分，灵敏度为81.7%，特异度为43.1%；Additive评分模型最佳截断值为8.5分，灵敏度为80.9%，特异度为42.8%。当阈值概率在20%~30%时，预测模型具有较高的临床净获益。结论基于Meta分析构建的BCRL风险预测模型具有较好的预测性能，可作为BCRL风险评估工具，指导BCRL的分层管理，但其预测性能和临床实用性仍有待进一步验证和优化。

关键词: 乳腺癌淋巴水肿, 乳腺肿瘤, 危险因素, 风险预测模型, Meta分析, 前瞻性队列研究

SHEN Aomei,LU Qian,FU Xin, et al. Constructing a Risk Prediction Model of Breast Cancer-related Lymphedema Based on a Meta-analysis of Prospective Cohort Studies[J]. Chinese General Practice, 2023, 26(17): 2078-2088. DOI: 10.12114/j.issn.1007-9572.2022.0827.
沈傲梅,路潜,符鑫等. 基于前瞻性队列研究的Meta分析构建乳腺癌相关淋巴水肿风险预测模型研究[J]. 中国全科医学, 2023, 26(17): 2078-2088. DOI: 10.12114/j.issn.1007-9572.2022.0827.

Figures/Tables 8

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步骤	检索式	命中数量（篇）
#1	"Breast Neoplasms" [Mesh]	315 036
#2	"breast cancer" [Title/Abstract] OR "breast neoplasm" [Title/Abstract] OR "breast tumor" [Title/Abstract] OR "breast tumour*" [Title/Abstract] OR "Breast Carcinoma" [Title/Abstract]	328 162
#3	#1 OR #2	409 101
#4	"lymphedema" [MeSH Terms] OR "Breast Cancer Lymphedema" [MeSH Terms]	13 039
#5	"lymphoedema" [Title/Abstract] OR "lymphedema" [Title/Abstract] OR "lymphatic oedema" [Title/Abstract] OR "lymphatic edema" [Title/Abstract] OR "oedema" [Title/Abstract] OR "edema" [Title/Abstract] OR "arm oedema" [Title/Abstract] OR "arm edema" [Title/Abstract] OR "upper extremity oedema" [Title/Abstract] OR "upper limb oedema" [Title/Abstract] OR "upper extremity edema" [Title/Abstract] OR "upper limb edema" [Title/Abstract] OR "breast cancer lymphedema" [Title/Abstract] OR "breast cancer related lymphedema" [Title/Abstract] OR "breast cancer treatment related lymphedema" [Title/Abstract] OR "breast cancer lymphoedema" [Title/Abstract] OR "breast cancer related lymphoedema" [Title/Abstract] OR "breast cancer treatment related lymphoedema" [Title/Abstract] OR "breast cancer related arm lymphedema" [Title/Abstract] OR "lymphedemic" [Title/Abstract] OR "lymphedemic" [Title/Abstract] OR "swelling" [Title/Abstract]	252 930
#6	#4 OR #5	257 765
#7	"Risk Factors" [MeSH Terms] OR "epidemiologic factors" [MeSH Terms]	1 656 289
#8	"risk factor" [Title/Abstract] OR "risk variable" [Title/Abstract] OR "predictor" [Title/Abstract] OR "prediction" [Title/Abstract] OR "predict factor" [Title/Abstract] OR "prognostic factor" [Title/Abstract] OR "prognostic variable" [Title/Abstract] OR "epidemiologic factor" [Title/Abstract] OR "epidemiologic variable" [Title/Abstract] OR "relevant factor" [Title/Abstract] OR "relevant variable" [Title/Abstract] OR "related factor" [Title/Abstract] OR "related variable" [Title/Abstract] OR "contributing factor*" [Title/Abstract]	1 445 977
#9	#7 OR #8	2 646 906
#10	#3 AND #7 AND #11	768
#11	#10 AND （（humans [Filter]） AND （Chinese [Filter] OR English [Filter]））	626

步骤	检索式	命中数量（篇）
#1	"Breast Neoplasms" [Mesh]	315 036
#2	"breast cancer" [Title/Abstract] OR "breast neoplasm" [Title/Abstract] OR "breast tumor" [Title/Abstract] OR "breast tumour*" [Title/Abstract] OR "Breast Carcinoma" [Title/Abstract]	328 162
#3	#1 OR #2	409 101
#4	"lymphedema" [MeSH Terms] OR "Breast Cancer Lymphedema" [MeSH Terms]	13 039
#5	"lymphoedema" [Title/Abstract] OR "lymphedema" [Title/Abstract] OR "lymphatic oedema" [Title/Abstract] OR "lymphatic edema" [Title/Abstract] OR "oedema" [Title/Abstract] OR "edema" [Title/Abstract] OR "arm oedema" [Title/Abstract] OR "arm edema" [Title/Abstract] OR "upper extremity oedema" [Title/Abstract] OR "upper limb oedema" [Title/Abstract] OR "upper extremity edema" [Title/Abstract] OR "upper limb edema" [Title/Abstract] OR "breast cancer lymphedema" [Title/Abstract] OR "breast cancer related lymphedema" [Title/Abstract] OR "breast cancer treatment related lymphedema" [Title/Abstract] OR "breast cancer lymphoedema" [Title/Abstract] OR "breast cancer related lymphoedema" [Title/Abstract] OR "breast cancer treatment related lymphoedema" [Title/Abstract] OR "breast cancer related arm lymphedema" [Title/Abstract] OR "lymphedemic" [Title/Abstract] OR "lymphedemic" [Title/Abstract] OR "swelling" [Title/Abstract]	252 930
#6	#4 OR #5	257 765
#7	"Risk Factors" [MeSH Terms] OR "epidemiologic factors" [MeSH Terms]	1 656 289
#8	"risk factor" [Title/Abstract] OR "risk variable" [Title/Abstract] OR "predictor" [Title/Abstract] OR "prediction" [Title/Abstract] OR "predict factor" [Title/Abstract] OR "prognostic factor" [Title/Abstract] OR "prognostic variable" [Title/Abstract] OR "epidemiologic factor" [Title/Abstract] OR "epidemiologic variable" [Title/Abstract] OR "relevant factor" [Title/Abstract] OR "relevant variable" [Title/Abstract] OR "related factor" [Title/Abstract] OR "related variable" [Title/Abstract] OR "contributing factor*" [Title/Abstract]	1 445 977
#9	#7 OR #8	2 646 906
#10	#3 AND #7 AND #11	768
#11	#10 AND （（humans [Filter]） AND （Chinese [Filter] OR English [Filter]））	626

第一作者	发表时间（年）	国家	样本量（例）	年龄（岁）	水肿例数（例）	发生率（%）	诊断标准	危险因素
KUIJER^[14]	2021	美国	888	37（22~40）	122	13.7	①	ABC
KEELY^[15]	2021	美国	1 100	56±12	251	22.8	②	E
KIM^[16]	2021	韩国	910	腋窝淋巴结清扫（n=595）：52.5±10.4；前哨淋巴结活检（n=315）：51.6±10.5	121	13.3	③	ABCD
BIDYUT^[17]	2021	印度	342	47	144	42.1	①	AB
佟洋^[18]	2021	中国	713	水肿（n=61）：55.17±13.26；非水肿（n=652）：54.81±11.59	61	8.6	③	AD
袁芊芊^[19]	2021	中国	312	48	45	14.4	②	AD
BRUNELLE^[20]	2020	美国	647	56.6（27~83）	64	9.9	②	ABCD
BRUNELLE^[21]	2020	美国	1 181	569例≥55，612例<55	129	10.9	②	ABCDE
BUNDRED^[22]	2020	英国	545	55.7±12.4	122	22.4	②	AE
NAOUM^[23]	2020	美国	1 815	56（24.4，87.9）	171	9.4	②	C
王季^[24]	2020	中国	472	水肿（n=23）：55.57±13.14；非水肿（n=449）：54.83±11.10	23	4.9	①或③	ACD
ARMER^[25]	2019	美国	486	50.1±10.8	284	58.4	②	AD
GROSS^[26]	2019	美国	265	52.6（42.7，62.1）	39	14.7	③	A
KHANNA^[27]	2019	印度	98	76.8%年龄41~60	23	23.5	③	DE
GROSS^[28]	2018	美国	492	50（40~60）	83	16.9	③	AD
RASTOGI^[29]	2018	印度	100	49.5（21~79）	13	13.0	③	AD
TSAI^[30]	2018	美国	522	63	102	19.5	①或③	ABCD
ZOU^[31]	2018	中国	387	50（24，81）	114	29.4	③	ABCD
张顺康^[32]	2018	中国	197	55.94（28~79）	38	19.3	③	C
RIBEIRO PEREIRA^[33]	2017	巴西	964	55.31±12.98	300	31.1	④	ADE
ZHANG^[34]	2017	中国	2 597	49.9±10.1	277	10.7	③	AE
ZHU^[35]	2017	中国	319	18~75	88	27.6	⑤	B
KWAN^[36]	2016	美国	2 953	非水肿60.0±11.9；水肿患者56.0±11.1	342	11.6	①	—
MENEZES^[37]	2016	巴西	622	258例年龄<50，364例年龄≥50	204	32.8	④	B
WANG^[5]	2016	中国	358	>18	114	31.8	③	CDE
KILBREATH^[38]	2016	澳大利亚	450	56.9±11.2	46	10.2	⑥	D
刘风华^[39]	2016	中国	141	51（24，81）	39	27.7	①或③	ABCD
SWAROOP^[40]	2015	美国	1 121	不清楚	59	5.3	⑦	ACD
BOUGHEY^[41]	2014	美国	124	59（36~85）	38	30.7	⑧	AC
IKEDA^[42]	2014	日本	76	非水肿（n=52）：60.8±13.7；水肿患者（n=24）：55.3±9.6	24	31.6	③	CD
TOGAWA^[43]	2014	美国	666	51.5±7.2	190	28.5	①	AB
JAMMOLLO^[44]	2013	美国	787	56（27~89）	39	5.0	②	ACD
RIDENER^[45]	2011	美国	138	58.9±12.3	27	19.6	②或④	A
AVRAHAM^[46]	2010	美国	316	非乳房重建（n=130）：61（30~83）；乳房重建（n=186）：45（27~74）	34	10.8	③	B
CLOUGH-GORR^[47]	2010	美国	400	>65	145	36.3	①	AB
GOLDBERG^[48]	2010	美国	600	56（24~83）	31	5.2	③	ABCDE
HAYES^[49]	2010	澳大利亚	287	54±10	不清楚	NR	⑥	E
HELYER^[50]	2010	加拿大	137	56（36~78）	16	11.7	④	A
KWAN^[51]	2010	美国	997	水肿（n=133）：55.3±11.1；非水肿（n=864）：55.3±11.1	133	13.3	⑧	ABCD
NORMAN^[52]	2010	美国	631	196例<50，435例≥50	238	37.7	①	ABCDE
YANG^[53]	2010	韩国	183	不清楚	22	12.0	⑨	BCD
MEESK^[54]	2009	美国	494	35~64	120	24.2	①	AC
YEN^[55]	2009	美国	1 338	水肿（n=193）：71.9±5.2；非水肿（n=1 145）：72.9±5.6	193	14.4	①	BCD
HAYES^[56]	2008	澳大利亚	287	54±10	41	23.6	①或⑥	AC
PASKETT^[57]	2007	美国	622	38.5±4.9	336	54.0	①	BCD
WILKE^[58]	2006	美国	2 904	56（23~95）	203	7.0	③	A
NIWIN'SKA^[59]	2005	波兰	174	53（28~76）	17	9.8	③	C
OZASLAN^[60]	2004	土耳其	240	50（28~80）	68	28.3	③	AD
GELLER^[61]	2003	美国	145	水肿（n=55）：49.7±8.5；非水肿（n=90）：59.5±12.4	55	37.9	①	BCD

第一作者	发表时间（年）	国家	样本量（例）	年龄（岁）	水肿例数（例）	发生率（%）	诊断标准	危险因素
KUIJER^[14]	2021	美国	888	37（22~40）	122	13.7	①	ABC
KEELY^[15]	2021	美国	1 100	56±12	251	22.8	②	E
KIM^[16]	2021	韩国	910	腋窝淋巴结清扫（n=595）：52.5±10.4；前哨淋巴结活检（n=315）：51.6±10.5	121	13.3	③	ABCD
BIDYUT^[17]	2021	印度	342	47	144	42.1	①	AB
佟洋^[18]	2021	中国	713	水肿（n=61）：55.17±13.26；非水肿（n=652）：54.81±11.59	61	8.6	③	AD
袁芊芊^[19]	2021	中国	312	48	45	14.4	②	AD
BRUNELLE^[20]	2020	美国	647	56.6（27~83）	64	9.9	②	ABCD
BRUNELLE^[21]	2020	美国	1 181	569例≥55，612例<55	129	10.9	②	ABCDE
BUNDRED^[22]	2020	英国	545	55.7±12.4	122	22.4	②	AE
NAOUM^[23]	2020	美国	1 815	56（24.4，87.9）	171	9.4	②	C
王季^[24]	2020	中国	472	水肿（n=23）：55.57±13.14；非水肿（n=449）：54.83±11.10	23	4.9	①或③	ACD
ARMER^[25]	2019	美国	486	50.1±10.8	284	58.4	②	AD
GROSS^[26]	2019	美国	265	52.6（42.7，62.1）	39	14.7	③	A
KHANNA^[27]	2019	印度	98	76.8%年龄41~60	23	23.5	③	DE
GROSS^[28]	2018	美国	492	50（40~60）	83	16.9	③	AD
RASTOGI^[29]	2018	印度	100	49.5（21~79）	13	13.0	③	AD
TSAI^[30]	2018	美国	522	63	102	19.5	①或③	ABCD
ZOU^[31]	2018	中国	387	50（24，81）	114	29.4	③	ABCD
张顺康^[32]	2018	中国	197	55.94（28~79）	38	19.3	③	C
RIBEIRO PEREIRA^[33]	2017	巴西	964	55.31±12.98	300	31.1	④	ADE
ZHANG^[34]	2017	中国	2 597	49.9±10.1	277	10.7	③	AE
ZHU^[35]	2017	中国	319	18~75	88	27.6	⑤	B
KWAN^[36]	2016	美国	2 953	非水肿60.0±11.9；水肿患者56.0±11.1	342	11.6	①	—
MENEZES^[37]	2016	巴西	622	258例年龄<50，364例年龄≥50	204	32.8	④	B
WANG^[5]	2016	中国	358	>18	114	31.8	③	CDE
KILBREATH^[38]	2016	澳大利亚	450	56.9±11.2	46	10.2	⑥	D
刘风华^[39]	2016	中国	141	51（24，81）	39	27.7	①或③	ABCD
SWAROOP^[40]	2015	美国	1 121	不清楚	59	5.3	⑦	ACD
BOUGHEY^[41]	2014	美国	124	59（36~85）	38	30.7	⑧	AC
IKEDA^[42]	2014	日本	76	非水肿（n=52）：60.8±13.7；水肿患者（n=24）：55.3±9.6	24	31.6	③	CD
TOGAWA^[43]	2014	美国	666	51.5±7.2	190	28.5	①	AB
JAMMOLLO^[44]	2013	美国	787	56（27~89）	39	5.0	②	ACD
RIDENER^[45]	2011	美国	138	58.9±12.3	27	19.6	②或④	A
AVRAHAM^[46]	2010	美国	316	非乳房重建（n=130）：61（30~83）；乳房重建（n=186）：45（27~74）	34	10.8	③	B
CLOUGH-GORR^[47]	2010	美国	400	>65	145	36.3	①	AB
GOLDBERG^[48]	2010	美国	600	56（24~83）	31	5.2	③	ABCDE
HAYES^[49]	2010	澳大利亚	287	54±10	不清楚	NR	⑥	E
HELYER^[50]	2010	加拿大	137	56（36~78）	16	11.7	④	A
KWAN^[51]	2010	美国	997	水肿（n=133）：55.3±11.1；非水肿（n=864）：55.3±11.1	133	13.3	⑧	ABCD
NORMAN^[52]	2010	美国	631	196例<50，435例≥50	238	37.7	①	ABCDE
YANG^[53]	2010	韩国	183	不清楚	22	12.0	⑨	BCD
MEESK^[54]	2009	美国	494	35~64	120	24.2	①	AC
YEN^[55]	2009	美国	1 338	水肿（n=193）：71.9±5.2；非水肿（n=1 145）：72.9±5.6	193	14.4	①	BCD
HAYES^[56]	2008	澳大利亚	287	54±10	41	23.6	①或⑥	AC
PASKETT^[57]	2007	美国	622	38.5±4.9	336	54.0	①	BCD
WILKE^[58]	2006	美国	2 904	56（23~95）	203	7.0	③	A
NIWIN'SKA^[59]	2005	波兰	174	53（28~76）	17	9.8	③	C
OZASLAN^[60]	2004	土耳其	240	50（28~80）	68	28.3	③	AD
GELLER^[61]	2003	美国	145	水肿（n=55）：49.7±8.5；非水肿（n=90）：59.5±12.4	55	37.9	①	BCD

第一作者	人群选择				组间可比性	结局测量			总分	第一作者	人群选择				组间可比性	结局测量			总分
第一作者	①	②	③	④	⑤	⑥	⑦	⑧	总分	第一作者	①	②	③	④	⑤	⑥	⑦	⑧	总分
KUIJER^[14]	1	1	1	1	2	0	0	1	7	KILBREATH^[38]	1	1	1	0	2	1	0	1	7
KEELY^[15]	1	1	0	1	2	1	1	0	7	刘风华^[39]	1	1	1	1	2	1	1	0	8
KIM^[16]	1	1	1	1	2	1	1	1	9	SWAROOP^[40]	1	1	1	1	2	1	1	1	9
BIDYUF^[17]	1	1	1	0	2	1	1	0	7	BOUGHEY^[41]	1	0	1	1	2	1	1	1	8
佟洋^[18]	1	1	0	1	2	1	0	1	7	IKEDA^[42]	1	1	0	1	2	1	1	1	8
袁芊芊^[19]	1	1	1	1	2	1	0	0	7	TOGAWA^[43]	1	1	1	0	2	0	1	1	7
BRUNELLE^[20]	1	1	1	1	2	1	1	1	9	JAMMOLLO^[44]	1	1	1	1	2	1	1	1	9
BRUNELLE^[21]	1	1	1	1	2	1	1	1	9	RIDENER^[45]	1	1	1	1	2	1	1	1	9
BUNDRED^[22]	1	1	0	1	2	1	1	1	8	AVRAHAM^[46]	1	1	1	1	2	1	1	1	9
NAOUM^[23]	1	1	1	1	2	1	1	0	8	CLOUGH-GORR^[47]	1	1	1	1	2	0	1	1	8
王季^[24]	1	1	0	1	2	1	1	0	7	GOLDBERG^[48]	1	1	1	0	2	1	1	1	8
ARMER^[25]	1	1	1	1	2	1	1	1	9	HAYES^[49]	1	1	1	1	2	1	1	1	9
GROSS^[26]	1	1	1	1	2	1	1	1	9	HELYER^[50]	1	1	1	1	2	1	1	0	8
KHANNA^[27]	1	1	0	1	2	1	0	1	7	KWAN^[51]	1	1	1	1	2	1	1	0	8
GROSS^[28]	1	1	1	1	2	1	1	1	9	NORMAN^[52]	1	1	1	1	2	0	1	1	8
RASTOGI^[29]	1	1	0	1	2	1	1	1	8	YANG^[53]	1	1	1	0	2	1	0	1	7
TSAI^[30]	1	1	1	1	2	1	1	1	9	MEESK^[54]	1	1	1	1	2	0	1	1	8
ZOU^[31]	1	1	1	1	2	1	1	0	8	YEN^[55]	1	1	1	1	2	0	1	1	8
张顺康^[32]	1	1	1	1	2	1	1	1	9	HAYES^[56]	1	1	1	1	2	1	1	1	9
RIBEIRO PEREIRA^[33]	1	1	1	1	2	1	1	1	9	PASKETT^[57]	1	1	1	0	2	0	1	1	7
ZHANG^[34]	1	1	1	1	2	1	1	1	9	WILKE^[58]	1	1	0	0	2	1	1	1	7
ZHU^[35]	1	1	0	1	2	1	1	0	7	NIWIN'SKA^[59]	1	1	0	1	2	1	1	0	7
KWAN^[36]	1	1	1	1	2	1	1	0	8	OZASLAN^[60]	1	1	1	1	2	1	1	1	9
MENEZES^[37]	1	1	1	1	2	1	1	1	9	GELLER^[61]	1	1	1	1	2	0	1	1	8
WANG^[5]	1	1	1	1	2	1	0	1	8

Constructing a Risk Prediction Model of Breast Cancer-related Lymphedema Based on a Meta-analysis of Prospective Cohort Studies

基于前瞻性队列研究的Meta分析构建乳腺癌相关淋巴水肿风险预测模型研究

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危险因素	分组	研究数量（篇）	病例数（例）	样本量（例）	模型	I²（%）	P值	RR（95%CI）	Egger's P值	β
BMI	≥30 kg/m²与<30 kg/m²	14^{[15,18,20,22,24,33,43,44,45,47,50,51,52,57]}	1 945	8 819	随机效应模型	30.00	0.144	1.777（1.515，2.085）	0.488	0.575
乳腺手术类型	乳腺切除术与肿物切除术	16^{[14,16,20,21,30,31,35,39,43,47,48,51,52,53,55,61]}	1 786	9 955	随机效应模型	26.50	0.157	1.320（1.125，1.549）	0.809	0.278
腋窝手术类型	ALND与SLNB	10^{[14,16,21,23,24,31,39,41,44,57]}	1 132	7 327	随机效应模型	16.30	0.289	3.058（2.325，4.020）	0.465	1.118
放疗	是与否	12^{[16,25,31,33,39,42,48,51,53,55,57,61]}	1 652	6 849	随机效应模型	61.50	0.003	1.620（1.214，2.160）	0.853	0.482
术后并发症	是与否	6^{[5,21,33,34,48,52]}	1 089	6 331	随机效应模型	74.00	0.002	2.373（1.278，4.405）	—	0.864

风险因素	Logistic评分	Additive评分
BMI（kg/m²）
≥30	6	2
<30	0	1
乳腺手术类型
全乳切除	3	2
肿物切除	0	1
腋窝手术类型
ALND	11	3
SLNB	0	1
放疗
是	5	2
否	0	1
术后并发症
是	9	2
否	0	1

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