预后营养指数及其他临床指标与脑胶质瘤术后患者预后的相关性分析

刘娟; 刘洋; 刘小华; 杨宇; 连治琼

doi:10.7619/jcmp.20230487

预后营养指数及其他临床指标与脑胶质瘤术后患者预后的相关性分析

刘娟¹,
刘洋¹,
刘小华²,
杨宇³,
连治琼^4, ,

1.
四川省宜宾市第一人民医院神经外二科, 四川宜宾, 644000
2.
四川省宜宾市第一人民医院神经内科, 四川宜宾, 644000
3.
四川省宜宾市第一人民医院肿瘤科, 四川宜宾, 644000
4.
四川省宜宾市第一人民医院, 神经外一科, 四川宜宾, 644000

基金项目:

四川省卫健委普及科研项目 18PJ541

详细信息

通讯作者:
连治琼: 连志琼, E-mail: 5161711@qq.com

中图分类号: R739.41;R459.3
计量
- 文章访问数: 154
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- PDF下载量: 17
出版历程
- 收稿日期: 2023-02-20
- 修回日期: 2023-04-10
- 网络出版日期: 2023-05-10

Correlation of prognostic nutritional index and related indicators with prognosis of postoperative patients with glioma

1.
the Second Department of Neurosurgery, the First Peoples' Hospital of Yibin of Sichuan Province, Yibin, Sichuan, 644000
2.
Department of Neurology, the First Peoples' Hospital of Yibin of Sichuan Province, Yibin, Sichuan, 644000
3.
Department of Oncology, the First Peoples' Hospital of Yibin of Sichuan Province, Yibin, Sichuan, 644000
4.
the First Department of Neurosurgery, the First Peoples' Hospital of Yibin of Sichuan Province, Yibin, Sichuan, 644000

摘要

摘要:
目的
探讨预后营养指数(PNI)及其他临床指标与脑胶质瘤术后患者预后的相关性。

方法
回顾性选取行手术治疗的151例脑胶质瘤患者作为研究对象, 收集患者临床资料并进行随访。绘制受试者工作特征(ROC)曲线分析不同指标对患者预后的预测效能，采用Kaplan-Meier法绘制生存曲线，通过Log-rank检验和Cox回归分析探讨患者预后的影响因素。

结果
151例患者中位随访时间为13个月，平均生存周期21.89个月，随访期内死亡101例(占66.89%)。白蛋白(ALB)、前白蛋白(PA)、淋巴细胞计数(LYM)、PNI、体质量指数(BMI)和C反应蛋白(CRP)预测患者预后的最佳截断值分别为36.45 g/L、206.0 mg/L、1.85×10⁹/L、46.9、21.45 kg/m²和8.55 mg/L, 曲线下面积分别为0.885、0.683、0.867、0.955、0.635和0.646。单因素分析显示, 世界卫生组织(WHO)中枢神经系统肿瘤分级(简称WHO分级)、术式、术前卡氏功能状态(KPS)评分、ALB、PA、LYM、BMI、CRP和PNI均为脑胶质瘤患者预后的影响因素(P < 0.05)。多因素Cox回归分析显示, PNI≥46.9、术前KPS评分≥60分和WHO分级Ⅱ级、Ⅲ级(相对于Ⅳ级)均为脑胶质瘤患者预后的保护因素(HR=0.925、0.964、0.122、0.577, P < 0.05)。WHO分级Ⅱ级、Ⅲ级、Ⅳ级患者的1年生存率分别为85.37%、50.75%、16.28%, PNI < 46.9和PNI≥46.9患者的1年生存率分别为34.38%和87.27%, 术前KPS评分 < 60分和KPS评分≥60分患者的1年生存率分别为30.23%和62.96%。

结论
脑胶质瘤患者的临床预后较差, PNI、术前KPS评分和WHO分级均与患者预后相关。
- 脑胶质瘤 /
- 营养指数 /
- 预后 /
- 中枢神经系统肿瘤分级 /
- 卡氏功能状态评分
Abstract:
Objective
To discuss the correlation of prognostic nutritional index (PNI) and related indicators with prognosis of postoperative patients with glioma.

Methods
A total of 151 patients with glioma were retrospectively selected as study objects, and relevant clinical data were collected and the follow-up was conducted. Receiver operating characteristic (ROC) curve was drawn to analyze the efficacy of different indicators in predicting prognosis. Kaplan-Meier method was used to draw the survival curve, and Log-rank test and Cox regression were used to analyze the influencing factors of prognosis.

Results
Of the 151 patients, the median follow-up duration was 13 months, and the average survival period was 21.9 months. The death rate during the follow-up period was 66.89%(101 cases). The best cutoff values of albumin (ALB), prealbumin (PA), lymphocyte (LYM), PNI, body mass index (BMI) and C-reactive protein (CRP) in predicting the prognosis of patients were 36.45 g/L, 206.0 mg/L and 1.85× 10⁹/L, 46.9, 21.45 kg/m² and 8.55 mg/L, respectively, with areas under the curve of 0.885, 0.683, 0.867, 0.955, 0.635 and 0.646, respectively. Univariate analysis showed that World Health Organization (WHO) tumor grading for central nervous system (short for WHO grading), operation mode, preoperative Karnofsky Performance Status (KPS) score, ALB, PA, LYM, BMI, CRP and PNI were the influencing factors of prognosis in glioma patients (P < 0.05). COX multivariate analysis showed that PNI ≥ 46.9, preoperative KPS score ≥60 and WHO grade Ⅱ and Ⅲ (compared with grade Ⅳ) are the protective factors for the prognosis of patients with glioma(HR=0.925, 0.964, 0.122 and 0.577 respectively, P < 0.05). The one-year survival rates of patients with grade Ⅱ, Ⅲ and Ⅳ of WHO were 85.37%, 50.75 and 16.28% respectively, and the one-year survival rates of patients with PNI < 46.9 and ≥46.9 were 34.38% and 87.27% respectively. The one-year survival rates of patients with preoperative KPS score < 60 and ≥60 were 30.23% and 62.96%, respectively.

Conclusion
The clinical prognosis of glioma is poor. PNI, preoperative KPS score and WHO grade are related to the clinical outcome of patients with glioma.
- glioma /
- nutritional index /
- prognosis /
- tumor grade of central nervous system /
- Karnofsky Performance Status

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图 1 151例脑胶质瘤患者的总生存曲线

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图 2 不同指标预测患者预后的ROC曲线

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图 3 不同PNI、KPS评分和WHO分级患者的生存曲线

A: 不同PNI患者的生存曲线; B: 不同KPS评分患者的生存曲线; C: 不同WHO分级患者的生存曲线。

下载: 全尺寸图片幻灯片

表 1 不同指标对患者预后的预测效能

指标	AUC	95%CI	最佳截断值	约登指数	P
ALB/(g/L)	0.885	0.824~0.946	36.45	0.612	<0.001
PA/(mg/L)	0.683	0.592~0.774	206.00	0.303	<0.001
LYM/(×10⁹/L)	0.867	0.809~0.926	1.85	0.544	<0.001
PNI	0.955	0.923~0.987	46.90	0.771	<0.001
BMI/(kg/m²)	0.635	0.542~0.729	21.45	0.226	0.007
CRP/(mg/L)	0.646	0.554~0.738	8.55	0.246	0.004
ALB: 白蛋白; PA: 前白蛋白; LYM: 淋巴细胞计数; PNI: 预后营养指数; BMI: 体质量指数; CRP: C反应蛋白。

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表 2 患者临床预后影响因素的单因素分析

指标	分类	n	死亡/例	生存时间(95%CI)/月	Log-rank χ²	P
性别	男	81	49	17.35(15.27~19.44)	2.282	0.131
	女	70	52	14.21(12.56~15.86)
WHO分级	Ⅱ级	41	7	26.16(24.24~28.09)	91.277	<0.001
	Ⅲ级	67	51	14.16(12.74~15.58)
	Ⅳ级	43	43	9.05(7.83~10.26)
术式	全切除术	58	19	21.89(19.61~24.17)	38.730	<0.001
	部分切除术	93	82	12.69(11.32~14.07)
术前KPS评分	<60分	43	41	10.29(8.88~11.69)	32.926	<0.001
	≥60分	108	60	18.71(16.95~20.48)
ALB	<36.45 g/L	89	80	12.28(10.94~13.62)	43.469	<0.001
	≥36.45 g/L	62	21	22.53(20.25~24.80)
PA	<206.0 mg/L	88	69	13.83(12.16~15.50)	12.808	<0.001
	≥206.0 mg/L	63	32	19.60(17.30~21.91)
LYM	<1.85×10⁹/L	70	65	12.08(10.80~13.36)	30.812	<0.001
	≥1.85×10⁹/L	81	36	20.20(18.03~22.38)
BMI	<21.45 kg/m²	50	41	13.47(11.93~15.00)	4.554	0.033
	≥21.45 kg/m²	101	60	17.39(15.47~19.32)
CRP	<8.55 mg/L	99	58	17.85(15.94~19.76)	8.956	0.003
	≥8.55 mg/L	52	43	13.11(11.27~14.95)
PNI	<46.9	96	90	11.64(10.53~12.76)	71.030	<0.001
	≥46.9	55	11	25.14(23.08~27.21)
WHO: 世界卫生组织; KPS: 卡氏功能状态; ALB: 白蛋白; PA: 前白蛋白; LYM: 淋巴细胞计数; PNI: 预后营养指数; BMI: 体质量指数; CRP: C反应蛋白。

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表 3 患者预后影响因素的多因素Cox回归分析

因素		β	S. E.	HR(95%CI)	Wald χ²	P
PNI		-0.078	0.024	0.925(0.882~0.970)	10.464	0.001
术前KPS评分		-0.037	0.010	0.964(0.945~0.983)	13.323	<0.001
WHO分级	Ⅱ级	-2.104	0.455	0.122(0.050~0.297)	21.402	<0.001
	Ⅲ级	-0.550	0.228	0.577(0.369~0.901)	5.840	0.016

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参考文献(22)

[1]	FANGUSARO J, ONAR-THOMAS A, YOUNG POUSSAINT T, et al. Selumetinib in paediatric patients with BRAF-aberrant or neurofibromatosis type 1-associated recurrent, refractory, or progressive low-grade glioma: a multicentre, phase 2 trial[J]. Lancet Oncol, 2019, 20(7): 1011-1022. doi: 10.1016/S1470-2045(19)30277-3
[2]	WANG Z H, GAO L, GUO X P, et al. Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma[J]. Aging, 2019, 11(24): 12246-12269. doi: 10.18632/aging.102566
[3]	HU C, CHEN K N, TANG X P. Prognostic value of preoperative controlling nutritional status in patients with glioblastoma[J]. Clin Neurol Neurosurg, 2020, 198: 106129. doi: 10.1016/j.clineuro.2020.106129
[4]	XIAO Y P, WEI G, MA M, et al. Association among prognostic nutritional index, post-operative infection and prognosis of stage Ⅱ/Ⅲ gastric cancer patients following radical gastrectomy[J]. Eur J Clin Nutr, 2022, 76(10): 1449-1456. doi: 10.1038/s41430-022-01120-7
[5]	《中国中枢神经系统胶质瘤诊断和治疗指南》编写组. 中国中枢神经系统胶质瘤诊断和治疗指南(2015)[J]. 中华医学杂志, 2016, 96(7): 485-509. doi: 10.3760/cma.j.issn.0376-2491.2016.07.003
[6]	XU S C, TANG L, LI X Z, et al. Immunotherapy for glioma: current management and future application[J]. Cancer Lett, 2020, 476: 1-12. doi: 10.1016/j.canlet.2020.02.002
[7]	MLADENOVSK M, VALKOV I, OVCHAROV M, et al. High grade glioma surgery-clinical aspects and prognosis[J]. Folia Med (Plovdiv), 2021, 63(1): 35-41. doi: 10.3897/folmed.63.e55255
[8]	ALAN O, TELLI T A, BASOGLU T, et al. Impact of prognostic nutritional index on survival in recurrent glioblastoma[J]. Neurocirugia, 2022, 33(1): 15-21. doi: 10.1016/j.neucir.2020.11.005
[9]	李涛, 翟景光, 刘晓. 脑胶质瘤患者手术治疗预后不良的相关因素分析[J]. 齐齐哈尔医学院学报, 2022, 43(11): 1047-1051. doi: 10.3969/j.issn.1002-1256.2022.11.011
[10]	RATHORE S, AKBARI H, DOSHI J, et al. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning[J]. J Med Imaging, 2018, 5(2): 021219.
[11]	LIANG R, ZHI Y, ZHENG G, et al. Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis, Cox regression, and L1-LASSO penalization[J]. Onco Targets Ther, 2019, 12: 157-168.
[12]	DEMIRELLI B, BABACAN N A, ERCELEP Ö, et al. Modified Glasgow prognostic score, prognostic nutritional index and ECOG performance score predicts survival better than sarcopenia, Cachexia and some inflammatory indices in metastatic gastric cancer[J]. Nutr Cancer, 2021, 73(2): 230-238. doi: 10.1080/01635581.2020.1749290
[13]	WANG D, HU X, XIAO L, et al. Prognostic nutritional index and systemic immune-inflammation index predict the prognosis of patients with HCC[J]. J Gastrointest Surg, 2021, 25(2): 421-427. doi: 10.1007/s11605-019-04492-7
[14]	徐日. 预后营养指数对胶质瘤患者预后的影响[J]. 分子诊断与治疗杂志, 2020, 12(2): 203-206.
[15]	晁储瑞, 赵伟峰, 刘慧敏, 等. 脑胶质瘤患者术前血液学指标与预后的关系[J]. 中华实用诊断与治疗杂志, 2019, 33(11): 1105-1109.
[16]	SENGER A S, DINCER M, UZUN O, et al. Impact of preoperative prognostic nutritional index levels on morbidity in colorectal cancer surgery[J]. Ann Ital Chir, 2022, 92: 422-426.
[17]	DING P A, GUO H H, SUN C Y, et al. Combined systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) predicts chemotherapy response and prognosis in locally advanced gastric cancer patients receiving neoadjuvant chemotherapy with PD-1 antibody sintilimab and XELOX: a prospective study[J]. BMC Gastroenterol, 2022, 22(1): 121. doi: 10.1186/s12876-022-02199-9
[18]	HE J, YIN H, XIA Y, et al. Prognostic nutritional index, a novel biomarker which predicts worse prognosis in diffuse large B cell lymphoma[J]. Leuk Res, 2021, 110: 106664. doi: 10.1016/j.leukres.2021.106664
[19]	DEME D, TELEKES A. Prognostic importance of albumin in oncology[J]. Orv Hetil, 2018, 159(3): 96-106. doi: 10.1556/650.2018.30885
[20]	KIM Y J, OH H, LEE S J, et al. Prognostic significance of the postoperative prognostic nutritional index in patients with glioblastoma: a retrospective study[J]. BMC Cancer, 2021, 21(1): 942. doi: 10.1186/s12885-021-08686-8
[21]	刘竞辉, 娄淼, 冀培刚, 等. 老年胶质瘤预后相关因素分析[J]. 中南大学学报: 医学版, 2018, 43(4): 403-409. https://www.cnki.com.cn/Article/CJFDTOTAL-HNYD201804013.htm
[22]	BARBAGALLO G M V, CERTO F, DI GREGORIO S, et al. Recurrent high-grade glioma surgery: a multimodal intraoperative protocol to safely increase extent of tumor resection and analysis of its impact on patient outcome[J]. Neurosurg Focus, 2021, 50(1): E20. doi: 10.3171/2020.10.FOCUS20744

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9.	唐永红，刘涛，彭健. 人乳头瘤病毒在宫颈病变中的感染分型及其特点. 系统医学. 2020(17): 134-136 . 百度学术