结直肠癌无进展生存时间预测模型及影响因素研究

doi:10.3969/j.issn.1674-8115.2025.03.009

上海交通大学学报（医学版） ›› 2025, Vol. 45 ›› Issue (3): 324-334.doi: 10.3969/j.issn.1674-8115.2025.03.009

• 论著 · 临床研究 • 上一篇

结直肠癌无进展生存时间预测模型及影响因素研究

陈佳莹¹^,²(), 褚以忞¹^,², 彭海霞¹^,²()

^1.上海交通大学医学院附属同仁医院内窥镜室，上海 200336
^2.上海市柔性医疗机器人重点实验室，上海 200336

收稿日期:2024-05-29 接受日期:2024-09-06 出版日期:2025-03-28 发布日期:2025-03-28
通讯作者: 彭海霞 E-mail:cjy811976991@163.com;phx1101@shtrhospital.com
作者简介:陈佳莹（1996—），女，硕士生；电子信箱：cjy811976991@163.com。
基金资助:
上海市自然科学基金(21ZR1058600);上海交通大学“交大之星”计划医工交叉研究基金(YG2022ZD031);上海市卫生健康委员会临床专项青年课题(20234Y0016)

Study on prediction model and influencing factors of progression-free survival in colorectal cancer

CHEN Jiaying¹^,²(), CHU Yimin¹^,², PENG Haixia¹^,²()

^1.Digestive Endoscopy Center, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
^2.Shanghai Key Laboratory of Flexible Medical Robotics, Institute of Medical Robotics, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China

Received:2024-05-29 Accepted:2024-09-06 Online:2025-03-28 Published:2025-03-28
Contact: PENG Haixia E-mail:cjy811976991@163.com;phx1101@shtrhospital.com
Supported by:
Natural Science Foundation of Shanghai(21ZR1058600);Medical-Engineering Cross Research of Shanghai Jiao Tong University(YG2022ZD031);Shanghai Municipal Health Commission Health industry Clinical Research Project(20234Y0016)

摘要/Abstract

摘要：

目的·探究影响结直肠癌（colorectal cancer，CRC）患者无进展生存时间（progression-free survival，PFS）的风险因素，建立相应的预后预测模型。方法·采用回顾性队列研究，纳入上海交通大学医学院附属同仁医院的533例经手术切除并病理确诊为结直肠腺癌的患者，按照7∶3的比例随机拆分为训练集（373例）和验证集（160例）。采用单因素和多因素Cox比例风险回归模型分析纳入的临床资料，分析CRC患者术后PFS的独立影响因素，并建立关于结直肠癌的临床预后预测模型。使用一致性指数（concordance index，C-index）、受试者操作特征曲线（receiver operator characteristic curve，ROC曲线）下面积（area under the curve，AUC）、校准曲线、生存曲线评估预测模型的区分度和校准度。进一步通过多因素Cox回归分析确定不同年龄、性别以及美国癌症联合委员会（American Joint Committee on Cancer，AJCC）癌症分期人群预后的独立影响因素。结果·多因素Cox回归分析发现，年龄、吸烟史、肝脏疾病、糖类抗原724（carbohydrate antigen 724，CA724）、AJCC分期是PFS的独立影响因素。在训练集中，该PFS模型的C-index为0.69，3年和5年的AUC分别为0.744和0.713。在验证集中，该PFS模型的C-index为0.64，3年和5年的AUC分别为0.706和0.683。校准曲线图可见训练集和验证集的PFS预测校准曲线均趋近标准曲线。生存曲线提示低风险组患者的进展率显著低于高风险组。分层分析结果表明：对年龄≥65岁患者，年龄、肝脏疾病、AJCC临床分期是术后PFS的独立影响因素；对年龄<65岁的患者，吸烟史、CA724、AJCC临床分期是术后PFS的独立影响因素。对男性患者，年龄、吸烟史、CA724、AJCC临床分期是术后PFS的独立影响因素；对女性患者，肝脏疾病、CA724、AJCC临床分期是术后PFS的独立影响因素。对AJCC分期Ⅰ~Ⅱ期的患者，年龄和吸烟史是术后PFS的独立影响因素；对AJCC分期Ⅲ~Ⅳ期的患者，年龄、肝脏疾病和CA724是术后PFS的独立影响因素。结论·针对CRC患者建立的PFS预后模型具有较好区分能力，为临床医师提供了有效的风险评估工具。

关键词: 结直肠癌, 无进展生存时间, 影响因素, 预测模型

Abstract:

Objective ·To explore the risk factors affecting progression-free survival (PFS) in colorectal cancer (CRC) patients and establish a corresponding prognostic prediction model. Methods ·A retrospective cohort study was used for analysis, including 533 patients with surgically resected and pathologically confirmed colorectal adenocarcinoma at Tongren Hospital, Shanghai Jiao Tong University School of Medicine, who were randomly divided into a training set (373 cases) and a validation set (160 cases) in a 7:3 ratio. The included clinical data were analyzed using univariate and multivariate Cox proportional hazards models to explore independent factors affecting postoperative PFS in patients with CRC and to establish a clinical prognostic prediction model based on these factors. The discrimination and calibration of the prediction model were evaluated by using concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and survival curve. The independent effects of age, gender, and American Joint Committee on Cancer (AJCC) cancer stage on prognosis were assessed using multivariate Cox regression analysis. Results ·Multivariate Cox regression analysis revealed that age, smoking history, liver disease, carbohydrate antigen 724 (CA724), and AJCC stage were independent factors influencing PFS. In the training set, the C-index of the PFS model was 0.69, with AUCs of 0.744 and 0.713 at 3 and 5 years, respectively. In the validation set, the C-index of the PFS model was 0.64, with AUCs of 0.706 and 0.683 at 3 and 5 years, respectively. The calibration curves showed that the prediction of PFS for the training and validation sets were close to the standard curve. The survival curves suggested that the progression rate of patients in the low-risk group was significantly lower than that in the high-risk group. Stratified analysis revealed that among patients aged ≥65 years, age, liver disease, and AJCC clinical stage were independent factors affecting postoperative PFS. Among patients aged <65 years, smoking history, CA724, and AJCC clinical stage were independent factors affecting postoperative PFS. For male patients, age, smoking history, CA724, and AJCC clinical stage were independent factors affecting postoperative PFS, while for female patients, liver disease, CA724, and AJCC clinical staging were independent predictors of postoperative PFS. Among patients with AJCC stage Ⅰ‒Ⅱ, age and smoking history were independent factors affecting postoperative PFS, whereas in those with AJCC stage Ⅲ‒⁠Ⅳ, age, liver disease, and CA724 were independent factors affecting postoperative PFS. Conclusion ·The PFS prognostic model established in this study for CRC patients has good discriminative ability and provides clinicians with an effective risk assessment tool.

Key words: colorectal cancer, progression-free survival, influencing factor, predictive modeling

中图分类号:

R574.6

陈佳莹, 褚以忞, 彭海霞. 结直肠癌无进展生存时间预测模型及影响因素研究[J]. 上海交通大学学报（医学版）, 2025, 45(3): 324-334.

CHEN Jiaying, CHU Yimin, PENG Haixia. Study on prediction model and influencing factors of progression-free survival in colorectal cancer[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(3): 324-334.

图/表 9

参考文献 25

1	SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249.
2	BRENNER B, GEVA R, ROTHNEY M, et al. Impact of the 12-gene colon cancer assay on clinical decision making for adjuvant therapy in stage Ⅱ colon cancer patients[J]. Value Health, 2016, 19(1): 82-87.
3	OSTERMAN E, GLIMELIUS B. Recurrence risk after up-to-date colon cancer staging, surgery, and pathology: analysis of the entire Swedish population[J]. Dis Colon Rectum, 2018, 61(9): 1016-1025.
4	VAN ZUTPHEN M, BOSHUIZEN H C, KENKHUIS M F, et al. Lifestyle after colorectal cancer diagnosis in relation to recurrence and all-cause mortality[J]. Am J Clin Nutr, 2021, 113(6): 1447-1457.
5	史金鑫, 崔健, 李子建, 等. 不同年龄段老年结直肠癌患者术后临床特征分析[J]. 中华医学杂志, 2022, 102(8): 563-568.
	SHI J X, CUI J, LI Z J, et al. Contrastive analysis about the postoperative clinical characteristics of elderly patients with colorectal cancer in different age groups[J]. National Medical Journal of China, 2022, 102(8): 563-568.
6	中华人民共和国国家卫生健康委员会. 中国结直肠癌诊疗规范(2023版)[J]. 中华消化外科杂志, 2023, 22(6): 667-698.
	National Health Commission of the People′s Republic of China. Chinese protocol of diagnosis and treatment of colorectal cancer (2023 edition)[J]. Chinese Society of Oncology, 2023, 22(6): 667-698.
7	STIKSMA J, GROOTENDORST D C, VAN DER LINDEN P W G. CA 19-9 as a marker in addition to CEA to monitor colorectal cancer[J]. Clin Colorectal Cancer, 2014, 13(4): 239-244.
8	AMIN M B, GREENE F L, EDGE S B, et al. The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging[J]. CA Cancer J Clin, 2017, 67(2): 93-99.
9	KONISHI T, SHIMADA Y, HSU M, et al. Association of preoperative and postoperative serum carcinoembryonic antigen and colon cancer outcome[J]. JAMA Oncol, 2018, 4(3): 309-315.
10	KUANG J A, GONG Y Z, XIE H L, et al. The prognostic value of preoperative serum CA724 for CEA-normal colorectal cancer patients[J]. PeerJ, 2020, 8: e8936.
11	闫超, 陕飞, 李子禹. 2020年中国与全球结直肠癌流行概况分析[J]. 中华肿瘤杂志, 2023, 45(3): 221-229.
	YAN C, SHAN F, LI Z Y. Prevalence of colorectal cancer in 2020: a comparative analysis between China and the world[J]. Chinese Journal of Oncology, 2023, 45(3): 221-229.
12	LIEU C H, RENFRO L A, DE GRAMONT A, et al. Association of age with survival in patients with metastatic colorectal cancer: analysis from the ARCAD Clinical Trials Program[J]. J Clin Oncol, 2014, 32(27): 2975-2984.
13	ZHOU X, XIAO Q, JIANG F Y, et al. Dissecting the pathogenic effects of smoking and its hallmarks in blood DNA methylation on colorectal cancer risk[J]. Br J Cancer, 2023, 129(8): 1306-1313.
14	BOTTERI E, BORRONI E, SLOAN E K, et al. Smoking and colorectal cancer risk, overall and by molecular subtypes: a meta-analysis[J]. Am J Gastroenterol, 2020, 115(12): 1940-1949.
15	AMITAY E L, CARR P R, JANSEN L, et al. Smoking, alcohol consumption and colorectal cancer risk by molecular pathological subtypes and pathways[J]. Br J Cancer, 2020, 122(11): 1604-1610.
16	PHIPPS A I, SHI Q, NEWCOMB P A, et al. Associations between cigarette smoking status and colon cancer prognosis among participants in North Central Cancer Treatment Group Phase Ⅲ Trial N0147[J]. J Clin Oncol, 2013, 31(16): 2016-2023.
17	LV Y, ZHANG H J. Effect of non-alcoholic fatty liver disease on the risk of synchronous liver metastasis: analysis of 451 consecutive patients of newly diagnosed colorectal cancer[J]. Front Oncol, 2020, 10: 251.
18	WU K N, ZHAI M Z, WELTZIEN E K, et al. Non-alcoholic fatty liver disease and colorectal cancer survival[J]. Cancer Causes Control, 2019, 30(2): 165-168.
19	UTSUNOMIYA T, SAITSU H, SAKU M, et al. Rare occurrence of colorectal cancer metastasis in livers infected with hepatitis B or C virus[J]. Am J Surg, 1999, 177(4): 279-281.
20	QIU H B, ZHANG L Y, ZENG Z L, et al. HBV infection decreases risk of liver metastasis in patients with colorectal cancer: a cohort study[J]. World J Gastroenterol, 2011, 17(6): 804-808.
21	HUO T T, CAO J Y, TIAN Y W, et al. Effect of concomitant positive hepatitis B surface antigen on the risk of liver metastasis: a retrospective clinical study of 4033 consecutive cases of newly diagnosed colorectal cancer[J]. Clin Infect Dis, 2018, 66(12): 1948-1952.
22	JING X L, ZHANG S C. An ancient molecule with novel function: alanine aminotransferase as a lipopolysaccharide binding protein with bacteriocidal activity[J]. Dev Comp Immunol, 2011, 35(1): 94-104.
23	HE M M, FANG Z, HANG D, et al. Circulating liver function markers and colorectal cancer risk: a prospective cohort study in the UK Biobank[J]. Int J Cancer, 2021, 148(8): 1867-1878.
24	FU J M, DU F Q, TIAN T, et al. Development and validation of prognostic nomograms based on De Ritis ratio and clinicopathological features for patients with stage Ⅱ/Ⅲ colorectal cancer[J]. BMC Cancer, 2023, 23(1): 620.
25	LEE J H, JUNG S, PARK W S, et al. Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer-analysis of TCGA database[J]. Sci Rep, 2019, 9(1): 1803.

Item	Total population (n=533)	Training set (n=373)	Validation set (n=160)	P value
Outcome/n(%)				0.573
Survival	412 (77.3)	291 (78.0)	121 (75.6)
Death	121 (22.7)	82 (22.0)	39 (24.4)
Progression/n(%)				0.491
Yes	191 (35.8)	130 (34.9)	61 (38.1)
No	342 (64.2)	243 (65.1)	99 (61.9)
Gender/n(%)				0.528
Male	324 (60.8)	230 (61.7)	94 (58.8)
Female	209 (39.2)	143 (38.3)	66 (41.3)
Age/year	68.00 (62.00, 77.00)	68.00 (62.00, 77.00)	67.00 (62.00, 76.00)	0.707
Family history of CRC/n(%)				>0.999
No	528 (99.1)	369 (98.9)	159 (99.4)
Yes	5 (0.9)	4 (1.1)	1 (0.6)
Smoking history/n(%)				0.182
No	502 (94.2)	348 (93.3)	154 (96.3)
Yes	31 (5.8)	25 (6.7)	6 (3.8)
Alcohol consumption history/n(%)				0.639
No	528 (99.1)	370 (99.2)	158 (98.8)
Yes	5 (0.9)	3 (0.8)	2 (1.3)
Hypertension/n(%)				0.275
No	294 (55.2)	200 (53.6)	94 (58.8)
Yes	239 (44.8)	173 (46.4)	66 (41.3)
Diabetes/n(%)				0.304
No	437 (82.0)	310 (83.1)	127 (79.4)
Yes	96 (18.0)	63 (16.9)	33 (20.6)
Cardiovascular disease/n(%)				0.024
No	384 (72.0)	258 (69.2)	126 (78.8)
Yes	149 (28.0)	115 (30.8)	34 (21.3)
Kidney disease/n(%)				0.018
No	509 (95.5)	351 (94.1)	158 (98.8)
Yes	24 (4.5)	22 (5.9)	2 (1.3)
Liver disease/n(%)				0.507
No	511 (95.9)	359 (96.2)	152 (95.0)
Yes	22 (4.1)	14 (3.8)	8 (5.0)
Inflammatory bowel disease/n(%)				>0.999
No	532 (99.8)	372 (99.7)	160 (100.0)
Yes	1 (0.2)	1 (0.3)	0 (0)
Hyperlipidemia/n(%)				0.639
No	478 (89.7)	333 (89.3)	145 (90.6)
Yes	55 (10.3)	40 (10.7)	15 (9.4)
Neutrophil percentage/%	67.61±9.91	66.82±10.07	69.45±9.30	0.004
Lymphocyte percentage/%	23.01±8.53	23.61±8.44	21.61±8.59	0.014
Absolute neutrophil count/n	4.15 (3.11, 5.37)	4.01 (3.01, 5.24)	4.33 (3.59, 5.60)	0.003
Absolute lymphocyte count/n	1.40 (1.10, 1.80)	1.40 (1.10, 1.80)	1.40 (1.04, 1.70)	0.263
Monocyte percentage/%	6.60 (5.20, 8.20)	1.40 (1.10, 1.80)	6.50 (5.20, 7.98)	0.298
NLR	3.00 (2.14, 4.42)	2.90 (2.07, 4.05)	3.34 (2.41, 4.95)	0.002
PLR	600.00 (438.5, 823.49)	584.76 (437.96, 834.08)	626.19 (438.76, 786.53)	0.977
LMR	3.41 (2.44, 4.58)	3.45 (2.49, 4.57)	3.33 (2.21, 4.66)	0.338
Hemoglobin/(g·L^-1)	119.00 (96.00, 134.00)	120.00 (98.00, 134.00)	118.00 (93.00, 132.00)	0.188
Platelet/(×10⁹·L^-1)	245.00 (198.00, 308.00)	245.00 (196.00, 304.00)	245.50 (200.50, 317.50)	0.609
Fasting blood glucose/(mmol·L^-1)	5.80 (5.10, 7.30)	5.70 (5.00, 7.20)	6.00 (5.40, 7.60)	0.005
Creatinine/(μmol·L^-1)	69.10 (59.70, 80.00)	69.30 (60.00, 79.60)	68.90 (58.35, 81.12)	0.571
GFR/[mL·(min·1.73 m²)^-1]	116.69 (99.34, 137.51)	118.80 (99.55, 136.77)	118.31 (99.23, 139.27)	0.646
Urea/(mmol·L^-1)	5.00 (4.07, 6.15)	5.00 (4.07, 6.10)	5.04 (4.08, 6.15)	0.910
Total protein/(g·L^-1)	70.00 (64.70, 74.90)	69.90 (64.60, 74.50)	71.00 (64.85, 75.32)	0.226
Albumin/(g·L^-1)	40.30 (36.90, 43.30)	40.40 (37.00, 43.00)	40.15 (36.22, 43.62)	0.665
Total bilirubin/(μmol·L^-1)	10.10 (7.40, 14.10)	10.10 (7.30, 14.10)	10.05 (7.40, 13.72)	0.678
GPT/(U·L^-1)	20.00 (13.00, 29.00)	20.00 (13.00, 29.00)	21.00 (13.00, 30.00)	0.539
GGT/(U·L^-1)	19.00 (14.00, 28.00)	19.00 (14.00, 29.00)	18.00 (13.00, 26.00)	0.135
GOT/(U·L^-1)	21.00 (17.00, 25.00)	21.00 (17.00, 25.00)	20.00 (16.75, 26.00)	0.987
AFP/(ng·L^-1)	2.37 (1.56, 3.53)	2.36 (1.59, 3.50)	2.40 (1.52, 3.57)	0.796
CEA/(ng·L^-1)	3.43 (1.67, 8.98)	3.43 (1.63, 9.05)	3.38 (1.76, 8.39)	0.812
CA199/(U·mL^-1)	14.08 (8.47, 28.17)	13.39 (8.19, 27.76)	16.41 (9.31, 28.47)	0.320
CA125/(U·mL^-1)	11.65 (8.37, 18.47)	11.29 (7.90, 17.24)	13.36 (9.24, 24.01)	0.004
CA153/(U·mL^-1)	7.20 (4.82, 10.61)	7.09 (4.80, 10.86)	7.70 (4.91, 10.59)	0.998
CA724/(U·mL^-1)	2.42 (1.50, 5.74)	2.21 (1.50, 5.20)	3.14 (1.50, 6.69)	0.029
D-D/(mg·L^-1)	0.47 (0.25, 0.98)	0.45 (0.25, 0.95)	0.57 (0.27, 1.14)	0.100
INR	0.99 (0.94, 1.04)	0.99 (0.94, 1.04)	0.98 (0.92, 1.05)	0.298
C-reactive protein/(mg·L^-1)	5.90 (1.80, 19.28)	5.90 (1.80, 19.28)	5.95 (1.64, 19.66)	0.879
AJCC clinical stage/n(%)				0.577
StageⅠ‒Ⅱ	263 (49.3)	187 (50.1)	76 (47.5)
Stage Ⅲ‒Ⅳ	270 (50.7)	186 (49.9)	84 (52.5)
LCR	0.24 (0.07, 0.90)	0.24 (0.07, 0.91)	0.24 (0.06, 0.90)	0.775
CAR	154.88 (42.62, 493.89)	154.93 (42.36, 502.48)	152.82 (43.00, 492.59)	0.830

Item	Total population (n=533)	Training set (n=373)	Validation set (n=160)	P value
Outcome/n(%)				0.573
Survival	412 (77.3)	291 (78.0)	121 (75.6)
Death	121 (22.7)	82 (22.0)	39 (24.4)
Progression/n(%)				0.491
Yes	191 (35.8)	130 (34.9)	61 (38.1)
No	342 (64.2)	243 (65.1)	99 (61.9)
Gender/n(%)				0.528
Male	324 (60.8)	230 (61.7)	94 (58.8)
Female	209 (39.2)	143 (38.3)	66 (41.3)
Age/year	68.00 (62.00, 77.00)	68.00 (62.00, 77.00)	67.00 (62.00, 76.00)	0.707
Family history of CRC/n(%)				>0.999
No	528 (99.1)	369 (98.9)	159 (99.4)
Yes	5 (0.9)	4 (1.1)	1 (0.6)
Smoking history/n(%)				0.182
No	502 (94.2)	348 (93.3)	154 (96.3)
Yes	31 (5.8)	25 (6.7)	6 (3.8)
Alcohol consumption history/n(%)				0.639
No	528 (99.1)	370 (99.2)	158 (98.8)
Yes	5 (0.9)	3 (0.8)	2 (1.3)
Hypertension/n(%)				0.275
No	294 (55.2)	200 (53.6)	94 (58.8)
Yes	239 (44.8)	173 (46.4)	66 (41.3)
Diabetes/n(%)				0.304
No	437 (82.0)	310 (83.1)	127 (79.4)
Yes	96 (18.0)	63 (16.9)	33 (20.6)
Cardiovascular disease/n(%)				0.024
No	384 (72.0)	258 (69.2)	126 (78.8)
Yes	149 (28.0)	115 (30.8)	34 (21.3)
Kidney disease/n(%)				0.018
No	509 (95.5)	351 (94.1)	158 (98.8)
Yes	24 (4.5)	22 (5.9)	2 (1.3)
Liver disease/n(%)				0.507
No	511 (95.9)	359 (96.2)	152 (95.0)
Yes	22 (4.1)	14 (3.8)	8 (5.0)
Inflammatory bowel disease/n(%)				>0.999
No	532 (99.8)	372 (99.7)	160 (100.0)
Yes	1 (0.2)	1 (0.3)	0 (0)
Hyperlipidemia/n(%)				0.639
No	478 (89.7)	333 (89.3)	145 (90.6)
Yes	55 (10.3)	40 (10.7)	15 (9.4)
Neutrophil percentage/%	67.61±9.91	66.82±10.07	69.45±9.30	0.004
Lymphocyte percentage/%	23.01±8.53	23.61±8.44	21.61±8.59	0.014
Absolute neutrophil count/n	4.15 (3.11, 5.37)	4.01 (3.01, 5.24)	4.33 (3.59, 5.60)	0.003
Absolute lymphocyte count/n	1.40 (1.10, 1.80)	1.40 (1.10, 1.80)	1.40 (1.04, 1.70)	0.263
Monocyte percentage/%	6.60 (5.20, 8.20)	1.40 (1.10, 1.80)	6.50 (5.20, 7.98)	0.298
NLR	3.00 (2.14, 4.42)	2.90 (2.07, 4.05)	3.34 (2.41, 4.95)	0.002
PLR	600.00 (438.5, 823.49)	584.76 (437.96, 834.08)	626.19 (438.76, 786.53)	0.977
LMR	3.41 (2.44, 4.58)	3.45 (2.49, 4.57)	3.33 (2.21, 4.66)	0.338
Hemoglobin/(g·L^-1)	119.00 (96.00, 134.00)	120.00 (98.00, 134.00)	118.00 (93.00, 132.00)	0.188
Platelet/(×10⁹·L^-1)	245.00 (198.00, 308.00)	245.00 (196.00, 304.00)	245.50 (200.50, 317.50)	0.609
Fasting blood glucose/(mmol·L^-1)	5.80 (5.10, 7.30)	5.70 (5.00, 7.20)	6.00 (5.40, 7.60)	0.005
Creatinine/(μmol·L^-1)	69.10 (59.70, 80.00)	69.30 (60.00, 79.60)	68.90 (58.35, 81.12)	0.571
GFR/[mL·(min·1.73 m²)^-1]	116.69 (99.34, 137.51)	118.80 (99.55, 136.77)	118.31 (99.23, 139.27)	0.646
Urea/(mmol·L^-1)	5.00 (4.07, 6.15)	5.00 (4.07, 6.10)	5.04 (4.08, 6.15)	0.910
Total protein/(g·L^-1)	70.00 (64.70, 74.90)	69.90 (64.60, 74.50)	71.00 (64.85, 75.32)	0.226
Albumin/(g·L^-1)	40.30 (36.90, 43.30)	40.40 (37.00, 43.00)	40.15 (36.22, 43.62)	0.665
Total bilirubin/(μmol·L^-1)	10.10 (7.40, 14.10)	10.10 (7.30, 14.10)	10.05 (7.40, 13.72)	0.678
GPT/(U·L^-1)	20.00 (13.00, 29.00)	20.00 (13.00, 29.00)	21.00 (13.00, 30.00)	0.539
GGT/(U·L^-1)	19.00 (14.00, 28.00)	19.00 (14.00, 29.00)	18.00 (13.00, 26.00)	0.135
GOT/(U·L^-1)	21.00 (17.00, 25.00)	21.00 (17.00, 25.00)	20.00 (16.75, 26.00)	0.987
AFP/(ng·L^-1)	2.37 (1.56, 3.53)	2.36 (1.59, 3.50)	2.40 (1.52, 3.57)	0.796
CEA/(ng·L^-1)	3.43 (1.67, 8.98)	3.43 (1.63, 9.05)	3.38 (1.76, 8.39)	0.812
CA199/(U·mL^-1)	14.08 (8.47, 28.17)	13.39 (8.19, 27.76)	16.41 (9.31, 28.47)	0.320
CA125/(U·mL^-1)	11.65 (8.37, 18.47)	11.29 (7.90, 17.24)	13.36 (9.24, 24.01)	0.004
CA153/(U·mL^-1)	7.20 (4.82, 10.61)	7.09 (4.80, 10.86)	7.70 (4.91, 10.59)	0.998
CA724/(U·mL^-1)	2.42 (1.50, 5.74)	2.21 (1.50, 5.20)	3.14 (1.50, 6.69)	0.029
D-D/(mg·L^-1)	0.47 (0.25, 0.98)	0.45 (0.25, 0.95)	0.57 (0.27, 1.14)	0.100
INR	0.99 (0.94, 1.04)	0.99 (0.94, 1.04)	0.98 (0.92, 1.05)	0.298
C-reactive protein/(mg·L^-1)	5.90 (1.80, 19.28)	5.90 (1.80, 19.28)	5.95 (1.64, 19.66)	0.879
AJCC clinical stage/n(%)				0.577
StageⅠ‒Ⅱ	263 (49.3)	187 (50.1)	76 (47.5)
Stage Ⅲ‒Ⅳ	270 (50.7)	186 (49.9)	84 (52.5)
LCR	0.24 (0.07, 0.90)	0.24 (0.07, 0.91)	0.24 (0.06, 0.90)	0.775
CAR	154.88 (42.62, 493.89)	154.93 (42.36, 502.48)	152.82 (43.00, 492.59)	0.830

Characteristic	Univariate Cox regression		Multivariate Cox regression
Characteristic	HR (95%CI)	P value	HR (95%CI)	P value
Age	1.03 (1.01‒⁠1.05)	<0.001	1.03 (1.01‒⁠1.05)	0.007
Smoking history	1.96 (1.08‒⁠3.56)	0.028	2.10 (1.11‒⁠3.96)	0.023
Liver disease	4.03 (2.16‒⁠7.53)	<0.001	2.91 (1.50‒⁠5.62)	0.001
Hyperlipidemia	0.40 (0.19‒⁠0.86)	0.018	0.51 (0.23‒⁠1.11)	0.088
Hemoglobin	0.99 (0.98‒⁠1.00)	0.002	1.00 (0.99‒⁠1.01)	0.793
Total protein	0.96 (0.94‒⁠0.99)	0.002	0.97 (⁠0.94‒1.03)	0.161
Albumin	0.94 (0.91‒⁠0.97)	<0.001	1.01 (0.94‒⁠1.08)	0.769
GPT	0.98 (0.97‒⁠1.00)	0.046	0.99 (0.97‒⁠1.01)	0.172
CEA	1.01 (1.00‒⁠1.01)	<0.001	1.00 (1.00‒⁠1.01)	0.096
CA724	1.09 (1.06‒⁠1.13)	<0.001	1.08 (1.04‒⁠1.12)	<0.001
D-D	1.06 (1.00‒⁠1.11)	0.034	1.01 (0.94‒⁠1.10)	0.747
AJCC clinical stage	2.03 (1.42‒⁠2.91)	<0.001	1.87 (1.29‒⁠2.72)	<0.001

Characteristic	Univariate Cox regression		Multivariate Cox regression
Characteristic	HR (95%CI)	P value	HR (95%CI)	P value
Age	1.03 (1.01‒⁠1.05)	<0.001	1.03 (1.01‒⁠1.05)	0.007
Smoking history	1.96 (1.08‒⁠3.56)	0.028	2.10 (1.11‒⁠3.96)	0.023
Liver disease	4.03 (2.16‒⁠7.53)	<0.001	2.91 (1.50‒⁠5.62)	0.001
Hyperlipidemia	0.40 (0.19‒⁠0.86)	0.018	0.51 (0.23‒⁠1.11)	0.088
Hemoglobin	0.99 (0.98‒⁠1.00)	0.002	1.00 (0.99‒⁠1.01)	0.793
Total protein	0.96 (0.94‒⁠0.99)	0.002	0.97 (⁠0.94‒1.03)	0.161
Albumin	0.94 (0.91‒⁠0.97)	<0.001	1.01 (0.94‒⁠1.08)	0.769
GPT	0.98 (0.97‒⁠1.00)	0.046	0.99 (0.97‒⁠1.01)	0.172
CEA	1.01 (1.00‒⁠1.01)	<0.001	1.00 (1.00‒⁠1.01)	0.096
CA724	1.09 (1.06‒⁠1.13)	<0.001	1.08 (1.04‒⁠1.12)	<0.001
D-D	1.06 (1.00‒⁠1.11)	0.034	1.01 (0.94‒⁠1.10)	0.747
AJCC clinical stage	2.03 (1.42‒⁠2.91)	<0.001	1.87 (1.29‒⁠2.72)	<0.001

Item	Age≥65 years		Age<65 years
Item	HR (95%CI)	P value	HR (95%CI)	P value
Age	1.03 (1.01‒⁠1.05)	0.009	1.01 (0.97‒⁠1.05)	0.649
Smoking history	1.78 (0.86‒⁠3.72)	0.123	2.40 (1.02‒⁠5.68)	0.046
Liver disease	2.82 (1.50‒⁠5.28)	0.001	1.80 (0.55‒⁠5.83)	0.330
CA724	1.03 (0.99‒⁠1.08)	0.118	1.12 (1.07‒⁠1.16)	<0.001
AJCC clinical stage	2.15 (1.50‒⁠3.07)	<0.001	3.50 (1.89‒⁠6.50)	<0.001

结直肠癌无进展生存时间预测模型及影响因素研究

Study on prediction model and influencing factors of progression-free survival in colorectal cancer

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