COMPASS-CAT、Padua、Caprini和Khorana风险评估模型对肺癌患者静脉血栓栓塞预测效能比较

doi:10.13481/j.1671-587X.20260424

摘要/Abstract

摘要：

目的探讨肺癌患者发生静脉血栓栓塞（VTE）的独立危险因素，阐明4种风险评估模型（RAM）对肺癌患者发生VTE预测及风险评估效能的优势，探讨肺癌患者发生VTE的最佳RAM，并评估各模型在不同病理类型肺癌中的预测价值。方法收集2013年3月-2023年3月确诊肺癌的390例住院患者病历资料，将患者分为VTE组（130例）和非VTE组（260例）；收集所有肺癌患者的一般资料和临床资料，共纳入35个相关危险因素。通过Logistic回归分析筛选出肺癌患者发生VTE的独立危险因素，应用Khorana RAM、 Caprini RAM、 COMPASS-CAT RAM和Padua RAM对肺癌患者进行评分，绘制4种RAM的受试者工作特征（ROC）曲线并计算曲线下面积（AUC）；MedCalc软件分析比较4种RAM对肺癌患者发生VTE风险的预测价值，比较同一RAM对不同肺癌患者发生VTE风险预测概率。结果逐步回归分析，静脉曲张［比值比（OR）=2.23， P<0.05）］、COMPASS-CAT RAM≥7分（OR=9.332，P<0.05）、急性疾病入院（OR=2.732，P<0.05）、ZPS评分（OR=2.178，P<0.05）和D-二聚体（OR=3.034，P<0.05）是肺癌患者发生VTE的独立危险因素。ROC曲线：COMPASS-CAT RAM对肺癌患者发生VTE的预测效能（AUC=0.742）显著优于Caprini RAM（AUC=0.613）、Khorana RAM（AUC=0.558）和Padua RAM（AUC=0.642）。MedCalc软件分析，4种RAM预测不同类型肺癌患者发生VTE方面比较差异无统计学意义（P>0.05）。结论静脉曲张、COMPASS-CAT RAM≥7分、急性疾病入院、D-二聚体和ZPS评分是肺癌患者发生VTE的独立危险因素。COMPASS-CAT RAM对肺癌患者发生VTE风险预测价值高于其他3种RAM。4种RAM对肺腺癌、肺鳞癌和小细胞肺癌发生VTE预测效能比较无显著差别。

关键词: 静脉血栓栓塞, COMPASS-CAT风险评估模型, Khorana风险评估模型, Caprini风险评估模型, Padua风险评估模型, 肺肿瘤

Abstract:

Objective To investigate the independent risk factors for venous thromboembolism （VTE） in lung cancer patients， clarify the respective advantages of four risk assessment models（RAM） in predicting and assessing the risk of VTE occurrence in lung cancer patients， explore the most suitable RAM for predicting VTE occurrence in lung cancer patients， and to evaluate the predictive value of each model in different pathological types of lung cancer. Methods A total of 390 lung cancer patients admitled to hospital from Mar.2013 to Mar.2013 were included in this study， with 130 cases allocated to VTE group and 260 cases to non-VTE group based on their medical records. The general and clinical data characteristics of all lung cancer patients were collected， and 35 related risk factors were included. The independent risk factors of lung cancer patients with VTE were screened by Logistic regression analysis. Khorana RAM， Caprini RAM， COMPASS-CAT RAM and Padua RAM were used to score the lung cancer patients. The receiver operating characteristic （ROC） curves of the four RAM were plotted and the area under curve （AUC） was caculated； MedCalc software was used to analyze and compare the predictive value of the four RAM for VTE risk in the lung cancer patients， and to compare the prediction probability of the same RAM in the different lung cancer patients. Results The results from stepwise regression analysis showed that varicose veins ［odds ratio （OR）=2.23， P<0.05）］， COMPASS-CAT RAM≥7 points （OR=9.332， P<0.05）， acute disease hospitalization （OR=2.732， P<0.05）， ZPS score （OR=2.178， P<0.05）， and D-dimer （OR=3.034， P<0.05） were independent risk factors for VTE in the lung cancer patients. The ROC curves were plotted for the four models， showing that COMPASS-CAT RAM’s predictive performance for VTE occurrence in the lung cancer patients （AUC=0.742） was significantly superior to Caprini RAM （AUC=0.613）， Khorana RAM （AUC=0.558）， and Padua RAM （AUC=0.642）. The analysis using MedCalc software revealed that there were no significant differences in the prediction of four kinds of RAM for VTE occurreuce in the lung cancer patients with different types （P>0.05）. Conclusion Varicose veins， COMPASS CATRAM≥7 points， acute disease hospitalization， D-dimer score and ZPS score are the independent risk factors for VTE. COMPASS-CAT RAM has higher predictive value for VTE risk in the lung cancer patients than Khorana RAM， Padua RAM and Caprini RAM. It is more suitable for the risk prediction of VTE occurrence in the lung cancer patients. There is no significant difference between four kinds of RAM in predicting VTE occurence in lung adenocarcinoma， squamous cell carcinoma and small cell lung cancer.

Key words: Venous thromboembolism, COMPASS-CAT risk assessment model, Khorana risk assessment model, Caprini risk assessment model, Padua risk assessment model, Lung neoplasm

中图分类号:

R563.5

郭佳琪,徐磊. COMPASS-CAT、Padua、Caprini和Khorana风险评估模型对肺癌患者静脉血栓栓塞预测效能比较[J]. 吉林大学学报(医学版), 2026, 52(4): 1116-1127.

Jiaqi GUO,Lei XU. Comparison of predictive performance of risk assessment models of COMPASS-CAT， Padua， Caprini， and Khorana for venous thromboembolism in lung cancer patients[J]. Journal of Jilin University(Medicine Edition), 2026, 52(4): 1116-1127.

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Variable	Non-VTE(n=260)	VTE (n=130)	χ²/t	P
Age (year）	66.56±4.44	66.55±4.4	0.032	0.974
Gender			2.393	0.122
Male	135 (51.92)	79 (60.77)
Female	125 (48.08)	51 (39.23)
Smoking history			2.158	0.142
Yes	118 (45.38)	70 (53.85)
No	142 (54.62)	60 (46.15)
BMI（kg·m^-2）			0.460	0.795
<25	23(8.85)	14(10.77)
≥25	157(60.38)	75 (57.69)
≥30	80 (30.77)	41 (31.54)

Variable	Non-VTE (n=260)	VTE (n=130)	χ²/t	P
Lung cancer type			5.39	0.068
Adenocarcinoma	121(46.54)	60 (46.15)
Squamous cell carcinoma	99 (38.08)	60 (46.15)
Small cell lung cancer	40(15.38)	10 (7.69)
Occurrence less than 6 months			0	1
Yes	201 (77.31)	100 (76.92)
No	59 (22.69)	30 (23.08)
Tumor stage			10.854	<0.001
Ⅰ-Ⅱ	56 (21.54)	10 (7.69)
Ⅲ-Ⅳ	204 (78.46)	120 (92.31)
ZPS score	0.70±0.68	1.68±1.07	-9.115	<0.001
Central venous catheterization			0.955	0.328
Yes	33 (12.69)	22 (16.92)
No	227 (87.31)	108 (83.08)
Number of transferred organ			Fisher	0.609
0	158 (60.77)	73 (56.15)
1	69 (26.54)	42 (32.31)
2	25 (9.62)	10 (7.69)
3	8 (3.08)	5 (3.85)
Treatment modalities			Fisher	0.673
Platinum-based chemotherapy	173 (66.54)	86 (66.15)
Radiotherapy	15 (5.77)	5 (3.85)
Chemotherapy targeted	34 (13.08)	16 (12.31)
Surgical treatment	7 (2.69)	7 (5.38)
Targeted	31 (11.92)	16 (12.31)

Variable	Non-VTE (n=260)	VTE (n=130)	χ²	P
Underlying pulmonary disease			6.754	0.009
Yes	20 (7.69)	22 (16.92)
No	240 (92.31)	108 (83.08)
Hypertension			2.808	0.094
Yes	60 (23.08)	41 (31.54)
No	200 (76.92)	89 (68.46)
Diabetes			0.032	0.858
Yes	25 (9.62)	14 (10.77)
No	235 (90.38)	116 (89.23)
Coronary heart disease			0.078	0.780
Yes	23(9.23)	15 (11.54)
No	236 (90.77)	115(88.46)
Cerebrovascular disease			16.142	<0.001
Yes	17 (6.54)	27 (20.77)
No	243 (93.46)	103 (79.23)
Respiratory failure			35.486	<0.001
Yes	3 (1.15)	23 (17.69)
No	257 (98.85)	107 (82.31)
Atrial fibrillation			Fisher	0.167
Yes	4 (1.54)	5 (3.85)
No	256 (98.46)	125 (96.15)
Heart failure			25.63	<0.001
Yes	6 (2.31)	22 (16.92)
No	254 (97.69)	108 (83.08)
Lung infection			28.154	<0.001
Yes	50 (19.23)	59 (45.38)
No	210 (80.77)	71 (54.62)
Varicose veins in the lower extremities			74.793	<0.001
Yes	6 (2.31)	23 (17.69)
No	254 (97.69)	107 (82.30)
Hyperlipidemia			Fisher	0.111
Yes	6(2.31)	9 (6.92)
No	254 (97.69)	121 (93.08)
Hospitalization for acute illness			54.935	<0.001
Yes	13 (5.00)	51 (39.23)
No	247 (95.00)	79 (60.77)
History of surgery			2.667	0.102
Yes	79 (30.38)	51 (39.23)
No	181 (69.62)	79 (60.77)

Variable	Non-VTE (n=260)	VTE (n=130)	χ²/t/Z	P
White blood cell>11×10⁹L^-1			2.289	0.130
Yes	25 (9.62)	20 (15.38)
No	235 (90.38)	110 (84.62)
Haemoglobin<100 g·L^-1			0.094	0.759
Yes	57 (21.92)	26 (20.00)
No	203 (78.08)	104 (80.00)
Platelet≥350×10⁹L^-1			0.132	0.716
Yes	72 (27.69)	33 (25.38)
No	188 (72.31)	97 (74.62)
Total cholesterol≥6.22 mmol·L^-1			0.047	0.829
Yes	56 (21.54)	30 (23.08)
No	204 (78.46)	100 (76.92)
Low-density lipoprotein≥4.14 mmol·L^-1			0.110	0.741
Yes	102 (39.23)	48 (36.92)
No	158 (60.77)	82 (63.08)
Triglycerides≥2.26 mmol·L^-1			Fisher	<0.001
Yes	0 (0)	7 (5.38)
No	260 (100)	123 (94.62)
D-dimer	0.49（0.36,0.64）	1.77（0.97,4.82）	-12.553	<0.001

RAM	n	COMPASSCAT RAM		Padua RAM		Khorana RAM		Caprini RAM
RAM	n	Low risk	Risk	Low risk	Risk	Low risk	Risk	Low risk	Risk
Non-VTE	260	240(92.31)	20(7.69)	190(73.08)	70(26.92)	250(96.15)	10(3.85)	169(65.00)	91(35.00)
VTE	130	57(43.85)	73(56.15)	58(44.62)	72(55.38)	110(84.62)	20(15.38)	55(42.31)	75(57.69)
χ²		109.429		29.105		14.666		17.339
P		<0.001		<0.001		<0.001		<0.001