Ma J Y, Zhang Y H, Teng G S, Du C X, Zhang H Q, Wang Y, Li Y Q, Duan Y F, Zhou Y, Shao Z H, Bai J
Department of Hematology, the Second Hospital of Tianjin Medical University, Tianjin 300211, China.
State Key Laboratory of Experimental Hematology, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Zhonghua Yi Xue Za Zhi. 2024 Jul 2;104(25):2336-2341. doi: 10.3760/cma.j.cn112137-20240304-00476.
To investigate the risk factors of venous thrombosis in patients with polycythemia vera (PV) and establish a prediction model for venous thrombosis. PV patients with JAK2 gene mutation positive in the Second Hospital of Tianjin Medical University from September 2017 to November 2023 were retrospectively included. The patients were divided into groups according to whether they had venous thrombosis. After matching age and gender factors with propensity scores, 102 patients were included in the venous thrombosis group [46 males, 56 females, with a median age (, ) of 52 (44, 60) years] and 204 cases were included in the group without venous thrombosis [92 males, 112 females, with a median age of 52 (44, 59) years]. The clinical and laboratory characteristics, disease progression and incidence of gene mutation were compared between the two groups. The follow-up cohort ended on November 20, 2023, with a median follow-up [ (, )] of 11 (1, 53) years. Multivariate Cox risk model was used to analyze the influencing factors of venous thrombosis in PV patients, and establish a scoring system for the venous thrombosis risk factor prediction model of PV patients. Receiver operating characteristic (ROC) curve was used to evaluate the predictive efficiency of the model. Hemoglobin concentration, the ratio of hematopoietic volume≥55%, neutrophil to lymphocyte ratio≥5, hypertension, subcostal spleen≥5 cm and secondary myelofibrosis in venous thrombosis group were higher than those in non-venous thrombosis group (all <0.05). In addition, the proportion of history of thromboembolism, V617F gene mutation load (V617F%)≥50%, diabetes mellitus, ASXL1 mutation and secondary reticular silver staining≥3 in the venous thrombosis group were higher than those in the non-venous thrombosis group (all <0.05). The proportion of PV patients with 3 or more gene mutations was 44.1% (45/102) in venous thrombosis group, which was higher than that of PV patients without venous thrombosis 29.9% (61/204) (=0.014). The proportion of ASXL1 gene mutation in venous thrombosis group was 17.6% (18/102), which was higher than the 4.9% (10/204) in non-venous thrombosis group (<0.001). Multivariate Cox risk model analysis showed that previous thromboembolism history (2.031, 95%: 1.297-3.179, =0.002), V617F%≥50% (2.141 95%: 1.370-3.347, 0.001), ASXL1 mutation (=4.632, 95%: 1.497-14.336, =0.008), spleen subcostal≥5 cm (=1.771, 95% 1.047-2.996, =0.033) are the risk factors of venous thrombosis in PV patients. According to values, a score system for predicting risk of venous thrombosis in PV patients was established: previous history of thromboembolism, V617F%≥50% and spleen subcostoal≥5 cm were assigned 1 point respectively, and ASXL1 mutation was assigned 2 points. Low risk group: score 0, medium risk group: score 1-2, high risk group: score≥3. The ROC curve analysis of the model for predicting venous thrombosis in PV patients showed that the area under the curve (AUC) was 0.807 (95% 0.755-0.860), with the sensitivity of 88.2% and the specificity of 59.8% when the Youden index was 0.48. Previous thromboembolism history, V617F%≥50%, ASXL1 mutation, spleen subcostoal≥5 cm are risk factors of venous thrombosis in PV patients. The established prediction model has good prediction efficiency.
探讨真性红细胞增多症（PV）患者静脉血栓形成的危险因素，并建立静脉血栓形成的预测模型。回顾性纳入2017年9月至2023年11月在天津医科大学第二医院JAK2基因突变阳性的PV患者。根据是否发生静脉血栓将患者分组。在按倾向得分匹配年龄和性别因素后，静脉血栓形成组纳入102例患者[男性46例，女性56例，中位年龄（，）为52（44，60）岁]，无静脉血栓形成组纳入204例[男性92例，女性112例，中位年龄为52（44，59）岁]。比较两组的临床和实验室特征、疾病进展及基因突变发生率。随访队列于2023年11月20日结束，中位随访时间[（，）]为11（1，53）年。采用多因素Cox风险模型分析PV患者静脉血栓形成的影响因素，并建立PV患者静脉血栓形成危险因素预测模型的评分系统。采用受试者工作特征（ROC）曲线评估模型的预测效能。静脉血栓形成组的血红蛋白浓度、造血容积比例≥55%、中性粒细胞与淋巴细胞比值≥5、高血压、肋下脾脏≥5 cm及继发性骨髓纤维化均高于无静脉血栓形成组（均<0.05）。此外，静脉血栓形成组的血栓栓塞病史比例、V617F基因突变负荷（V617F%）≥50%、糖尿病、ASXL1突变及继发性网状纤维染色≥3均高于无静脉血栓形成组（均<0.05）。静脉血栓形成组PV患者有3个或更多基因突变的比例为44.1%（45/102），高于无静脉血栓形成的PV患者29.9%（61/204）（=0.014）。静脉血栓形成组ASXL1基因突变比例为17.6%（18/102），高于无静脉血栓形成组的4.9%（10/204）（<0.001）。多因素Cox风险模型分析显示，既往血栓栓塞病史（2.031，95%：1.297 - 3.179，=0.002）、V617F%≥50%（2.141 95%：1.370 - 3.347，0.001）、ASXL1突变（=4.632，95%：1.497 - 14.336，=0.008）、脾脏肋下≥5 cm（=1.771，95% 1.047 - 2.996，=0.033）是PV患者静脉血栓形成的危险因素。根据值，建立PV患者静脉血栓形成风险预测的评分系统：既往血栓栓塞病史、V617F%≥50%及脾脏肋下≥5 cm各赋值1分，ASXL1突变赋值2分。低风险组：评分0分，中风险组：评分1 - 2分，高风险组：评分≥3分。PV患者静脉血栓形成预测模型的ROC曲线分析显示，曲线下面积（AUC）为0.807（95% 0.755 - 0.860），约登指数为0.48时，灵敏度为88.2%，特异度为59.8%。既往血栓栓塞病史、V617F%≥50%、ASXL1突变、脾脏肋下≥5 cm是PV患者静脉血栓形成的危险因素。所建立的预测模型具有良好的预测效能。
Zotero 插件