Wang Yudi, Jia Suzhen, Jiang Yinyan, Cao Xiubo, Ge Shengchen, Yang Kaiqian, Chen Yi, Yu Kang
Department of Hematology, The First Affiliated Hospital of Wenzhou Medical University, No. 342, Wenrui Avenue, Ouhai District, Wenzhou City, Zhejiang Province China.
Department of Hematology, Shaoxing People's Hospital, Shaoxing, China.
Indian J Hematol Blood Transfus. 2024 Oct;40(4):613-620. doi: 10.1007/s12288-024-01767-1. Epub 2024 Apr 12.
To find the independent factors affecting the prognosis of AITL patients, establish a novel predictive model, and stratify the prognosis of AITL patients. We retrospectively analyzed the clinical data of 86 patients diagnosed with AITL in the First Affiliated Hospital of Wenzhou Medical University from December 2010 to March 2022. The clinical features, recurrence time, and death time of patients were collected and analyzed statistically. The median age of our patients was 68 years old, and the male-to-female ratio was 2.2: 1. There are differences between males and females in ECOG PS score (p = 0.037), β2 microglobulin levels (p = 0.018) and IgM (p = 0.021). Multivariate COX regression analysis showed that C-reactive protein > 39.3 mg/L (hazard ratio (HR), 5.41; p = 0.0001), Age > 66 years (hazard ratio (HR), 3.06; p = 0.0160), Ki67 positive (hazard ratio (HR), 4.86; p = 0.0010) and early progression of disease within 24 months (POD24) after diagnosis (hazard ratio (HR), 12.47; p = 0.0001) were independent factors affecting the prognosis of OS. KM analysis showed that the predictive model established by these four factors could effectively predict the prognosis of patients with AITL (p < 0.0001), and the ROC curve showed that the predictive ability of the new predictive model (AUC = 0.909) was significantly better than that of the traditional predictive models, such as IPI (AUC = 0.730), PIT (AUC = 0.720), PIAI (AUC = 0.715) and AITL score (AUC = 0.724). Age, C-reactive protein, Ki67, and POD24 were independent factors affecting the prognosis of OS. The prognostic model established by them combined clinical features, and serological and pathological indicators and could effectively predict the prognosis of AITL patients.
The online version contains supplementary material available at 10.1007/s12288-024-01767-1.
为寻找影响血管免疫母细胞性T细胞淋巴瘤(AITL)患者预后的独立因素,建立一种新的预测模型,并对AITL患者的预后进行分层。我们回顾性分析了2010年12月至2022年3月在温州医科大学附属第一医院确诊为AITL的86例患者的临床资料。收集患者的临床特征、复发时间和死亡时间并进行统计学分析。我们患者的中位年龄为68岁,男女比例为2.2:1。男性和女性在东部肿瘤协作组(ECOG)体能状态(PS)评分(p = 0.037)、β2微球蛋白水平(p = 0.018)和IgM(p = 0.021)方面存在差异。多因素COX回归分析显示,C反应蛋白>39.3 mg/L(风险比(HR),5.41;p = 0.0001)、年龄>66岁(风险比(HR),3.06;p = 0.0160)、Ki67阳性(风险比(HR),4.86;p = 0.0010)以及诊断后24个月内疾病早期进展(POD24)(风险比(HR),12.47;p = 0.0001)是影响总生存期(OS)预后的独立因素。Kaplan-Meier(KM)分析表明,由这四个因素建立的预测模型可以有效预测AITL患者的预后(p < 0.0001),并且ROC曲线显示新预测模型的预测能力(曲线下面积(AUC)= 0.909)明显优于传统预测模型,如国际预后指数(IPI)(AUC = 0.730)、预后指数(PIT)(AUC = 0.720)、预后指数(PIAI)(AUC = 0.715)和AITL评分(AUC = 0.7