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非裂滤泡中心细胞淋巴瘤和免疫母细胞肉瘤的预后因素。一种贝叶斯方法。

Prognostic factors for non-cleaved follicular center-cell lymphomas and immunoblastic sarcoma. A Bayesian approach.

作者信息

Aine R, Kataja M, Alavaikko M

出版信息

Scand J Haematol. 1984 May;32(5):475-87. doi: 10.1111/j.1600-0609.1984.tb02188.x.

Abstract

The Bayesian multivariate statistical method was applied to determine the relative strength and optimal combination of 17 variables in predicting survival of 151 patients with non-Hodgkin's lymphomas assigned as non-cleaved follicular center-cell and immunoblastic sarcoma types according to the classification of Lukes & Collins. Considering all the factors simultaneously, the analysis showed that the combination of stage, Hb level and location of the lymphoma was included in the best predictive model at each survival time studied. Additional factors were erythrocyte sedimentation rate, thrombocyte count and leucocyte count. Of the histological variables, only growth pattern and mitotic ratio in the biopsy specimen remained significant. At manually controlled computer simulation with these best indicators, this model would have given a correct classification for 69-78% of the patients at the 4 survival times studied. One can thus expect about 70% correct prognoses using this model.

摘要

采用贝叶斯多元统计方法,根据卢克斯和柯林斯的分类,确定17个变量在预测151例非霍奇金淋巴瘤患者(分类为非裂滤泡中心细胞型和免疫母细胞肉瘤型)生存情况时的相对强度和最佳组合。综合考虑所有因素,分析表明,在所研究的每个生存时间点,分期、血红蛋白水平和淋巴瘤位置的组合都包含在最佳预测模型中。其他因素包括红细胞沉降率、血小板计数和白细胞计数。在组织学变量中,活检标本中的生长模式和有丝分裂率仍然具有显著意义。在使用这些最佳指标进行人工控制的计算机模拟时,该模型在研究的4个生存时间点对69%-78%的患者做出了正确分类。因此,使用该模型可以预期约70%的正确预后。

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