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<p>The aim of this study was to develop and validate a nomogram predicting progression-free survival (PFS) for adult patients with positive acute lymphoblastic leukemia(Ph + ALL) who have undergone allogeneic hematopoietic stem cell transplantation(allo-HSCT) and tyrosine kinase inhibitor(TKI) treatment. Data were retrospectively collected from 176 adult patients diagnosed with Ph + ALL and treated with allo-HSCT and TKIs at The First Affiliated Hospital, Zhejiang University School of Medicine, between January 2015 and May 2023. 70% of the patients were randomly assigned to the training group(<i>n</i> = 124) and 30% of the patients were assigned to the validation group(<i>n</i> = 52). Univariate Cox regression analysis and Akaike Information Criterion(AIC) were utilized to identify significant predictive factors, leading to the development of a nomogram designed to forecast the probability of PFS at 6, 9, and 12 months post-transplantation. The final nomogram incorporated three key variables: presence of complex additional cytogenetic abnormalities (ACAs), minimal residual disease (MRD) status prior to allo-HSCT, and <i>IKZF1</i> gene deletions. The calibration curves showed excellent consistency between the nomogram prediction and actual observation for 6-, 9- and 12-month PFS in the training set and validation set. The C-index of the training set was 0.726(95%CI: 0.635–0.816), which was no significantly different from the validation set(C-index = 0.774, 95%CI: 0.674–0.875, <i>P</i> &gt; 0.05). This study may provide a simple and efficient prediction model for patients with Ph + ALL undergoing allo-HSCT and TKIs, which can accurately predict PFS subsequent to transplantation. This tool could potentially aid clinicians in decision-making processes and improve patient outcomes.</p>

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Adult patients with Philadelphia chromosome positive acute lymphoblastic leukemia undergoing allogeneic hematopoietic stem cell transplantation and tyrosine kinase inhibitors: development and validation of a clinical prediction model based on cytogenetics, IKZF1 deletions and minimal residual disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月23日

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  • Jing Zheng,
  • Yuping Zhou,
  • Yanmin Zhao,
  • Yi Luo,
  • Jian Yu,
  • Xiaoyu Lai,
  • Jinuo Wang,
  • Yishan Ye,
  • Lizhen Liu,
  • Huarui Fu,
  • Luxin Yang,
  • Yibo Wu,
  • Jie Sun,
  • Weiyan Zheng,
  • Jingsong He,
  • Yi Zhao,
  • Wenjun Wu,
  • Zhen Cai,
  • Guoqing Wei,
  • He Huang,
  • Weiming Li,
  • Jimin Shi

摘要

The aim of this study was to develop and validate a nomogram predicting progression-free survival (PFS) for adult patients with positive acute lymphoblastic leukemia(Ph + ALL) who have undergone allogeneic hematopoietic stem cell transplantation(allo-HSCT) and tyrosine kinase inhibitor(TKI) treatment. Data were retrospectively collected from 176 adult patients diagnosed with Ph + ALL and treated with allo-HSCT and TKIs at The First Affiliated Hospital, Zhejiang University School of Medicine, between January 2015 and May 2023. 70% of the patients were randomly assigned to the training group(n = 124) and 30% of the patients were assigned to the validation group(n = 52). Univariate Cox regression analysis and Akaike Information Criterion(AIC) were utilized to identify significant predictive factors, leading to the development of a nomogram designed to forecast the probability of PFS at 6, 9, and 12 months post-transplantation. The final nomogram incorporated three key variables: presence of complex additional cytogenetic abnormalities (ACAs), minimal residual disease (MRD) status prior to allo-HSCT, and IKZF1 gene deletions. The calibration curves showed excellent consistency between the nomogram prediction and actual observation for 6-, 9- and 12-month PFS in the training set and validation set. The C-index of the training set was 0.726(95%CI: 0.635–0.816), which was no significantly different from the validation set(C-index = 0.774, 95%CI: 0.674–0.875, P > 0.05). This study may provide a simple and efficient prediction model for patients with Ph + ALL undergoing allo-HSCT and TKIs, which can accurately predict PFS subsequent to transplantation. This tool could potentially aid clinicians in decision-making processes and improve patient outcomes.