Department of Biostatistics, Colleges of Medicine, Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Leukemia. 2024 Apr;38(4):720-728. doi: 10.1038/s41375-024-02166-1. Epub 2024 Feb 15.
Current strategies to treat pediatric acute lymphoblastic leukemia rely on risk stratification algorithms using categorical data. We investigated whether using continuous variables assigned different weights would improve risk stratification. We developed and validated a multivariable Cox model for relapse-free survival (RFS) using information from 21199 patients. We constructed risk groups by identifying cutoffs of the COG Prognostic Index (PI) that maximized discrimination of the predictive model. Patients with higher PI have higher predicted relapse risk. The PI reliably discriminates patients with low vs. high relapse risk. For those with moderate relapse risk using current COG risk classification, the PI identifies subgroups with varying 5-year RFS. Among current COG standard-risk average patients, PI identifies low and intermediate risk groups with 96% and 90% RFS, respectively. Similarly, amongst current COG high-risk patients, PI identifies four groups ranging from 96% to 66% RFS, providing additional discrimination for future treatment stratification. When coupled with traditional algorithms, the novel PI can more accurately risk stratify patients, identifying groups with better outcomes who may benefit from less intensive therapy, and those who have high relapse risk needing innovative approaches for cure.
目前治疗小儿急性淋巴细胞白血病的策略依赖于使用分类数据的风险分层算法。我们研究了使用不同权重的连续变量是否会改善风险分层。我们使用来自 21199 名患者的信息开发并验证了一个用于无复发生存 (RFS) 的多变量 Cox 模型。我们通过确定 COG 预后指数 (PI) 的截断值来构建风险组，这些截断值最大限度地提高了预测模型的区分能力。PI 较高的患者具有更高的预测复发风险。PI 可靠地区分了低复发风险和高复发风险的患者。对于那些使用当前 COG 风险分类的中度复发风险患者，PI 确定了具有不同 5 年 RFS 的亚组。在当前 COG 标准风险的普通患者中，PI 分别确定了 96%和 90%的 RFS 的低风险和中风险组。同样，在当前 COG 高危患者中，PI 确定了四个从 96%到 66%RFS 的组，为未来的治疗分层提供了额外的区分度。当与传统算法结合使用时，新的 PI 可以更准确地对患者进行风险分层，确定具有更好结局的患者，他们可能受益于强度较低的治疗，而那些复发风险较高的患者则需要创新的方法来治愈。
