Development and validation of a predictive model for tumor lysis syndrome in childhood acute lymphoblastic leukemia

IF 2.1 4区医学 Q3 HEMATOLOGY

Leukemia research Pub Date : 2024-09-19 DOI:10.1016/j.leukres.2024.107587

Yao Xiao , Li Xiao , Ximing Xu , Xianmin Guan , Yuxia Guo , Yali Shen , XiaoYing Lei , Ying Dou , Jie Yu

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引用次数: 0

Abstract

Background

Tumor lysis syndrome (TLS) frequently manifests shortly after induction chemotherapy for acute lymphoblastic leukemia (ALL), with the potential for swift progression. This study endeavored to develop a nomogram to predict the risk of TLS, utilizing clinical indicators present at the time of ALL diagnosis.

Methods

We retrospectively gathered data from 2243 patients with ALL, spanning December 2008 to December 2021, utilizing the clinical research big data platform of the National Center for Clinical Research on Children's Health and Diseases. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to filter variables and identify predictors, followed by the application of multivariate logistic regression to construct the nomogram.

Results

The LASSO regression identified six critical variables among ALL patients, upon which a nomogram was subsequently constructed. Multifactorial logistic regression revealed that an elevated white blood cell count (WBC), serum phosphorus <2.1 mmol/L, potassium <3.5 mmol/L, aspartate transaminase (AST) ≥50 U/L, uric acid (UA) ≥476μmol/L, and the presence of acute kidney injury (AKI) at the time of initial diagnosis were significant risk factors for the development of TLS in ALL patients (P<0.05). The predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.824 [95 % CI (0.783, 0.865)], with an internal validation AUC of 0.859 [95 % CI (0.806, 0.912)]. The Hosmer-Lemeshow goodness-of-fit test confirmed the model’s robustness (P=0.687 for the training cohort; P=0.888 for the validation cohort). Decision curve analysis (DCA) indicated that the predictive model provided substantial clinical benefit across threshold probabilities ranging from 10 % to 70 %.

Conclusions

A nomogram incorporating six predictive variables holds significant potential for accurately forecasting TLS in pediatric patients with ALL.

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儿童急性淋巴细胞白血病肿瘤溶解综合征预测模型的开发与验证

背景急性淋巴细胞白血病（ALL）诱导化疗后不久常出现肿瘤溶解综合征（TLS），并有可能迅速恶化。方法我们利用国家儿童健康与疾病临床研究中心的临床研究大数据平台，回顾性地收集了2243例ALL患者的数据，时间跨度为2008年12月至2021年12月。结果 LASSO回归在ALL患者中发现了6个关键变量，并据此构建了提名图。多因素逻辑回归显示，白细胞计数（WBC）升高、血清磷<2.1 mmol/L、钾<3.5 mmol/L、天门冬氨酸转氨酶（AST）≥50 U/L、尿酸（UA）≥476μmol/L以及初诊时存在急性肾损伤（AKI）是ALL患者发生TLS的显著风险因素（P<0.05）。预测模型的接收者操作特征曲线下面积（AUC）为 0.824 [95 % CI (0.783, 0.865)]，内部验证 AUC 为 0.859 [95 % CI (0.806, 0.912)]。Hosmer-Lemeshow 拟合优度检验证实了该模型的稳健性（训练队列的 P=0.687；验证队列的 P=0.888）。结论包含六个预测变量的提名图在准确预测小儿 ALL 患者的 TLS 方面具有巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Leukemia research 医学-血液学

CiteScore

4.00

自引率

3.70%

发文量

259

审稿时长

1 months

期刊介绍： Leukemia Research an international journal which brings comprehensive and current information to all health care professionals involved in basic and applied clinical research in hematological malignancies. The editors encourage the submission of articles relevant to hematological malignancies. The Journal scope includes reporting studies of cellular and molecular biology, genetics, immunology, epidemiology, clinical evaluation, and therapy of these diseases.