肌萎缩侧索硬化症预后因素的动态影响及个体生存预测。

Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease.

机构信息

Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, Guangzhou, China.

出版信息

Ann Clin Transl Neurol. 2023 Jun;10(6):892-903. doi: 10.1002/acn3.51771. Epub 2023 Apr 4.

DOI:10.1002/acn3.51771

PMID:37014017

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10270250/

Abstract

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time.

METHODS

A total of 1260 ALS patients from the PRO-ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score.

RESULTS

Three baseline covariates and seven time-dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6-month survival probability according to the longitudinal information of individual patients.

INTERPRETATION

We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions.

摘要

目的

肌萎缩侧索硬化症（ALS）是一种影响运动神经元的神经退行性疾病，不同患者的疾病进展和生存存在广泛的异质性。因此，一个准确的预测模型对于及时干预和延长患者的生存时间至关重要。

方法

对 PRO-ACT 数据库中的 1260 名 ALS 患者进行分析，包括患者的人口统计学、临床变量和死亡报告。我们通过标记法构建了 ALS 动态 Cox 模型。通过计算曲线下面积（AUC）和 Brier 评分评估模型在不同标记时间点的预测性能。

结果

选择了三个基线协变量和七个时间依赖协变量来构建 ALS 动态 Cox 模型。为了进行更好的预后分析，该模型确定了治疗、白蛋白、肌酐、钙、红细胞压积和血红蛋白的动态效应。其预测性能（在所有标记时间点，AUC≥0.70，Brier 得分≤0.12）优于传统 Cox 模型，并且根据个体患者的纵向信息预测动态 6 个月生存率。