预处理血清乳酸脱氢酶（LDH）水平与多种癌症，包括转移性结直肠癌（mCRC）的不良预后有关。然而，在治疗过程中，很少有模型将生存与重复测量的LDH在时间上相关联。我们调查了mCRC中处理期间的LDH动态的预测价值。利用两个大型III期研究的数据（2L和3L+ mCRC设置，分别为824和210例），我们发现将长期的LDH数据与基线危险因素结合显著改善了生存预测。目前的LDH值表现最佳，相对于基线变量，显著提高了鉴别能力（受试者工作特征曲线下面积）4.5%~15.4%和预测准确性（布里尔分数）3.9%~15.0%。将所有四个长期LDH标志物结合在一起进一步提高了预测性能。在控制基线协变量和其他长期LDH指标后，目前的LDH水平仍然是mCRC中的一个显著危险因素，每单位的目前对数（LDH）增加使2L患者的死亡风险增加超过90％（P < 0.001），3L+患者的死亡风险增加60-70％（P < 0.01）。机器学习技术，如功能主成分分析（FPCA），从长期的LDH数据中提取了信息特征，捕获了99%以上的变异性，并实现了生存预测。基于提取的FPCA特征的无监督聚类将患者分为三组，具有不同的LDH动态和生存结果。因此，我们的方法为风险分层提供了有价值且经济高效的方式，并通过LDH轨迹改善了mCRC的生存预测。本文章受版权保护。版权所有。

Pre-treatment serum lactate dehydrogenase (LDH) levels have been associated with poor prognosis in several types of cancer, including metastatic colorectal cancer (mCRC). However, very few models link survival to longitudinal LDH measured repeatedly over time during treatment. We investigated the prognostic value of on-treatment LDH dynamics in mCRC. Using data from two large Phase III studies (2L and 3L+ mCRC settings, n = 824 and 210, respectively), we found that integrating longitudinal LDH data with baseline risk factors significantly improved survival prediction. Current LDH values performed best, enhancing discrimination ability (area under the receiver operating characteristic curve) by 4.5% ~ 15.4% and prediction accuracy (Brier score) by 3.9% ~ 15.0% compared to baseline variables. Combining all four longitudinal LDH markers further improved predictive performance. After controlling for baseline covariates and other longitudinal LDH indicators, current LDH levels remained a significant risk factor in mCRC, increasing mortality risk by over 90% (P < 0.001) in 2L patients and 60-70% (P < 0.01) in 3L+ patients per unit increment in current log(LDH). Machine learning techniques like functional principal component analysis (FPCA) extracted informative features from longitudinal LDH data, capturing over 99% of variability and allowing prediction of survival. Unsupervised clustering based on the extracted FPCA features stratified patients into three groups with distinct LDH dynamics and survival outcomes. Hence, our approaches offer a valuable and cost-effective way for risk stratification and improves survival prediction in mCRC using LDH trajectories.This article is protected by copyright. All rights reserved.