Pang Naidong, Tian Ying, Chi Hongjie, Fu Xiaohong, Li Xin, Wang Shuyu, Pan Feifei, Wang Dongying, Xu Lin, Luo Jingyi, Liu Aijun, Liu XingPeng
Department of Cardiology, Heart Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Department of Hematology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Cardiooncology. 2025 May 15;11(1):45. doi: 10.1186/s40959-025-00342-5.
Cardiac involvement is the primary driver of death in systemic light chain (AL) amyloidosis. However, the early prediction of cardiac death risk in AL amyloidosis remains insufficient.
We aimed to develop a novel prediction model and prognostic scoring system that enables early identification of these high-risk individuals.
This study enrolled 235 patients with confirmed AL cardiac amyloidosis from three hospitals. Patients from the first hospital were randomly assigned to the training and internal validation sets in an 8:2 ratio, while the external validation set comprised patients from the other two hospitals. Participants were categorized into a cardiac death group and a non-cardiac death group (including survivors and those who died from other causes). Five different machine learning models were used to train model, and model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.
All five models showed excellent performance on the training and internal validation sets. In external validation, both the Logistic Regression (LR) and Random Forest models achieved an area under the ROC curve of 0.873 and 0.877, respectively, and exhibited superior calibration and decision curve analysis. Considering the comprehensive performance and clinical applicability, the LR model was selected as the final prediction model. The visualization results are ultimately presented in a nomogram. Further analyses were performed on the newly identified predictors.
This prediction model enables early identification and risk assessment of cardiac death in patients with AL amyloidosis, exhibiting considerable predictive ability.
心脏受累是系统性轻链（AL）淀粉样变性患者死亡的主要驱动因素。然而，对AL淀粉样变性患者心脏死亡风险的早期预测仍然不足。
我们旨在开发一种新型预测模型和预后评分系统，以便能够早期识别这些高危个体。
本研究纳入了来自三家医院的235例确诊为AL心脏淀粉样变性的患者。第一家医院的患者按8:2的比例随机分配到训练集和内部验证集，而外部验证集包括来自其他两家医院的患者。参与者被分为心脏死亡组和非心脏死亡组（包括幸存者和死于其他原因的患者）。使用五种不同的机器学习模型训练模型，并使用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析评估模型性能。
所有五种模型在训练集和内部验证集上均表现出优异的性能。在外部验证中，逻辑回归（LR）模型和随机森林模型的ROC曲线下面积分别为0.873和0.877，并且在校准和决策曲线分析方面表现出色。考虑到综合性能和临床适用性，选择LR模型作为最终预测模型。可视化结果最终以列线图呈现。对新确定的预测因素进行了进一步分析。
该预测模型能够对AL淀粉样变性患者的心脏死亡进行早期识别和风险评估，具有相当的预测能力。
