Hu Chong, Yan Xiao, Song Henglian, Dong Qin, Yi Changying, Li Jianzhi, Lv Xin
Clinical Laboratory, Children's Hospital Affiliated to Shandong University, Jinan, China.
Clinical Laboratory, Jinan Children's Hospital, Jinan, China.
Front Cardiovasc Med. 2025 Jan 14;11:1522473. doi: 10.3389/fcvm.2024.1522473. eCollection 2024.
The nomogram is a powerful and robust tool in disease risk prediction that summarizes complex variables into a visual model that is interpretable with a quantified risk probability. In the current study, a nomogram was developed to predict the occurrence of coronary artery lesions (CALs) among patients with Kawasaki disease (KD). This is especially valuable in the early identification of the risk of CALs, which will lead to proper diagnosis and treatment to reduce their associated complications.
Retrospective clinical data of 677 children diagnosed with KD who were treated in the Children's Hospital Affiliated with Shandong University were analyzed. All the participants were divided into the CAL group and no CAL group according to their coronary echocardiography results. Least absolute shrinkage and selection operator (LASSO) regression was applied for the identification of the most informative predictors of CAL. Based on this, a nomogram was developed for accurate risk estimation.
The data were divided into a training set and a validation set. Receiver operating characteristic analysis, calibration curves, and decision curve analysis all supported the high accuracy and clinical utility of this model. LASSO regression highlighted five key predictors: sodium, hemoglobin, platelet count, D-dimer, and cystatin C. A nomogram based on these predictors was established and successfully validated in both datasets. In the training set, the AUC was 0.819 and in the validation set it was 0.844. The C-index of the calibration curve in the training set was 0.820, while in the validation set it was 0.844. In the decision curve analysis, the predictive benefit of the model was greater than zero when the threshold probability was below 95% in the training set and below 92% in the validation set.
The predictive factors identified through the LASSO regression approach and the development of the nomogram are important contributions in this respect. This model had a high predictive accuracy and reliability for identifying high-risk children in the very early stage of disease with remarkable precision, laying the foundation for personalized treatment strategies and targeted treatment and providing a strong scientific basis for precise therapeutic intervention.
列线图是疾病风险预测中一种强大且可靠的工具，它将复杂变量汇总为一个可视模型，该模型可通过量化的风险概率进行解读。在本研究中，我们开发了一种列线图来预测川崎病（KD）患者冠状动脉病变（CALs）的发生情况。这对于早期识别CALs风险尤为重要，因为这将有助于进行正确的诊断和治疗，以减少相关并发症。
分析了山东大学附属儿童医院收治的677例诊断为KD的儿童的回顾性临床数据。根据冠状动脉超声心动图结果，将所有参与者分为CAL组和无CAL组。应用最小绝对收缩和选择算子（LASSO）回归来确定CAL最具信息量的预测因子。在此基础上，开发了一种列线图用于准确的风险估计。
数据分为训练集和验证集。受试者工作特征分析、校准曲线和决策曲线分析均支持该模型的高准确性和临床实用性。LASSO回归突出了五个关键预测因子：钠、血红蛋白、血小板计数、D-二聚体和胱抑素C。基于这些预测因子建立了列线图，并在两个数据集中均成功验证。在训练集中，AUC为0.819，在验证集中为0.844。训练集中校准曲线的C指数为0.820，而在验证集中为0.844。在决策曲线分析中，当阈值概率在训练集中低于95%且在验证集中低于92%时，模型的预测效益大于零。
通过LASSO回归方法确定的预测因子以及列线图的开发在这方面具有重要意义。该模型在疾病极早期识别高危儿童方面具有很高的预测准确性和可靠性，为个性化治疗策略和靶向治疗奠定了基础，并为精确的治疗干预提供了有力的科学依据。
