Hou Ling, Min Ming, Hou Rui, Tan Wei, Zhang Minghua, Liu Qianfei
Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Hubei, China.
Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China.
PeerJ. 2025 Feb 25;13:e18989. doi: 10.7717/peerj.18989. eCollection 2025.
Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD.
A cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC).
Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients.
The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction.
炎症反应和凝血系统在慢性阻塞性肺疾病（COPD）临床病情恶化的发病机制中起关键作用，这促使我们推测全身凝血-炎症（SCI）指数与COPD临床病情恶化相关。
分析了2018年1月至2021年12月的957例COPD患者队列（平均年龄：68.4±7.8岁；74.4%为男性）。使用准确度、精确度、召回率、F1分数和受试者工作特征曲线下面积（AUC-ROC）评估了六种机器学习模型（XGBoost、逻辑回归、随机森林、弹性网（ENT）、支持向量机（SVM）和K近邻（KNN））。
我们的研究纳入了957例患者，其中171例被归类为COPD临床病情恶化（cd-COPD）队列。cd-COPD组和非cd-COPD组在年龄、呼吸衰竭等合并症、C反应蛋白、淋巴细胞计数、红细胞分布宽度（RDW）、SCI、降钙素原（PCT）和D-二聚体方面存在显著差异。关于机器学习和模型比较，与其他五个机器学习（ML）模型相比，SVM模型在训练集和测试集上均表现出一致的性能和强大的泛化能力。SCI指数作为最具影响力的预测指标，在cd-COPD患者中的中位数为93.08，而非cd-COPD患者为81.67。
与COPD患者相比，cd-COPD患者的SCI显著升高，SVM在cd-COPD预测中表现出可靠的性能。
