Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China.
Nanjing Medical University, Nanjing, China.
PeerJ. 2024 May 13;12:e17340. doi: 10.7717/peerj.17340. eCollection 2024.
This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor B (ARB) drugs and to identify key features affecting patient prognosis using an unsupervised learning method.
A large-scale clinical dataset, including patient information, medical history, and laboratory test results, was collected. Two hundred patients with COVID-19 and hypertension were included. After cluster analysis, patients were divided into good and poor prognosis groups. The unsupervised learning method was used to evaluate clinical characteristics and prognosis, and patients were divided into different prognosis groups. The improved wild dog optimization algorithm (IDOA) was used for feature selection and cluster analysis, followed by the IDOA-k-means algorithm. The impact of ACEI/ARB drugs on patient prognosis and key characteristics affecting patient prognosis were also analysed.
Key features related to prognosis included baseline information and laboratory test results, while clinical symptoms and imaging results had low predictive power. The top six important features were age, hypertension grade, MuLBSTA, ACEI/ARB, NT-proBNP, and high-sensitivity troponin I. These features were consistent with the results of the unsupervised prediction model. A visualization system was developed based on these key features.
Using unsupervised learning and the improved k-means algorithm, this study accurately analysed the prognosis of patients with COVID-19 and hypertension. The use of ACEI/ARB drugs was found to be a protective factor for poor clinical prognosis. Unsupervised learning methods can be used to differentiate patient populations and assess treatment effects. This study identified important features affecting patient prognosis and developed a visualization system with clinical significance for prognosis assessment and treatment decision-making.
本研究旨在评估 COVID-19 合并高血压患者使用血管紧张素转换酶抑制剂（ACEI）/血管紧张素受体阻滞剂（ARB）药物治疗的预后，并使用无监督学习方法确定影响患者预后的关键特征。
收集了一个大规模的临床数据集，包括患者信息、病史和实验室检查结果。纳入 200 例 COVID-19 合并高血压患者。经聚类分析后，将患者分为预后良好和预后不良组。使用无监督学习方法评估临床特征和预后，并将患者分为不同预后组。采用改进的野犬优化算法（IDOA）进行特征选择和聚类分析，然后采用 IDOA-k 均值算法。分析 ACEI/ARB 药物对患者预后的影响及影响患者预后的关键特征。
与预后相关的关键特征包括基线信息和实验室检查结果，而临床症状和影像学结果的预测能力较低。前六个重要特征为年龄、高血压分级、MuLBSTA、ACEI/ARB、NT-proBNP 和高敏肌钙蛋白 I。这些特征与无监督预测模型的结果一致。基于这些关键特征开发了一个可视化系统。
本研究使用无监督学习和改进的 k 均值算法准确分析了 COVID-19 合并高血压患者的预后。使用 ACEI/ARB 药物被认为是不良临床预后的保护因素。无监督学习方法可用于区分患者人群并评估治疗效果。本研究确定了影响患者预后的重要特征，并开发了一个具有临床意义的可视化系统，用于预后评估和治疗决策。
