Department of Respiratory Medicine, Federico II University, Naples, Italy.
Istituti Clinici Scientifici Maugeri IRCCS, Cardiac Rehabilitation Unit of Telese Terme Institute, Telese Terme, Italy.
Eur J Intern Med. 2022 Oct;104:66-72. doi: 10.1016/j.ejim.2022.07.019. Epub 2022 Jul 31.
One of the main problems in poorly controlled asthma is the access to the Emergency Department (ED). Using a machine learning (ML) approach, the aim of our study was to identify the main predictors of severe asthma exacerbations requiring hospital admission.
Consecutive patients with asthma exacerbation were screened for inclusion within 48 hours of ED discharge. A k-means clustering algorithm was implemented to evaluate a potential distinction of different phenotypes. K-Nearest Neighbor (KNN) as instance-based algorithm and Random Forest (RF) as tree-based algorithm were implemented in order to classify patients, based on the presence of at least one additional access to the ED in the previous 12 months.
To train our model, we included 260 patients (31.5% males, mean age 47.6 years). Unsupervised ML identified two groups, based on eosinophil count. A total of 86 patients with eosinophiles ≥370 cells/µL were significantly older, had a longer disease duration, more restrictions to daily activities, and lower rate of treatment compared to 174 patients with eosinophiles <370 cells/μL. In addition, they reported lower values of predicted FEV (64.8±12.3% vs. 83.9±17.3%) and FEV/FVC (71.3±9.3 vs. 78.5±6.8), with a higher amount of exacerbations/year. In supervised ML, KNN achieved the best performance in identifying frequent exacerbators (AUROC: 96.7%), confirming the importance of spirometry parameters and eosinophil count, along with the number of prior exacerbations and other clinical and demographic variables.
This study confirms the key prognostic value of eosinophiles in asthma, suggesting the usefulness of ML in defining biological pathways that can help plan personalized pharmacological and rehabilitation strategies.
控制不佳的哮喘的主要问题之一是需要进入急诊部（ED）。本研究采用机器学习（ML）方法，旨在确定需要住院治疗的严重哮喘恶化的主要预测因子。
在 ED 出院后 48 小时内，对哮喘加重的连续患者进行筛选以纳入研究。实施 k-均值聚类算法以评估不同表型的潜在区别。实施 K-最近邻（KNN）作为实例基算法和随机森林（RF）作为树基算法，以根据患者在过去 12 个月中至少有一次额外进入 ED 的情况对其进行分类。
为了训练我们的模型，我们纳入了 260 名患者（31.5%为男性，平均年龄 47.6 岁）。无监督 ML 根据嗜酸性粒细胞计数确定了两组。嗜酸性粒细胞≥370 个/µL 的患者显著更年长，疾病持续时间更长，日常生活受限更多，治疗率更低，而嗜酸性粒细胞<370 个/μL 的患者则相反。此外，他们报告了较低的预计 FEV（64.8±12.3% vs. 83.9±17.3%）和 FEV/FVC（71.3±9.3 vs. 78.5±6.8）值，每年哮喘加重的次数更多。在有监督 ML 中，KNN 在识别频繁加重者方面表现最佳（AUROC：96.7%），这证实了肺量计参数和嗜酸性粒细胞计数以及先前加重次数和其他临床及人口统计学变量的重要性。
本研究证实了嗜酸性粒细胞在哮喘中的关键预后价值，表明 ML 在定义可帮助制定个性化药物治疗和康复策略的生物学途径方面的有用性。
