Fresi Eleonora, Pagani Elisabetta, Pezzetti Federica, Montomoli Cristina, Monti Cristina, Betti Monia, De Silvestri Annalisa, Sagliocco Orlando, Zuccaro Valentina, Bruno Raffaele, Klersy Catherine
Biostatistics & Clinical Trial Center, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy.
Infectious Diseases Unit, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy.
J Clin Med. 2025 May 23;14(11):3670. doi: 10.3390/jcm14113670.
Long COVID can develop in individuals who have had COVID-19, regardless of the severity of their initial infection or the treatment they received. Several studies have examined the prevalence and manifestation of symptom phenotypes to comprehend the pathophysiological mechanisms associated with these symptoms. Numerous articles outlined specific approaches for multidisciplinary management and treatment of these patients, focusing primarily on those with mild acute illness. The various management models implemented focused on a patient-centered approach, where the specialists were positioned around the patient. On the other hand, the created pathways do not consider the possibility of symptom clusters when determining how to define diagnostic algorithms. This retrospective longitudinal study took place at the "Fondazione IRCCS Policlinico San Matteo", Pavia, Italy (SMATTEO) and at the "Ospedale di Cremona", ASST Cremona, Italy (CREMONA). Information was retrieved from the administrative data warehouse and from two dedicated registries. We included patients discharged with a diagnosis of severe COVID-19, systematically invited for a 3-month follow-up visit. Unsupervised machine learning was used to identify potential patient phenotypes. Three hundred and eighty-two patients were included in these analyses. About one-third of patients were older than 65 years; a quarter were female; more than 80% of patients had multi-morbidities. Diagnoses related to the circulatory system were the most frequent, comprising 46% of cases, followed by endocrinopathies at 20%. PCA (principal component analysis) had no clustering tendency, which was comparable to the PCA plot of a random dataset. The unsupervised machine learning approach confirms these findings. Indeed, while dendrograms for the hierarchical clustering approach may visually indicate some clusters, this is not the case for the PAM method. Notably, most patients were concentrated in one cluster. The extreme heterogeneity of patients affected by post-acute sequelae of SARS-CoV-2 infection (PASC) has not allowed for the identification of specific symptom clusters with the most recent statistical techniques, thus preventing the generation of common diagnostic-therapeutic pathways.
长期新冠可发生于感染过新冠病毒的个体，无论其初始感染的严重程度或接受的治疗如何。多项研究调查了症状表型的患病率和表现，以了解与这些症状相关的病理生理机制。众多文章概述了对这些患者进行多学科管理和治疗的具体方法，主要针对轻度急性疾病患者。实施的各种管理模式侧重于以患者为中心的方法，专家围绕患者开展工作。另一方面，所创建的路径在确定如何定义诊断算法时未考虑症状群的可能性。这项回顾性纵向研究在意大利帕维亚的“圣马泰奥综合医院基金会”（SMATTEO）和意大利克雷莫纳的“克雷莫纳医院”（CREMONA）进行。信息从行政数据仓库和两个专门的登记处获取。我们纳入了诊断为重症新冠且被系统邀请进行3个月随访的出院患者。使用无监督机器学习来识别潜在的患者表型。这些分析纳入了382名患者。约三分之一的患者年龄超过65岁；四分之一为女性；超过80%的患者患有多种疾病。与循环系统相关的诊断最为常见，占病例的46%，其次是内分泌疾病，占20%。主成分分析（PCA）没有聚类倾向，这与随机数据集的PCA图相当。无监督机器学习方法证实了这些发现。事实上，虽然层次聚类方法的树状图可能在视觉上显示出一些聚类，但对于PAM方法并非如此。值得注意的是，大多数患者集中在一个聚类中。新型冠状病毒感染后急性后遗症（PASC）患者的极端异质性使得无法用最新的统计技术识别特定的症状群，从而阻碍了通用诊断治疗路径的生成。
