PDF(Pubmed)
Abstract：
UNASSIGNED: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators.
UNASSIGNED: A retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients.
UNASSIGNED: A total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively.
UNASSIGNED: CAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability.
UNASSIGNED: A retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients.
UNASSIGNED: A total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively.
UNASSIGNED: CAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability.
摘要：
■住院患者的社区获得性肺炎（CAP）的临床表现表现出异质性。炎症和免疫反应在CAP发育中起重要作用。然而,对CAP患者免疫表型的研究有限，很少有机器学习(ML)模型分析免疫指标。
■在新华医院进行了一项回顾性队列研究，隶属于上海交通大学。纳入符合预定义标准的患者，并使用无监督聚类来鉴定表型。还比较了具有不同表型的患者的不同结局。通过机器学习方法，我们全面评估CAP患者的疾病严重程度.
■本研究共纳入了1156例CAP患者。在训练组(n=809)中，我们在患者中确定了三种免疫表型：表型A（42.0%），表型B（40.2%），和表型C（17.8%），表型C对应于更严重的疾病。在验证队列中可以观察到类似的结果。最佳预后模型，SuperPC,达到最高的平均C指数0.859。为了预测CAP严重程度，随机森林模型精度高，训练和验证队列中的C指数为0.998和0.794，分别。
■CAP患者可以分为三种不同的免疫表型，每个都具有预后相关性。通过利用临床免疫学数据，机器学习在预测CAP患者的死亡率和疾病严重程度方面具有潜力。进一步的外部验证研究对于确认适用性至关重要。
■在新华医院进行了一项回顾性队列研究，隶属于上海交通大学。纳入符合预定义标准的患者，并使用无监督聚类来鉴定表型。还比较了具有不同表型的患者的不同结局。通过机器学习方法，我们全面评估CAP患者的疾病严重程度.
■本研究共纳入了1156例CAP患者。在训练组(n=809)中，我们在患者中确定了三种免疫表型：表型A（42.0%），表型B（40.2%），和表型C（17.8%），表型C对应于更严重的疾病。在验证队列中可以观察到类似的结果。最佳预后模型，SuperPC,达到最高的平均C指数0.859。为了预测CAP严重程度，随机森林模型精度高，训练和验证队列中的C指数为0.998和0.794，分别。
■CAP患者可以分为三种不同的免疫表型，每个都具有预后相关性。通过利用临床免疫学数据，机器学习在预测CAP患者的死亡率和疾病严重程度方面具有潜力。进一步的外部验证研究对于确认适用性至关重要。
