Clinical Research on Microecological Landscape for Infection Risk Stratification in Newly Diagnosed Patients with Hematological Conditions

作者：彭苗新, Yueyi Xu, Yueyi Xu, Xuefang Cao, Yizhe Xue, Jie Pang, Shiyuan Zhou, Peipei Xu, Yonggong Yang, Xiaoping Zhang, Jun Qian, Yang Wang, Xuzhang Lu, Yan Wan, Yu Sun, Xiaoying Hua, Xu Yan, Xu Yan, Bing Chen, Jian Ouyang · 发表于：Infectious Diseases and Therapy · 年份：2026 · DOI：10.1007/s40121-026-01401-9 · 研究领域：Neutropenia and Cancer Infections、Gut microbiota and health、Bacterial Identification and Susceptibility Testing

INTRODUCTION: Infection is a common and potentially fatal complication during the treatment of hematological diseases, particularly in the context of chemotherapy-induced immunosuppression. The nonselective use of antibiotic prophylaxis in patients with neutropenia in China has persistently accelerated antimicrobial resistance. Early identification of patients at high risk for infection before clinical symptom onset could enable targeted preventive strategies; however, reliable and biologically informed screening approaches remain limited. METHODS: We developed a prediction model for infection risk stratification in newly diagnosed patients with hematological conditions. Plasma metagenomic next-generation sequencing was performed in a prospective cohort of 230 patients. Among them, 116 patients provided prechemotherapy, non-neutropenic plasma samples (cohort A), and 114 patients provided postchemotherapy, neutropenic samples (cohort B). Microbial community profiles were analyzed, and machine learning approaches were applied to construct classifiers for neutropenia status and subsequent infection risk. RESULTS: Plasma metagenomic profiling revealed a complex microecological landscape in patients with hematological conditions and identified distinct microbial features associated with neutropenia. A trained random forest classifier successfully distinguished patients without neutropenia from patients with neutropenia, achieving an area under the receiver operating characteristic...