血液病患者侵袭性肺真菌感染的临床特征和预测模型

Jun Wang , Xuefeng He , Feng Chen , Xiao Ma , Daxiong Zeng , Junhong Jiang
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引用次数: 0

摘要

目的探讨血液学疾病合并侵袭性肺部真菌感染的临床特点及影响治疗效果的因素,建立预测模型。方法收集2020年1月至2023年6月血液病和侵袭性肺部真菌感染患者的临床资料。基于支气管肺泡灌洗液(BALF)的宏基因组学下一代测序(mNGS),将患者分为三组:念珠菌、毛霉菌和曲霉。比较一般情况、临床特征、治疗和结果。治疗后两个月评估治疗结果,并将其分为改善或未改善。分析影响疗效的因素,建立治疗失败的风险预测模型。结果共纳入血液病合并肺部侵袭性真菌感染患者89例,其中念珠菌26例,毛霉25例,曲霉38例。两组间在长期使用皮质类固醇、血液学疾病结局、中性粒细胞减少持续时间、治疗持续时间、中心静脉置管、半乳甘露聚糖(GM)检测结果、CD4+ t细胞计数和临床表现方面均存在显著差异。抗真菌治疗2个月后,念珠菌、毛霉和曲霉菌的治愈率分别为96.15%、76.00%和63.16%。Logistic回归分析发现血小板计数升高(OR = 0.9823, 95%CI: 0.9663-0.9945)、d -二聚体(OR = 1.2130, 95%CI: 1.0544-1.4934)、c -反应蛋白(OR = 1.0066, 95%CI: 1.0026-1.0111)和基于mNGS结果的药物调整(OR = 0.0495, 95%CI: 0.0108-0.1624)是重要的预后因素。基于这些因素的nomogram预测模型具有良好的判别能力,C-index为0.86。结论血液病合并肺部侵袭性真菌感染患者不同类型真菌的临床特点及治疗效果不同。nomogram预测模型,结合血小板计数、d -二聚体、c -反应蛋白和mngs引导的治疗调整,对两个月的治疗结果提供了可靠的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders

Objective

This study was to investigate the clinical features of hematological disorders complicated by invasive pulmonary fungal infections and identify factors affecting treatment outcomes, with the aim of developing a predictive model.

Methods

Clinical data were collected from patients with hematological disorders and invasive pulmonary fungal infections between January 2020 and June 2023. Based on metagenomics next generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF), patients were categorized into three groups: Candida, Mucor, and Aspergillus. General conditions, clinical features, treatments, and outcomes were compared. Treatment outcomes were assessed two months after therapy and classified as either improved or not improved. Factors influencing outcomes were analyzed, and a risk prediction model for treatment failure was developed.

Results

A total of 89 patients with hematological diseases and invasive pulmonary fungal infections were included: 26 with Candida, 25 with Mucor, and 38 with Aspergillus. Significant differences were observed between groups in long-term corticosteroid use, hematological disease outcomes, neutropenia duration, treatment duration, central venous catheter placement, galactomannan (GM) test results, CD4+ T-cell count, and clinical manifestations. After two months of antifungal therapy, improvement rates were 96.15 % for Candida, 76.00 % for Mucor, and 63.16 % for Aspergillus. Logistic regression analysis identified elevated platelet count (OR = 0.9823, 95%CI: 0.9663–0.9945), D-dimer (OR = 1.2130, 95%CI: 1.0544–1.4934), C-reactive protein (OR = 1.0066, 95%CI: 1.0026–1.0111) and medication adjustments based on mNGS results (OR = 0.0495, 95%CI: 0.0108–0.1624) as significant prognostic factors. A nomogram prediction model based on these factors demonstrated good discrimination with a C-index of 0.86.

Conclusion

The clinical features and treatment outcomes differ among fungal types in patients with hematological disorders and invasive pulmonary fungal infections. The nomogram prediction model, incorporating platelet count, D-dimer, C-reactive protein and mNGS-guided therapy adjustments, offers robust predictive performance for two-month treatment outcomes.
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Advances in biomarker sciences and technology
Advances in biomarker sciences and technology Biotechnology, Clinical Biochemistry, Molecular Medicine, Public Health and Health Policy
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