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 hem...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-01-01
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| Series: | Advances in Biomarker Sciences and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2543106425000055 |
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| author | Jun Wang Xuefeng He Feng Chen Xiao Ma Daxiong Zeng Junhong Jiang |
| author_facet | Jun Wang Xuefeng He Feng Chen Xiao Ma Daxiong Zeng Junhong Jiang |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c87bd388737549798a20b9bd2b237885 |
| institution | DOAJ |
| issn | 2543-1064 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Advances in Biomarker Sciences and Technology |
| spelling | doaj-art-c87bd388737549798a20b9bd2b2378852025-08-20T02:45:07ZengKeAi Communications Co. Ltd.Advances in Biomarker Sciences and Technology2543-10642025-01-017869410.1016/j.abst.2025.02.001Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disordersJun Wang0Xuefeng He1Feng Chen2Xiao Ma3Daxiong Zeng4Junhong Jiang5Department of Pulmonary and Critical Care Medicine, The Fourth Affiliated Hospital of Soochow University, Chongwen Road, Suzhou, China; Department of Respiratory, Soochow Hopes Hematology Hospital, Wudong Road, Suzhou, ChinaDepartment of Hematology, The First Affiliated Hospital of Soochow University, Pinghai Road, Suzhou, ChinaDepartment of Hematology, The First Affiliated Hospital of Soochow University, Pinghai Road, Suzhou, ChinaDepartment of Hematology, The First Affiliated Hospital of Soochow University, Pinghai Road, Suzhou, ChinaDepartment of Pulmonary and Critical Care Medicine, The Fourth Affiliated Hospital of Soochow University, Chongwen Road, Suzhou, China; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Soochow University, Pinghai Road, Suzhou, ChinaDepartment of Pulmonary and Critical Care Medicine, The Fourth Affiliated Hospital of Soochow University, Chongwen Road, Suzhou, China; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Soochow University, Pinghai Road, Suzhou, China; Corresponding author. Department of Pulmonary and Critical Care Medicine, The Fourth Affiliated Hospital of Soochow University, Chongwen Road, Suzhou, China.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.http://www.sciencedirect.com/science/article/pii/S2543106425000055Invasive pulmonary fungal infectionsHematologic disordersClinical featuresPredictive modelTreatment failure |
| spellingShingle | Jun Wang Xuefeng He Feng Chen Xiao Ma Daxiong Zeng Junhong Jiang Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders Advances in Biomarker Sciences and Technology Invasive pulmonary fungal infections Hematologic disorders Clinical features Predictive model Treatment failure |
| title | Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders |
| title_full | Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders |
| title_fullStr | Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders |
| title_full_unstemmed | Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders |
| title_short | Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders |
| title_sort | clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders |
| topic | Invasive pulmonary fungal infections Hematologic disorders Clinical features Predictive model Treatment failure |
| url | http://www.sciencedirect.com/science/article/pii/S2543106425000055 |
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