Objective:To investigate the clinical characteristics and risk factors for disease progression after treatment in patients with post-tuberculosis chronic pulmonary aspergillosis (post-TB CPA).Methods:A retrospective cohort study was conducted on post-TB CPA patients admitted to Hangzhou Red Cross Hospital between January 2020 and December 2023. The demographic manifestation, clinical manifestation, laboratory indicators, imaging findings, and treatment strategies were collected. Patients were divided into progression group and non-progression group based on treatment outcomes, and the clinical data of the two groups were compared. Chi-square test was used for univariate analysis, and multivariate logistic regression were used to identify independent risk factors for disease progression after treatment.Results:A total of 109 post-TB CPA patients were included, and 33.9%(37/109) were in the progression group and 66.1%(72/109) in the non-progression group. Multivariate logistic regression revealed that subacute invasive aspergillosis (SAIA) (odds ratio ( OR)=14.356, 95% confidence interval ( CI) 2.923 to 70.504, P=0.001), elevated erythrocyte sedimentation rate (ESR) ( OR=5.276, 95% CI 1.505 to 18.491, P=0.009), and pulmonary fibrosis ( OR=5.030, 95% CI 1.437 to 17.612, P=0.012) were independent risk factors for disease progression. Antifungal treatment for ≥3 months was associated with a lower risk of disease progression ( OR=0.038, 95% CI 0.003 to 0.431, P=0.008). The proportion of non-progression group receiving surgical treatment was higher than that of progression group with statistical significance (31.9%(23/72) vs 5.4% (2/37), χ2=8.30, P=0.004), but the protective effect of surgery on disease progression was not confirmed by multivariate analysis ( OR=0.735, 95% CI 0.132 to 4.080, P=0.724). Conclusions:Disease progression in patients with post-TB CPA is strongly associated with SAIA, elevated ESR, and pulmonary fibrosis. Standardized anti-fungal treatment for ≥3 months significantly improves the prognosis.

Objective:To investigate the clinical characteristics and risk factors for disease progression after treatment in patients with post-tuberculosis chronic pulmonary aspergillosis (post-TB CPA).Methods:A retrospective cohort study was conducted on post-TB CPA patients admitted to Hangzhou Red Cross Hospital between January 2020 and December 2023. The demographic manifestation, clinical manifestation, laboratory indicators, imaging findings, and treatment strategies were collected. Patients were divided into progression group and non-progression group based on treatment outcomes, and the clinical data of the two groups were compared. Chi-square test was used for univariate analysis, and multivariate logistic regression were used to identify independent risk factors for disease progression after treatment.Results:A total of 109 post-TB CPA patients were included, and 33.9%(37/109) were in the progression group and 66.1%(72/109) in the non-progression group. Multivariate logistic regression revealed that subacute invasive aspergillosis (SAIA) (odds ratio ( OR)=14.356, 95% confidence interval ( CI) 2.923 to 70.504, P=0.001), elevated erythrocyte sedimentation rate (ESR) ( OR=5.276, 95% CI 1.505 to 18.491, P=0.009), and pulmonary fibrosis ( OR=5.030, 95% CI 1.437 to 17.612, P=0.012) were independent risk factors for disease progression. Antifungal treatment for ≥3 months was associated with a lower risk of disease progression ( OR=0.038, 95% CI 0.003 to 0.431, P=0.008). The proportion of non-progression group receiving surgical treatment was higher than that of progression group with statistical significance (31.9%(23/72) vs 5.4% (2/37), χ2=8.30, P=0.004), but the protective effect of surgery on disease progression was not confirmed by multivariate analysis ( OR=0.735, 95% CI 0.132 to 4.080, P=0.724). Conclusions:Disease progression in patients with post-TB CPA is strongly associated with SAIA, elevated ESR, and pulmonary fibrosis. Standardized anti-fungal treatment for ≥3 months significantly improves the prognosis.

Objective:To explore the diagnostic value and problems of artificial intelligence (AI) bone marrow cell recognition technology in the detection of minimal residual disease (MRD) of leukemia.Methods:A total of 65 cases with minimal residual disease of leukemia confirmed by flow cytometry from the Hematology Medical Center of Xinqiao Hospital affiliated to the Army Medical University (AMMU) from November 1 to December 31, 2020 were collected. The bone marrow Wright′s staining smears were obtained, and all bone marrow smears were scanned and classified automatically without artificial intervention by the analysis system based on Artificial Intelligence platform (morphogo). AI-MRD was defined to positive when the proportion of primary cells was more than 3%. According to the number of AI automatic recognition cells, the cases were divided into 18 cases of less than 500 (L500), 35 cases of 500 to 1900 (between 500 and 1900, B1900), and 12 cases of more than 1900 (M1900), no overlap or omission between groups. Kappa consistency test was performed on the results of artificial intelligence test and the results of flow cytometry for minimal residual disease of leukemia (MFC-MRD) in each group. The receiver operating characteristic curve (ROC) of the artificial intelligence test results of each group of patients was drawn based on the MFC-MRD results, and the sensitivity, specificity and accuracy of the area under the curve (AUC) value and AI results were calculated.Results:After grouping according to the number of cells automatically recognized by AI, the detection results of L500 group were MFC-MRD+/AI-MRD+7 cases, MFC-MRD+/AI-MFC-2 cases, MFC-MRD-/AI-MRD+6 cases, MFC-MRD-/AI-MRD-3 cases; In B1900 group, MFC-MRD+/AI-MRD+13 cases, MFC-MRD+/AI-MFC-6 cases, MFC-MRD-/AI-MRD+6 cases, MFC-MRD-/AI-MRD-10 cases; The results of M1900 group were MFC-MRD+/AI-MRD+5 cases, MFC-MRD+/AI-MFC-0 cases, MFC-MRD-/AI-MRD+1 case, MFC-MRD-/AI-MRD-6 cases. Taking MFC-MRD as the determination standard, the sensitivity of AI-MRD detection in L500 group, B1900 group and M1900 group was 53.8%, 68.4% and 83.3%, the specificity was 60%, 62.5% and 100%, the accuracy was 55.6%, 65.7% and 91.7%, and the AUC value were 0.568 P=0.654, 0.678 P=0.069,1.000 P=0.000. Conclusions:This study preliminarily explored the diagnostic value and problems of AI bone marrow cell recognition in the detection of minimal residual disease of leukemia. It was confirmed that when 3% of the proportion of blasts in AI cell classification is set>3% as the positive threshold of AI-MRD, the consistency between AI and MFC-MRD detection increases with the increase of the number of cells recognized by AI.