Objective:To investigate the impact of coronary angiography-derived index of microcirculatory resistance (caIMR) on the long-term prognosis of patients with coronary heart disease (CHD) undergoing elective percutaneous coronary intervention (PCI).Methods:The study was a retrospective cohort study conducted at a single centre. Patients who successfully underwent elective PCI with pre-and post-PCI caIMR measurements in Peking University First Hospital between August 2013 and December 2020 were included. Then patients were categorised into three groups based on pre-and post-PCI caIMR: post-PCI caIMR<25 U group, pre-PCI caIMR<25 U and post-PCI caIMR≥25 U group, and both pre-and post-PCI caIMR≥25 U group. Collected clinical data of patients, including comorbid diabetes mellitus.The primary endpoint was a composite endpoint, defined as a composite of all-cause death, non-fatal myocardial infarction, and any revascularisation. The association between caIMR-based groupings and clinical outcomes was analysed using Cox proportional hazards regression models.Results:A total of 625 patients who underwent successful elective PCI were included in the study, among whom 294 (47.0%) had stable angina. The age was (64.5±10.1) years, and 440 (70.4%) patients were male. Over a median follow-up of 3.69 (1.80, 5.80) years, 122 patients (19.5%) experienced composite endpoint events. Post-PCI caIMR≥25 U in combination with diabetes mellitus was associated with an increased risk of the composite endpoint compared to those with post-PCI caIMR<25 U and without diabetes mellitus (adjusted HR=2.13, 95% CI 1.17-3.88, P=0.014). In the combined analysis, compared with post-PCI caIMR<25 U group, those with both pre-and post-PCI caIMR≥25 U had higher risks of composite endpoint (adjusted HR=2.01, 95% CI 1.18-3.43, P=0.010) and any revascularisation (adjusted HR=2.12, 95% CI 1.17-3.84, P=0.013). The pre-PCI caIMR<25 U and post-PCI caIMR≥25 U group showed no statistically significant differences in any of the endpoints compared to post-PCI caIMR<25 U group. Conclusions:Integrated pre-and post-procedural assessment of caIMR may enhance risk stratification in patients with coronary heart disease. Persistent coronary microvascular dysfunction present both before and after PCI, as measured by caIMR, serves as an independent risk factor for adverse events in patients with coronary heart disease undergoing elective PCI.

Objective:To investigate the impact of coronary angiography-derived index of microcirculatory resistance (caIMR) on the long-term prognosis of patients with coronary heart disease (CHD) undergoing elective percutaneous coronary intervention (PCI).Methods:The study was a retrospective cohort study conducted at a single centre. Patients who successfully underwent elective PCI with pre-and post-PCI caIMR measurements in Peking University First Hospital between August 2013 and December 2020 were included. Then patients were categorised into three groups based on pre-and post-PCI caIMR: post-PCI caIMR<25 U group, pre-PCI caIMR<25 U and post-PCI caIMR≥25 U group, and both pre-and post-PCI caIMR≥25 U group. Collected clinical data of patients, including comorbid diabetes mellitus.The primary endpoint was a composite endpoint, defined as a composite of all-cause death, non-fatal myocardial infarction, and any revascularisation. The association between caIMR-based groupings and clinical outcomes was analysed using Cox proportional hazards regression models.Results:A total of 625 patients who underwent successful elective PCI were included in the study, among whom 294 (47.0%) had stable angina. The age was (64.5±10.1) years, and 440 (70.4%) patients were male. Over a median follow-up of 3.69 (1.80, 5.80) years, 122 patients (19.5%) experienced composite endpoint events. Post-PCI caIMR≥25 U in combination with diabetes mellitus was associated with an increased risk of the composite endpoint compared to those with post-PCI caIMR<25 U and without diabetes mellitus (adjusted HR=2.13, 95% CI 1.17-3.88, P=0.014). In the combined analysis, compared with post-PCI caIMR<25 U group, those with both pre-and post-PCI caIMR≥25 U had higher risks of composite endpoint (adjusted HR=2.01, 95% CI 1.18-3.43, P=0.010) and any revascularisation (adjusted HR=2.12, 95% CI 1.17-3.84, P=0.013). The pre-PCI caIMR<25 U and post-PCI caIMR≥25 U group showed no statistically significant differences in any of the endpoints compared to post-PCI caIMR<25 U group. Conclusions:Integrated pre-and post-procedural assessment of caIMR may enhance risk stratification in patients with coronary heart disease. Persistent coronary microvascular dysfunction present both before and after PCI, as measured by caIMR, serves as an independent risk factor for adverse events in patients with coronary heart disease undergoing elective PCI.

Objective: To establish a CT image radiomics-based prediction model for the differential diagnosis of silicosis and tuberculosis nodules. Methods: A total of 53 patients with silicosis and 89 patients with tuberculosis who underwent routine CT scans in Suzhou Fifth People's Hospital from January to August, 2018 were enrolled in this study. AK/ITK software was used to segment the images to obtain 139 silicosis lesions and 119 tuberculosis lesions. For each lesion image, 396 features were extracted, and feature dimension reduction was applied to select the most characteristic feature subset. Support vector machine (SVM) , feedforward back propagation neural network (FNN-BP) , and random forest (RF) were implemented using R software (Rstudio V1.1.463) , and the algorithm that achieved the largest area under of the receiver operating characteristic (ROC) curve (AUC) was selected as the final prediction model. Results: RF was the best prediction model for the differential diagnosis of silicosis and tuberculosis nodules, with an accuracy of 83.1%, a sensitivity of 0.76, a specificity of 0.9, and an AUC of 0.917 (95% confidence interval: 0.8431-0.9758) . RF had a significantly larger AUC than SVM and FNN-BP (P<0.05) . Conclusion CT image-based RF prediction model can be used to differentially diagnose silicosis and tuberculosis nodules.