Clinical Features Associated with ‘Normal Range’ Fibrin D-Dimer Levels in Atrial Fibrillation Patients with Left Atrial Thrombus

Background Left atrial thrombus (LAT) often complicates with atrial fibrillation (AF). The evidence whether fibrin D-dimer levels could be used as a predictive biomarker for LAT is contradictory. This study firstly investigated the relationship between ‘normal range’ D-dimer and prevalent LAT. Second, we explored factors contributing to normal D-dimer levels in the presence of LAT. Methods We studied 244 AF patients with LAT (mean age: 59.9 years, SD:11.7; 53.3% female): of these, 103 (42.2%) had normal D-dimer, 25 (10.2%) had atrial thrombus exclusion score (ATE score) of 0 19 (16.7%) males had CHA 2 DS 2 -VASc score of 0, 21(16.2%) females had CHA 2 DS 2 -VASc score of 1 and 16 had overlapped ATE score of 0 and CHA 2 DS 2 -VASc score of 0 (N = 8 if male) or CHA 2 DS 2 -VASc score of 1(N = 8 if female). Using multivariate binary analysis, larger left atrial diameter (LAD; adjusted OR: 1.06, 1.03−1.10, p = 0.001) were associated with increased D-dimer. Patients with high body mass index (BMI), hypertension history and previous anticoagulation were more likely to show normal range D-dimer levels in the presence of LAT. Conclusions A high prevalence (42.2%) of ‘normal range’ D-dimer levels was found in AF patients with LAT, especially in those with hypertension, high BMI and prior anticoagulation. D-dimer levels of those patients with larger LAD were more likely to be increased

Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor

Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the light gradient boosting machine (LightGBM)-based cross-validation (the recursive elimination method) was used for feature selection. The LightGBM framework forecasting models with classification thresholds 0.7, 0.8, and 0.9 are established using stratified 5-fold cross-validation. Model performance was assessed using receiver operating characteristics, sensitivity, and specificity. Results: There were 16, 18, and 13 features selected for models 0.7, 0.8, and 0.9, respectively. Texture features are of great importance in external/internal correlation prediction compared to other features in all models. The sensitivities of the predictions in models 0.7, 0.8, and 0.9 were 0.800 ± 0.126, 0.829 ± 0.140, and 0.864 ± 0.086, respectively. The specificities were 0.771 ± 0.114, 0.936 ± 0.0581, and 0.839 ± 0.101, whereas the area under the curve (AUC) was 0.837, 0.946, and 0.877, respectively. Conclusions: Our findings indicate that radiomics is an effective tool for respiratory motion correlation prediction, which can extract tumor motion characteristics. We proposed a machine learning framework for correlation prediction in the motion management strategy for lung tumor patients

The age, NT-proBNP, and Ejection Fraction Score as a Novel Predictor of Clinical Outcomes in CAD Patients After PCI

Background Previous evidences have been proved that age, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and ejection fraction are tightly associated with the long-term outcomes in patients suffered from coronary artery disease (CAD). Therefore, the present study aimed to assess the prognosis value of age, NT-proBNP, and ejection fraction (ABEF) score in CAD patients who underwent percutaneous coronary intervention (PCI). Methods Observational cohort methodology was used in this study which enrolled totally 3561 patients. And the patients were followed up regularly for 37.59 ± 22.24 months. Patients were classed into three groups based on the tertiles of ABEF sore: first tertile ( 524pg/mL is 3). The association between ABEF score and adverse prognosis, including all-cause death (ACD), cardiac death (CD), major adverse cardiovascular events (MACEs) and major adverse cardiac and cerebrovascular events (MACCEs), in patients who underwent PCI was analyzed. Results According to the risk category of ABEF score, the incidences of ACD ( P  < .001), CD ( P  < .001) and MACCEs ( P  = .021) among the three groups showed significant differences. Multivariate Cox regression analysis suggested that the respective risks of ACD and CD were increased 3.013 folds (hazard risk [HR] = 4.013 [95% confidence interval [CI]: 1.922-8.378], P  < .001) and 4.922 folds ([HR] = 5.922 [95% [CI]: 2.253-15.566], P  < .001) in the third tertile compared with those in the first tertile. Kaplan-Meier survival analyses showed that the cumulative risks of ACD,CD and MACCEs in patients with the high ABEF score tended to increase. Conclusion The present study indicated ABEF score was a novel biomarker suitable for predicting adverse prognosis in patients after PCI, which may be used for early recognition and risk stratification