The thrombodynamic ratio as a predictor of 28-day mortality in sepsis patients.

BACKGROUND: The thrombodynamic ratio (TDR) as a composite thromboelastography (TEG) parameter, has been proven to be valuable in multiple diseases. However, the association between TDR and mortality in sepsis has not been studied. METHODS: One hundred forty-one patients were enrolled in this retrospectively study. TEG was performed immediately at admission. Two cox proportional hazards models were developed for the prediction of 28-day mortality. The C statistic, continuous net reclassification index (cNRI) and integrated discriminatory index (IDI) were calculated to compare the discrimination performance of clinical models with and without the TDR value. The integrated calibration index (ICI) and E50 were calculated to compare the calibration. RESULTS: Patients with lower TDR were more likely to have organ impairments and increased 28-day mortality. The TDR value improved discrimination performance in both Model 1 (C statistic, 0.745 vs 0.735; cNRI 19.4%, p = 0.044; IDI 5.6%, p = 0.012) and Model 2 (C statistic, 0.761 vs 0.751; IDI, 5.1%, p = 0.012). Compared to the calibration curve of Model 1 without TDR, addition of TDR displayed better calibration (ICI, 0.023; E50, 0.021). CONCLUSION: TDR value significantly predicts 28-day mortality in patients with sepsis and could improve the discrimination and calibration performance of clinical prediction models.

An MSCT-based radiomics nomogram combined with clinical factors can identify Crohn's disease and ulcerative colitis.

BACKGROUND: We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly. METHODS: MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model. RESULTS: Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077). CONCLUSIONS: The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC.

Substantia Nigra Radiomics Feature Extraction of Parkinson's Disease Based on Magnitude Images of Susceptibility-Weighted Imaging.

BACKGROUND: It is reported that radiomic features extracted from quantitative susceptibility mapping (QSM) had promising clinical value for the diagnosis of Parkinson's disease (PD). We aimed to explore the usefulness of radiomics features based on magnitude images to distinguish PD from non-PD controls. METHODS: We retrospectively recruited PD patients and controls who underwent brain 3.0T MR including susceptibility-weighted imaging (SWI). A total of 396 radiomics features were extracted from the SN of 95 PD patients and 95 non-PD controls based on SWI. Intra-/inter-observer correlation coefficients (ICCs) were applied to measure the observer agreement for the radiomic feature extraction. Then the patients were randomly grouped into training and validation sets in a ratio of 7:3. In the training set, the maximum correlation minimum redundancy algorithm (mRMR) and the least absolute shrinkage and selection operator (LASSO) were conducted to filter and choose the optimized subset of features, and a radiomics signature was constructed. Moreover, radiomics signatures were constructed by different machine learning models. Area under the ROC curves (AUCs) were applied to evaluate the predictive performance of the models. Then correlation analysis was performed to evaluate the correlation between the optimized features and clinical factors. RESULTS: The intro-observer CC ranged from 0.82 to 1.0, and the inter-observer CC ranged from 0.77 to 0.99. The LASSO logistic regression model showed good prediction efficacy in the training set [AUC = 0.82, 95% confidence interval (CI, 0.74-0.88)] and the validation set [AUC = 0.81, 95% CI (0.68-0.91)]. One radiomic feature showed a moderate negative correlation with Hoehn-Yahr stage (r = -0.49, P = 0.012). CONCLUSION: Radiomic predictive features based on SWI magnitude images could reflect the Hoehn-Yahr stage of PD to some extent.

Morphology and signal changes of the lentiform nucleus based on susceptibility weighted imaging in parkinsonism-predominant multiple system atrophy.

INTRODUCTION: It remains challenging to make a differential diagnosis between atypical parkinsonism and Parkinson's disease (PD) from routine neuroimaging. This case-control study aimed to quantitatively investigate both morphological and signal intensity changes in susceptibility weighted imaging (SWI) of the lentiform nucleus (LN) for discriminating parkinsonism-predominant multiple system atrophy (MSA-P) from PD. METHODS: We retrospectively enrolled patients with MSA-P, PD, and sex- and age-matched controls between January 2016 and November 2019 at the Movement Disorder Center who underwent 3T MR imaging of brain with SWI sequence. Two specialists at the center reviewed the medical records and made the final diagnosis, and two experienced neuroradiologists performed MRI image analysis based on a defined radiological protocol to conduct the ROI-based morphological measurements of the LN and the signal intensity. RESULTS: A total of 19 patients with MSA-P, 19 patients with PD and 19 controls were enrolled in this study. We found that patients with MSA-P had significant decreases size in the short line (SL) and the ratio of the SL and the long line (SLLr) and increased value in the signal intensity standard deviation of the LN (SIsd_LN) compared with the patients with PD and with the controls (P < 0.05). Combining these three indexes, this finding had a sensitivity of 94.7% and a specificity of 63.2% to distinguish MSA-P from PD. CONCLUSION: As compared to PD and control subjects, the SA-P patients are characterized by narrowing morphology and the inhomogeneous signal intensity of the posterior region of LN. The quantitative morphological change is a possible potential marker to differentiate MSA-P from PD on SWI.