Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.

PURPOSE: To explore the predictive value of radiomics in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients and construct a prediction model based on clinical features and DWI-MRI radiomics features. METHODS: Univariate and multivariate logistic regression analyses were used to identify the independent clinical predictors for SAP. Pearson correlation analysis and the least absolute shrinkage and selection operator with ten-fold cross-validation were used to calculate the radiomics score for each feature and identify the predictive radiomics features for SAP. Multivariate logistic regression was used to combine the predictive radiomics features with the independent clinical predictors. The prediction performance of the SAP models was evaluated using receiver operating characteristics (ROC), calibration curves, decision curve analysis, and subgroup analyses. RESULTS: Triglycerides, the neutrophil-to-lymphocyte ratio, dysphagia, the National Institutes of Health Stroke Scale (NIHSS) score, and internal carotid artery stenosis were identified as clinically independent risk factors for SAP. The radiomics scores in patients with SAP were generally higher than in patients without SAP (P < 0. 05). There was a linear positive correlation between radiomics scores and NIHSS scores, as well as between radiomics scores and infarct volume. Infarct volume showed moderate performance in predicting the occurrence of SAP, with an AUC of 0.635. When compared with the other models, the combined prediction model achieved the best area under the ROC (AUC) in both training (AUC = 0.859, 95% CI 0.759-0.936) and validation (AUC = 0.830, 95% CI 0.758-0.896) cohorts (P < 0.05). The calibration curves and decision curve analysis further confirmed the clinical value of the nomogram. Subgroup analysis showed that this nomogram had potential generalization ability. CONCLUSION: The addition of the radiomics features to the clinical model improved the prediction of SAP in AIS patients, which verified its feasibility.

Adverse childhood experiences, marital status and depressive symptoms in later life among the Chinese middle-aged and older adults : the mediating role of marital status.

BACKGROUND: Many studies have shown that adverse childhood experiences (ACEs) lead to adverse social relations in middle-aged and older adults and harm physical and mental health, but few studies have focused on the impact of ACEs on marital status in middle-aged and older adults and the potential influence of marital status between ACEs and depressive symptoms. PURPOSE: This study aimed to analyze the effect of ACEs on marital status and depressive symptoms in the Chinese middle-aged and older adults, and to explore the mediating role of marital status in the association between ACEs and depressive symptoms in middle-aged and older adults. METHOD: This study used the China Health and Retirement Longitudinal Study (CHARLS) 2014 life history survey and 2015 and 2018 follow-up data to analyze, ten ACEs conditions and marital status were collected by questionnaire, using the Center for Epidemiological Studies Depression Scale (CESD-10) 10-item short form to assess depressive symptoms. The association between cumulative ACEs and marital status was assessed by constructing a multinomial logistic regression (MLR) model, as well as a binary logistic regression model to assess the association between ACEs and depressive symptoms. The mediating role of marital status in the association between ACEs and depressive symptoms was also assessed. RESULTS: A total of 10,246 individuals aged 45 years or older were included in the analysis. Compared to individuals who did not experience ACEs, those who experienced two or more ACEs had a higher risk of being unmarried (seperated/divorced/never married) (OR = 1.67, 95% CI=[1.10,2.51]) and a higher risk of depressive symptoms (OR = 1.66, 95% CI=[1.49,1.84]) in middle and old age. Unmarried status partially mediated the association of ACEs with depressive symptoms. CONCLUSION: Chinese middle-aged and older people who experienced two or more ACEs have higher risks of unmarried status and depressive symptoms, and unmarried status partially mediated the ACEs-depressive symptom association. These findings reveal the fact that we need to develop life-cycle public health strategies to reduce exposure to ACEs and society should give more attention to the marital status of older people, thereby reducing the risk of depression among middle-aged and older adults in China.

A retrospective cohort study of coagulation function in patients with liver cirrhosis receiving cefoperazone/sulbactam with and without vitamin K1 supplementation.

BACKGROUND: Cefoperazone/sulbactam is commonly prescribed for the treatment of infected patients with cirrhosis. AIM: To investigate the effect of cefoperazone/sulbactam on coagulation in cirrhotic patients and assess the effectiveness of vitamin K1 supplementation in preventing cefoperazone/sulbactam-induced coagulation disorders. METHOD: This retrospective cohort study compared coagulation function in 217 cirrhotic patients who received cefoperazone/sulbactam with and without vitamin K1 supplementation (vitamin K1 group, n = 108; non-vitamin K1 group, n = 109). Propensity score matching (PSM) was used to to reduce confounders' influence, the SHapley additive exPlanations (SHAP) model to explore the importance of each variable in coagulation disorders. RESULTS: In the non-vitamin K1 group, the post-treatment prothrombin time (PT) was 16.5 ± 6.5 s and the activated partial thromboplastin time (aPTT) was 34.8 ± 9.4 s. These were significantly higher than pre-treatment values (PT: 14.6 ± 2.4 s, p = 0.005; aPTT: 30.4 ± 5.9 s, p < 0.001). In the vitamin K1 group, no differences were observed in PT, thrombin time, or platelet count, except for a slightly elevated post-treatment aPTT (37.0 ± 10.4 s) compared to that of pre-treatment (34.4 ± 7.2 s, p = 0.033). The vitamin K1 group exhibited a lower risk of PT prolongation (OR: 0.211, 95% CI: 0.047-0.678) and coagulation disorders (OR: 0.257, 95% CI: 0.126-0.499) compared to that of the non-vitamin K1 group. Propensity score matching analysis confirmed a reduced risk in the vitamin K1 group for prolonged PT (OR: 0.128, 95% CI: 0.007-0.754) and coagulation disorders (OR: 0.222, 95% CI: 0.076-0.575). Additionally, the vitamin K1 group exhibited lower incidences of PT prolongation, aPTT prolongation, bleeding, and coagulation dysfunction compared to the non-vitamin K1 group. CONCLUSION: Cefoperazone/sulbactam use may be linked to a higher risk of PT prolongation and coagulation disorders in cirrhotic patients. Prophylactic use of vitamin K1 can effectively reduce the risk.

Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China.

INTRODUCTION: Stroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS). METHODS AND ANALYSIS: A total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will continue until the required sample size is reached, and we will recruit as many participants as possible. Multiple indicators including basic clinical data, image data, laboratory data, CYP2C19 genotype and follow-up data will be assessed at various time points during the registry, including baseline, 24 hours, 7 days, 1 month, 3 months, 6 months, 9 months and 12 months. The primary outcome was stroke recurrence. The secondary outcomes were death events, prognosis scores and adverse events. Imaging images were processed using deep learning algorithms to construct a programme capable of automatically labelling the lesion area and extracting radiomics features. The machine learning algorithms will be applied to integrate cross-scale, multidimensional data for exploratory analysis. Then, an ischaemic stroke recurrence prediction model of the best performance for patients with AIS will be established. Calibration, receiver operating characteristic and decision curve analyses will be evaluated. ETHICS AND DISSEMINATION: This study has received ethical approval from the Medical Ethics Committee of the Second Affiliated Hospital of Nanchang University (medical research review No.34/2021), and informed consent will be obtained voluntarily. The research findings will be disseminated through publication in journals and presented at conferences. TRIAL REGISTRATION NUMBER: ChiCTR2200055209.

Comparison of the Predictive Value of Inflammatory Biomarkers for the Risk of Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke.

Purpose: To investigate the predictive value of various inflammatory biomarkers in patients with acute ischemic stroke (AIS) and evaluate the relationship between stroke-associated pneumonia (SAP) and the best predictive index. Patients and Methods: We calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS), and prognostic index (PI). Variables were selectively included in the logistic regression analysis to explore the associations of NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI with SAP. We assessed the predictive performance of biomarkers by analyzing receiver operating characteristic (ROC) curves. We further used restricted cubic splines (RCS) to investigate the association. Next, we conducted subgroup analyses to investigate whether specific populations were more susceptible to NLR. Results: NLR, PLR, MLR, SIRI, SII, GPS, mGPS, and PI increased significantly in SAP patients, and PNI was significantly decreased. After adjustment for potential confounders, the association of inflammatory biomarkers with SAP persisted. NLR showed the most favorable discriminative performance and was an independent risk factor predicting SAP. The RCS showed an increasing nonlinear trend of SAP risk with increasing NLR. The AUC of the combined indicator of NLR and C-reactive protein (CRP) was significantly higher than those of NLR and CRP alone (DeLong test, P<0.001). Subgroup analyses suggested good generalizability of the predictive effect. Conclusion: NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI can predict the occurrence of SAP. Among the indices, the NLR was the best predictor of SAP occurrence. It can therefore be used for the early identification of SAP.

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics.

Purpose: This study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS). Methods: The MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models. Results: Twenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively. Conclusion: The ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model.

Cardiac Autonomic Dysfunction Is Associated With Risk of Diabetic Kidney Disease Progression in Type 2 Diabetes Mellitus.

Background: Evidence on the relationship between heart rate variability (HRV) and albumin-to-creatinine ratio (ACR) combined with estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes mellitus (T2DM) is rare. Thus, this study aimed to investigate the relationship between heart rate variability and the risk of diabetic kidney disease (DKD) progression in diabetes patients. Method: Overall, 747 T2DM patients who were admitted to the Second Affiliated Hospital of Nanchang University underwent 24-hour dynamic electrocardiograms for HRV analysis. Time-domain HRV measures included mean heart rate, standard deviation of the R-R interval (SDNN), SDNN index, root mean squared difference of successive RR intervals (RMSSD), and percent of adjacent RR intervals with a difference greater than 50 ms (PNN50). Frequency-domain measures included low frequency (LF), very low frequency (VLF), high frequency (HF) components and LF-to-HF ratio. The risk of DKD progression was determined by combining ACR and eGFR and stratified as low risk (Group A), moderately increased risk (Group B), high risk (Group C), and very high risk (Group D) based on the Kidney Disease: Improving Global Outcomes guidelines. Result: There were significant differences in HRV parameters among the four risk groups (SDNN: 113 ms vs 109 ms vs 101 ms vs 81 ms, P<0.01; LF: 240.2 ms2 vs 241.1 ms2 vs 155.2 ms2 vs 141.9 ms2, P<0.01; LF-to-HF ratio: 1.70 vs 1.24 vs 1.12 vs 0.93, P<0.01; VLF: 723.7 ms2 vs 601.1 ms2 vs 446.4 ms2 vs 356.3 ms2, P<0.01). A very high risk of DKD progression was significantly associated with a lower SDNN (ß=-19.5, 95% CI: -30.0 to -10.0, P<0.01), and moderately increased, high, and very high risks were associated with lower LF-to-HF ratio and VLF (P<0.05). Logistic regression analysis showed that group D had a higher risk of reduced SDNN, LF-to-HF ratio, and VLF compared with group A after adjusting for systolic blood pressure, glycated haemoglobin, haemoglobin, high-density lipoprotein cholesterol, and age (odds ratio (95% CI): 0.989 (0. 983-0.996), 0.674 (0.498-0.913), and 0.999 (0.999-1.000), respectively). Conclusion: Cardiac autonomic dysfunction is associated with a risk of DKD progression in adults with T2DM, and reduced heart rate variability increased such risk. Thus, HRV screening may be necessary in patients with T2DM, especially those with high proteinuria.