OBJECTIVES: To develop a risk prediction model for left ventricular adverse remodeling (LVAR) based on cardiac magnetic resonance (CMR) parameters in patients undergoing percutaneous coronary intervention (PCI) for acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 329 acute STEMI patients undergoing primary PCI at 8 medical centers from January, 2018 to December, 2021 were prospectively enrolled. The parameters of CMR, performed at 7±2 days and 6 months post-PCI, were analyzed using CVI42 software. LVAR was defined as an increase >20% in left ventricular end-diastolic volume or >15% in left ventricular end-systolic volume at 6 months compared to baseline. The patients were randomized into training (n=230) and validation (n=99) sets in a 7∶3 ratio. In the training set, potential predictors were selected using LASSO regression, followed by univariate and multivariate logistic regression to construct a nomogram. Model performance was evaluated using receiver-operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis. RESULTS: LVAR occurred in 100 patients (30.40%), who had a higher incidence of major adverse cardiovascular events than those without LVAR (58.00% vs 16.16%, P<0.001). Left ventricular global longitudinal strain (LVGLS; OR=0.76, 95% CI: 0.61-0.95, P=0.015) and left atrial active strain (LAAS; OR=0.78, 95% CI: 0.67-0.92, P=0.003) were protective factors for LVAR, while infarct size (IS; OR=1.05, 95% CI: 1.01-1.10, P=0.017) and microvascular obstruction (MVO; OR=1.26, 95% CI: 1.01-1.59, P=0.048) were risk factors for LVAR. The nomogram had an AUC of 0.90 (95% CI: 0.86-0.94) in the training set and an AUC of 0.88 (95% CI: 0.81-0.94) in the validation set. CONCLUSIONS LVGLS, LAAS, IS, and MVO are independent predictors of LVAR in STEMI patients following PCI. The constructed nomogram has a strong predictive ability to provide assistance for management and early intervention of LVAR.
Humans ; Percutaneous Coronary Intervention ; Prospective Studies ; ST Elevation Myocardial Infarction/diagnostic imaging* ; Ventricular Remodeling ; Magnetic Resonance Imaging ; Male ; Female ; Middle Aged ; Risk Factors ; Aged ; Risk Assessment

Malignant insulinoma is a kind of rare and challenging neuroendocrine tumor. It is often accompanied by distant metastasis, among which liver metastasis is most common, and the prognosis is often non-promising. In this paper, we report a case of multiple liver metastases from malignant insulinoma. The patient, a 70-year-old male, was admitted to the hospital due to "episodic consciousness disorder for more than four months." Blood glucose monitoring revealed recurrent hypoglycemia in the early morning, after meals, and at night. Pancreatic perfusion CT and dynamic enhanced MRI of the liver revealed a mass in the uncinate process of the pancreatic head and multiple liver metastases. Percutaneous liver biopsy confirmed the diagnosis of insulinoma. After multidisciplinary discussions, hepatic artery embolization and radiofrequency ablation were performed in stages, in combination with everolimus treatment. Thereafter, the enhanced CT demonstrated that some liver metastases shrank. The patient had regular meals, and the blood sugar gradually increased and remained normal thereafter. This article discusses this case's clinical characteristics and multidisciplinary collaborative diagnosis and treatment, aiming to provide experience for the comprehensive clinical diagnosis and treatment of malignant insulinoma patients.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Objective To evaluate the predictive value of global longitudinal strain(GLS)measured by cardiac magnetic resonance(CMR)feature-tracking technique for left ventricular remodeling(LVR)after percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 403 patients undergoing PCI for acute STEMI were prospectively recruited from multiple centers in China.CMR examinations were performed one week(7±2 days)and 6 months after myocardial infarction to obtain GLS,global radial strain(GRS),global circumferential strain(GCS),ejection fraction(LVEF)and infarct size(IS).The primary endpoint was LVR,defined as an increase of left ventricle end-diastolic volume by≥20%or an increase of left ventricle end-systolic volume by≥15%from the baseline determined by CMR at 6 months.Logistic regression analysis was performed to evaluate the predictive value of CMR parameters for LVR.Results LVR occurred in 101 of the patients at 6 months after myocardial infarction.Compared with those without LVR(n=302),the patients in LVR group exhibited significantly higher GLS and GCS(P<0.001)and lower GRS and LVEF(P<0.001).Logistic regression analysis indicated that both GLS(OR=1.387,95%CI:1.223-1.573;P<0.001)and LVEF(OR=0.951,95%CI:0.914-0.990;P=0.015)were independent predictors of LVR.ROC curve analysis showed that at the optimal cutoff value of-10.6%,GLS had a sensitivity of 74.3%and a specificity of 71.9%for predicting LVR.The AUC of GLS was similar to that of LVEF for predicting LVR(P=0.146),but was significantly greater than those of other parameters such as GCS,GRS and IS(P<0.05);the AUC of LVEF did not differ significantly from those of the other parameters(P>0.05).Conclusion In patients receiving PCI for STEMI,GLS measured by CMR is a significant predictor of LVR occurrence with better performance than GRS,GCS,IS and LVEF.

Objective:To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms.Methods:The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms—random forest, decision tree, and XGBoost—were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results:A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95% CI 0.813-0.995), 0.956 (95% CI 0.796-0.992), and 0.973 (95% CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95% CI 0.557-0.964), 0.706 (95% CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion:Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.

Objective To evaluate the predictive value of global longitudinal strain(GLS)measured by cardiac magnetic resonance(CMR)feature-tracking technique for left ventricular remodeling(LVR)after percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 403 patients undergoing PCI for acute STEMI were prospectively recruited from multiple centers in China.CMR examinations were performed one week(7±2 days)and 6 months after myocardial infarction to obtain GLS,global radial strain(GRS),global circumferential strain(GCS),ejection fraction(LVEF)and infarct size(IS).The primary endpoint was LVR,defined as an increase of left ventricle end-diastolic volume by≥20%or an increase of left ventricle end-systolic volume by≥15%from the baseline determined by CMR at 6 months.Logistic regression analysis was performed to evaluate the predictive value of CMR parameters for LVR.Results LVR occurred in 101 of the patients at 6 months after myocardial infarction.Compared with those without LVR(n=302),the patients in LVR group exhibited significantly higher GLS and GCS(P<0.001)and lower GRS and LVEF(P<0.001).Logistic regression analysis indicated that both GLS(OR=1.387,95%CI:1.223-1.573;P<0.001)and LVEF(OR=0.951,95%CI:0.914-0.990;P=0.015)were independent predictors of LVR.ROC curve analysis showed that at the optimal cutoff value of-10.6%,GLS had a sensitivity of 74.3%and a specificity of 71.9%for predicting LVR.The AUC of GLS was similar to that of LVEF for predicting LVR(P=0.146),but was significantly greater than those of other parameters such as GCS,GRS and IS(P<0.05);the AUC of LVEF did not differ significantly from those of the other parameters(P>0.05).Conclusion In patients receiving PCI for STEMI,GLS measured by CMR is a significant predictor of LVR occurrence with better performance than GRS,GCS,IS and LVEF.

Objective To investigate the relationship between systemic immune-inflammation index (SII)and lower extremity vascular disease in patients with type 2 diabetes mellitus (T2DM).Methods A cross-sectional study was conducted on 390 T2DM patients admitted in our department from January 2013 to January 2024.According to the diagnostic criteria for lower extremity vascular disease in T2DM patients,they were divided into a lower extremity vascular disease group (n=158)and a control group (n=232).General data and results of laboratory tests were compared between the 2 groups.Spearman correlation analysis was used to identify the related factors for lower extremity vascular diseases in T2DM patients.The correlation between SII and lower extremity vascular diseases in T2DM patients was analyzed using the Row Mean Scores and Cochran-Armitage Trend analysis.Multivariate logistic regression analysis was applied to identify the risk factors for lower limb vascular lesions in T2DM patients.Receiver operating characteristic (ROC)curve was plotted to evaluate the diagnostic efficacy of SII for lower extremity vascular disease in the patients.Results Compared with T2DMpatients without lower extremity vascular disease,those with lower extremity vascular disease were older,had higher levels of total cholesterol (TC),low-density lipoprotein cholesterol (LDL-C),SII,larger proportion of carotid vascular lesions,and increased proportion of no-taking statins.The lower extremity vascular disease in T2DM patients was positively correlated with SII/100 (r=0.429,P<0.001),age (r=0.517,P<0.001),TC (r=0.161,P=0.001),LDL-C (r=0.117,P=0.021),carotid artery lesions (r=0.101,P=0.047),no-taking statins (r=0.266,P<0.001).Logistic regression analysis showed that SII,age,LDL-C,and no-taking statins were the risk factors for lower extremity vascular lesions in T2DM patients (P<0.01).The area under the curve (AUC)value of SII combined with age,LDL-C,and no-taking statins in predicting lower extremity vascular disease in T2DM patients was 0.896.Conclusion SII is not only a risk factor,but also a simple marker for lower extremity vascular disease in T2DM patients,suggesting that inflammatory response plays an important role in the occurrence and development of lower extremity vascular disease in T2DM.

Objective To observe the value of Zero-Shot Noise2Noise(ZS-N2N)based on Z-axis correlation for reducing noise of low dose CT(LDCT)images.Methods CT data of the cancer imaging archive were enrolled,including normal dose CT(NDCT)images and LDCT images,with 3 sets of chest and 3 sets of abdominal images.Noise on LDCT images were reduced with ZS-N2N method based on Z-axis correlation,and the peak signal-to-noise ratio(PSNR),structural similarity(SSIM)and time-consuming of reducing noise were compared with those of Self2Self,simple ZS-N2N and traditional Block-matching and 3D filtering(BM3D).Results After reducing noise,noise on Self2Self denoised images remained significant,the structure edges on BM3D denoised images were blurry with some details lost,while simple ZS-N2N and ZS-N2N based on Z-axis correlation denoised images preserved more details and had better quality.PSNR and SSIM of Self2Self denoised images were poor and the time-consuming were longer.PSNR,SSIM and time-consuming of the other 3 methods were similar,among which PSNR of ZS-N2N based on Z-axis correlation were slightly higher than BM3D and simple ZS-N2N,but the time-consuming were also slightly longer.Conclusion ZS-N2N based on Z-axis correlation had high value for reducing noise of LDCT images.