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

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 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 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 determine the chemical constituents in BSLYT by ultra-high performance liquid chromatography-mass spectrometry(UHPLC-MS/MS),and to establish a high-performance liquid chromatographic fingerprint of the material basis of BSLYT and the content determination of its six main constituents,mononoside,loganin,oroxin A,oroxin B,baicalein and astilbin,providing a reference for the quality control.Methods The mass spectrometry data were used to establish the fingerprints.The content of the main components in the BSLYT samples was calculated by using the external method,and the discrepency between different batches of samples were analyzed by combining with chemometric methods.Results A total of 69 compounds were identified by mass spectrometry,and 13 compounds were identified after comparison with high-performance liquid chromatography(HPLC).The similarity of the baseline characterization profiles of the 15 batches of BSLYT substances was above 0.90,and a total of 27 common peaks were identified.Cluster analysis(CA)classified the substance benchmarks into 2 classes,S1,S2,S5,S8,S9,S13,and S15 were clustered into one class,and S3,S4,S6,S7,S10-S12,and S16 were clustered into one class.By combining PCA and OPLS-DA,the chemical components affecting the baseline classification of the substances were screened and attributed to wood butterfly,cornelian cherry and cocos nigra,respectively.The contents of six components were determined by MICS,which ranged from 0.31%-0.51%,0.12%-0.22%,0.09%-0.19%,0.09%-0.24%,0.07%-0.18%,and 0.08%-0.29%for mononoside,loganin,oroxin A,oroxin B,baicalein and astilbin respectively.Conclusion The fingerprint and multi-indicator content determination method established in this paper are accurate and stable,which provide a basis for the quality control of the material benchmark of Kidney and Pharynx Formula and its related preparations.

Objective To assess the association between Metformin use and the risk of dementia in patients with type 2 diabetes mellitus(T2DM).Methods The research data came from the big medical data platform of Yinzhou District,and we constructed a cohort of T2DM patients who had initiated treatment of Metformin or sulfonylurea since January 1,2009.The inverse probability of treatment weighting(IPTW)was used to control the baseline confounding factors,and the Cox regression model was used to estimate the HR(95%CI)of the association between Metformin use and dementia risk.Results The incidence rate of dementia in new users of Metformin(41181 persons)and sulfonylureas(38092 persons)was 128.4 per 100000 person years and 142.3 per 100000 person years respectively.Compared with sulfonylureas,the crude analysis with no adjustment for confounding factors showed that there was a negative association between the use of Metformin and the incidence of dementia,with an HR(95%CI)0.930(0.800～1.090).After adjusting for potential confounders with IPTW,Metformin was not significantly associated with the risk of dementia HR(95%CI)1.040(0.890～1.220).The subgroup analysis results for different baseline characteristics were consistent with the primary analysis results,and there were no statistically significant associations between Metformin and dementia incidence risk in all subgroups.Conclusions There is no significant association between the use of Metformin and the risk of dementia in T2DM patients in the Yinzhou District.

Objective To study the correlation between semen quality and bacterial infection in men with abnormal fer-tility,and provide clinical basis for guiding the reproductive health of men with abnormal fertility.Methods 200 male semen samples with abnormal fertility were collected,and then separated and cultured for 48 hours.According to the culture results,they were divided into three groups:the non-pathogenic group,the pathogenic group,and the sterile group.The bacterial resist-ance analysis of the pathogenic group was conducted,and the semen quality between each group was compared.Results After 48 hours of isolation and cultivation,200 semen samples had been tested,non-pathogenic bacteria was detected in 163 semen samples,accounting for 81.5% ;pathogenic bacteria was detected in 33 semen samples,accounting for 16.5% ;and bacteria was not detected in 4 semen samples,accounting for 2.0% .The top three strains of pathogenic bacteria in 33 cases were Escherichia coli,Streptococcus agalactiae,and Enterococcus faecalis,with drug resistance rates of 80.0% ,87.5% ,and 100.0% ,respec-tively.Conclusion The detection rate of bacterial culture in semen of men with abnormal fertility is relatively high,and patho-genic bacteria can affect semen quality.

Objective:To evaluate the correlation and consistency between quantitative coronary flow fraction (QFR) and cardiac magnetic resonance imaging (CMR) in assessing myocardial ischemia in patients with coronary heart disease (CAD).Methods:A retrospective analysis was conducted on the data of coronary heart disease patients who underwent load CMR examination and coronary angiography at the Beijing Anzhen Hospital, Capital Medical University from August 2017 to March 2022. CMR examination includes cardiac cine, load/rest myocardial perfusion imaging, and delayed enhancement sequence. According to the results of CMR examination, the patient′s left ventricular myocardial segments were divided into normal segment group and abnormal segment group (further divided into ischemic segment group and infarcted segment group). On the basis of coronary angiography, an artificial intelligence based platform (AngioPlus system) was applied to calculate the preoperative coronary artery QFR value of patients undergoing percutaneous coronary intervention treatment. Kappa test was used to evaluate the consistency of QFR and CMR in diagnosing abnormal myocardium; Mann Whitney U test was used to compare the differences in QFR between groups; The receiver operating characteristic (ROC) curve was used to evaluate the efficacy of QFR in diagnosing abnormal myocardium; Spearman correlation analysis was used to clarify the relationship between myocardial infarction area and QFR value of the supplying coronary artery in patients.Results:Among the 70 CAD patients enrolled, there were 60 males and 10 females, aged (54.1±11.1)years. At the vascular level, the consistency between QFR and CMR in diagnosing myocardial injury (including ischemia and infarction) is moderate (Kappa value=0.514). The sensitivity and specificity of detecting abnormal myocardial segments in CAD patients were 57% and 91%, respectively. The area under the curve (AUC) value of QFR predicting abnormal myocardium in CAD patients was 0.769, and the optimal cutoff value was QFR=0.865. At this time, the sensitivity and specificity of QFR predicting myocardial injury in CAD patients were 67.2% and 84.3%, respectively. The difference in vascular QFR between the normal segment group, ischemic segment group, and infarcted segment group was statistically significant ( P<0.001), with the infarcted segment group having significantly lower QFR values than the other two groups (all P<0.01). The range of myocardial infarction was negatively correlated with the QFR value of the supplying coronary artery ( r=-0.45, P<0.001). At the patient level, the consistency between QFR and CMR in diagnosing myocardial injury (including ischemia and infarction) was moderate (Kappa value=0.445), with a sensitivity of 74% and a specificity of 81% for diagnosing myocardial injury in CAD patients. Conclusions:Compared with CMR, QFR has better specificity in detecting myocardial injury in CAD patients. The QFR value of the infarcted segment group is significantly lower than that of the ischemic group and the normal group. The area of myocardial infarction is negatively correlated with the QFR value of the supplying coronary artery.

Objective:To investigate the clinical characteristics and prognosis of bloodstream infection caused by carbapenem-resistant Klebsiella pneumoniae (CRKP) in preterm infants, and to provide basis for early clinical diagnosis and infection control. Methods:The clinical data of infants with CRKP bloodstream infection admitted to the Preterm Infants Ward of Children′s Hospital Affiliated to Zhengzhou University from January 2015 to December 2022 were retrospectively analyzed. The risk factors for death in preterm infants caused by CRKP bloodstream infection were explored through multivariate logistic regression analysis, and the receiver operating characteristic (ROC) curve was used to analyze the clinical value of each factor on evaluating prognosis. The area under curves (AUC) of each factor in different ROC curve were compared by Delong′s test.Results:A total of 96 preterm infants with CRKP bloodstream infection were included, including 70 in the survival group and 26 in the death group. The first onset symptoms of CRKP bloodstream infection in preterm infants were persistent tachycardia (heart rate>180 per minute) (69 cases, 71.9%), fever (61 cases, 63.5%), and apnea (59 cases, 61.5%). There were 88(91.7%) cases of infection combined with septic shock, and 91(94.8%) cases required vasoactive drug support. Multivariate logistic regression analysis showed that the maximum vasoactive-inotropic score (VIS) within 48 hours of onset (odds ratio ( OR)=1.058, 95% confidence interval (95% CI) 1.022 to 1.095, P=0.001), concurrent purulent meningitis ( OR=8.029, 95% CI 1.344 to 47.972, P=0.022), and concurrent necrotizing enterocolitis (NEC) ( OR=10.881, 95% CI 1.566 to 75.580, P=0.016) were independent risk factors for death in preterm infants with CRKP bloodstream infection. The ROC curve showed that the AUCs for evaluating the prognosis of preterm infants with NEC and purulent meningitis were 0.784 and 0.711, respectively. The AUC for evaluating the prognosis of preterm infants with a maximum VIS ≥52.5 points within 48 hours of onset was 0.840, and the AUC for combining the three factors was 0.931. Compared with NEC and purulent meningitis, the AUC for combining factors was higher, the differences were statistically significant ( P=0.002, P<0.001). Conclusions:Preterm infants with CRKP bloodstream infection who have a maximum VIS ≥52.5 points within 48 hours of onset, with NEC and purulent meningitis have a higher risk of death.

Objective To investigate the differences and the immunocompatibility of wild-type (WT), four-gene modified (TKO/hCD55) and six-gene modified (TKO/hCD55/hCD46/hTBM) pig erythrocytes with human serum. Methods The blood samples were collected from 20 volunteers with different blood groups. WT, TKO/hCD55, TKO/hCD55/hCD46/hTBM pig erythrocytes, ABO-compatible (ABO-C) and ABO-incompatible (ABO-I) human erythrocytes were exposed to human serum of different blood groups, respectively. The blood agglutination and antigen-antibody binding levels (IgG, IgM) and complement-dependent cytotoxicity were detected. The immunocompatibility of two types of genetically modified pig erythrocytes with human serum was evaluated. Results No significant blood agglutination was observed in the ABO-C group. The blood agglutination levels in the WT and ABO-I groups were higher than those in the TKO/hCD55 and TKO/hCD55/hCD46/hTBM groups (all P<0.001). The level of erythrocyte lysis in the WT group was higher than those in the ABO-C, TKO/hCD55 and TKO/hCD55/hCD46/hTBM groups. The level of erythrocyte lysis in the ABO-I group was higher than those in the TKO/hCD55 and TKO/hCD55/hCD46/hTBM groups (both P<0.01). The pig erythrocyte binding level with IgM and IgG in the TKO/hCD55 group was lower than those in the WT and ABO-I groups. The pig erythrocyte binding level with IgG and IgM in the TKO/hCD55/hCD46/hTBM group was lower than that in the WT group and pig erythrocyte binding level with IgG was lower than that in the ABO-I group (all P<0.05). Conclusions The immunocompatibility of genetically modified pig erythrocytes is better than that of wild-type pigs and close to that of ABO-C pigs. Humanized pig erythrocytes may be considered as a blood source when blood sources are extremely scarce.