BACKGROUND Fulminant myocarditis is the critical form of myocarditis that is often associated with heart failure, malignant arrhythmia, and circulatory failure. Patients with fulminant myocarditis who end up with seve...BACKGROUND Fulminant myocarditis is the critical form of myocarditis that is often associated with heart failure, malignant arrhythmia, and circulatory failure. Patients with fulminant myocarditis who end up with severe multiple organic failure and death are not rare.AIM To analyze the predictors of in-hospital major adverse cardiovascular events(MACE) in patients diagnosed with fulminant myocarditis.METHODS We included a cohort of adult patients diagnosed with fulminant myocarditis who were admitted to Beijing Anzhen Hospital from January 2007 to December2017. The primary endpoint was defined as in-hospital MACE, including death,cardiac arrest, cardiac shock, and ventricular fibrillation. Baseline demographics,clinical history, characteristics of electrocardiograph and ultrasonic cardiogram,laboratory examination, and treatment were recorded. Multivariable logistic regression was used to examine risk factors for in-hospital MACE, and the variables were subsequently assessed by the area under the receiver operating characteristic curve(AUC).RESULTS The rate of in-hospital MACE was 40%. Multivariable logistic regression analysis revealed that baseline QRS duration > 120 ms was the independent risk factor for in-hospital MACE(odds ratio = 4.57, 95%CI: 1.23-16.94, P = 0.023). The AUC of QRS duration > 120 ms for predicting in-hospital MACE was 0.683(95%CI: 0.532-0.833, P = 0.03).CONCLUSION Patients with fulminant myocarditis has a poor outcome. Baseline QRS duration is the independent risk factor for poor outcome in those patients.展开更多
Background There are limited data on the prevalence of electrocardiographic (ECG) abnormalities, and their value for predicting a major adverse cardiovascular event (MACE) in patients at high cardiovascular risk. This...Background There are limited data on the prevalence of electrocardiographic (ECG) abnormalities, and their value for predicting a major adverse cardiovascular event (MACE) in patients at high cardiovascular risk. This study aimed to determine the prevalence of ECG abnormalities in patients at high risk for cardiovascular events, and to identify ECG abnormalities that significantly predict MACE. Methods Patients aged ≥ 45 years with established atherosclerotic disease (EAD) were consecutively enrolled from the outpatient clinics of the six participating hospitals during April 2011 to March 2014. The following data were collected: demographic data, cardiovascular risk factors, history of cardiovascular event, physical examination, ECG and medications. ECG was analyzed using Minnesota Code criteria. MACE included cardiovascular death, non-fatal myocardial infarction, and hospitalization due to unstable angina or heart failure. Results A total of 2009 patients were included, 1048 patients (52.2%) had established EAD, and 961 patients (47.8%) had multiple risk factors (MRF). ECG abnormalities included atrial fibrillation (6.7%), premature ventricular contraction (5.4%), pathological Q-wave (Q/QS)(21.3%), T-wave inversion (20.0%), intraventricular ventricular conduction delay (IVCD)(7.3%), left ventricular hypertrophy (LVH)(12.2%), and AV block (12.5%). MACE occurred in 88 patients (4.4%). Independent predictors of MACE were chronic kidney disease, EAD, and the presence of atrial fibrillation, Q/QS, IVCD or LVH by ECG. Conclusions A high prevalence of ECG abnormalities was found. The prevalence of ECG abnormalities was high even among those with risk factors without documented cardiovascular disease.展开更多
The index of Risk Frequency (RF) and other relative indices are used to analyze the temporal and spatial patterns of environmental riskevents in the past 30 years in Shenyang city. The results show that thereexists si...The index of Risk Frequency (RF) and other relative indices are used to analyze the temporal and spatial patterns of environmental riskevents in the past 30 years in Shenyang city. The results show that thereexists significant difference of the RFs between periods of 1966-1977 and1978-1991 (t=7.353**, t0.01=2.807). During the past 30 years, there areno significant changes of the spatial patterns of the environmental risk,while the proportions of environmental risk among the districts are extremely different. In Shenyang city, there exists a series of high riskenterprises, and the chemical industry is the one with highest risk.展开更多
Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe d...Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe data are considered in predicting safety risks.A deep learning method is adopted for extracting reactions in safety risks.The deep neural network(DNN)model for safety risk prediction is shown to extract complex data characteristics better than a shallow network model.Using extended unsafe data and monthly risk indices,hidden layers and iterations are determined.The effectiveness of DNN is also revealed in comparison with the traditional neural network.Through early risk detection using the method in the paper,airlines and the government can mitigate potential risk and take proactive measures to improve civil aviation safety.展开更多
AIM To investigate the incidence and the determinants of cardiovascular morbidity in Greek renal transplant recipients(RTRs) expressed as major advance cardiac event(MACE) rate. METHODS Two hundred and forty-two adult...AIM To investigate the incidence and the determinants of cardiovascular morbidity in Greek renal transplant recipients(RTRs) expressed as major advance cardiac event(MACE) rate. METHODS Two hundred and forty-two adult patients with a functioning graft for at least three months and availabledata that were followed up on the August 31, 2015 at two transplant centers of Western Greece were included in this study. Baseline recipients' data elements included demographics, clinical characteristics, history of comorbid conditions and laboratory parameters. Follow-up data regarding MACE occurrence were collected retrospectively from the patients' records and MACE risk score was calculated for each patient. RESULTS The mean age was 53 years(63.6% males) and 47 patients(19.4%) had a pre-existing cardiovascular disease(CVD) before transplantation. The mean estimated glomerular filtration rate was 52 ± 17 mL /min per 1.73 m2. During follow-up 36 patients(14.9%) suffered a MACE with a median time to MACE 5 years(interquartile range: 2.2-10 years). Recipients with a MACE compared to recipients without a MACE had a significantly higher mean age(59 years vs 52 years, P < 0.001) and a higher prevalence of pre-existing CVD(44.4% vs 15%, P < 0.001). The 7-year predicted mean risk for MACE was 14.6% ± 12.5% overall. In RTRs who experienced a MACE, the predicted risk was 22.3% ± 17.1% and was significantly higher than in RTRs without an event 13.3% ± 11.1%(P = 0.003). The discrimination ability of the model in the Greek database of RTRs was good with an area under the receiver operating characteristics curve of 0.68(95%CI: 0.58-0.78).CONCLUSION In this Greek cohort of RTRs, MACE occurred in 14.9% of the patients, pre-existing CVD was the main risk factor, while MACE risk model was proved a dependable utility in predicting CVD post RT.展开更多
The recognition and management of risk in donation process and blood product is critical to ensure donor and patient safety. To achieve this goal, the failure mode and effects analysis (FMEA) is a convenient method;mo...The recognition and management of risk in donation process and blood product is critical to ensure donor and patient safety. To achieve this goal, the failure mode and effects analysis (FMEA) is a convenient method;moreover it was used to prevent the occurrence of adverse events and look at what could go strong at each step. This study aimed to utilize FMEA in central blood bank in Khartoum to evaluate the potential risk and adverse event that may occur during the donation process. According to the severity, occurrence and the detection of each failure mode, the risk priority number (RPN) was calculated to determine which of the failures should take priority to find a solution and applying corrective action to reduce the failure risk. The statistical package for social sciences (SPSS) version 11 was used as descriptive and analytical statistics tool. The FMEA technique provides a systematic method for finding vulnerabilities in a process before they result in an error, and in this study a satisfactory outcome was reached.展开更多
Owing to information asymmetry,evaluating the credit risk of small-and mediumsized enterprises(SMEs)is difficult.While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generate...Owing to information asymmetry,evaluating the credit risk of small-and mediumsized enterprises(SMEs)is difficult.While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs,our study considers both intrinsic and relational risks generated by neighbor firms’publicly available risk events.We propose a framework for quantifying relational risk based on publicly available risk events for SMEs’credit risk evaluation.Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type,risk event type,and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk.Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs,providing valuable managerial and practical implications for financial institutions.展开更多
基金Supported by Beijing Natural Science Foundation,No.7184205Beijing Talents Fund,No.2017000021469G224Foundation of Beijing Anzhen Hospital,Capital Medical University,No.2016Z07
文摘BACKGROUND Fulminant myocarditis is the critical form of myocarditis that is often associated with heart failure, malignant arrhythmia, and circulatory failure. Patients with fulminant myocarditis who end up with severe multiple organic failure and death are not rare.AIM To analyze the predictors of in-hospital major adverse cardiovascular events(MACE) in patients diagnosed with fulminant myocarditis.METHODS We included a cohort of adult patients diagnosed with fulminant myocarditis who were admitted to Beijing Anzhen Hospital from January 2007 to December2017. The primary endpoint was defined as in-hospital MACE, including death,cardiac arrest, cardiac shock, and ventricular fibrillation. Baseline demographics,clinical history, characteristics of electrocardiograph and ultrasonic cardiogram,laboratory examination, and treatment were recorded. Multivariable logistic regression was used to examine risk factors for in-hospital MACE, and the variables were subsequently assessed by the area under the receiver operating characteristic curve(AUC).RESULTS The rate of in-hospital MACE was 40%. Multivariable logistic regression analysis revealed that baseline QRS duration > 120 ms was the independent risk factor for in-hospital MACE(odds ratio = 4.57, 95%CI: 1.23-16.94, P = 0.023). The AUC of QRS duration > 120 ms for predicting in-hospital MACE was 0.683(95%CI: 0.532-0.833, P = 0.03).CONCLUSION Patients with fulminant myocarditis has a poor outcome. Baseline QRS duration is the independent risk factor for poor outcome in those patients.
基金supported by the Heart Association of Thailand under the Royal Patronage of H.M. the King, National Research Council of Thailand
文摘Background There are limited data on the prevalence of electrocardiographic (ECG) abnormalities, and their value for predicting a major adverse cardiovascular event (MACE) in patients at high cardiovascular risk. This study aimed to determine the prevalence of ECG abnormalities in patients at high risk for cardiovascular events, and to identify ECG abnormalities that significantly predict MACE. Methods Patients aged ≥ 45 years with established atherosclerotic disease (EAD) were consecutively enrolled from the outpatient clinics of the six participating hospitals during April 2011 to March 2014. The following data were collected: demographic data, cardiovascular risk factors, history of cardiovascular event, physical examination, ECG and medications. ECG was analyzed using Minnesota Code criteria. MACE included cardiovascular death, non-fatal myocardial infarction, and hospitalization due to unstable angina or heart failure. Results A total of 2009 patients were included, 1048 patients (52.2%) had established EAD, and 961 patients (47.8%) had multiple risk factors (MRF). ECG abnormalities included atrial fibrillation (6.7%), premature ventricular contraction (5.4%), pathological Q-wave (Q/QS)(21.3%), T-wave inversion (20.0%), intraventricular ventricular conduction delay (IVCD)(7.3%), left ventricular hypertrophy (LVH)(12.2%), and AV block (12.5%). MACE occurred in 88 patients (4.4%). Independent predictors of MACE were chronic kidney disease, EAD, and the presence of atrial fibrillation, Q/QS, IVCD or LVH by ECG. Conclusions A high prevalence of ECG abnormalities was found. The prevalence of ECG abnormalities was high even among those with risk factors without documented cardiovascular disease.
文摘The index of Risk Frequency (RF) and other relative indices are used to analyze the temporal and spatial patterns of environmental riskevents in the past 30 years in Shenyang city. The results show that thereexists significant difference of the RFs between periods of 1966-1977 and1978-1991 (t=7.353**, t0.01=2.807). During the past 30 years, there areno significant changes of the spatial patterns of the environmental risk,while the proportions of environmental risk among the districts are extremely different. In Shenyang city, there exists a series of high riskenterprises, and the chemical industry is the one with highest risk.
基金supported by the Joint Funds of the National Natural Science Foundation of China (No. U1833110)
文摘Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe data are considered in predicting safety risks.A deep learning method is adopted for extracting reactions in safety risks.The deep neural network(DNN)model for safety risk prediction is shown to extract complex data characteristics better than a shallow network model.Using extended unsafe data and monthly risk indices,hidden layers and iterations are determined.The effectiveness of DNN is also revealed in comparison with the traditional neural network.Through early risk detection using the method in the paper,airlines and the government can mitigate potential risk and take proactive measures to improve civil aviation safety.
文摘AIM To investigate the incidence and the determinants of cardiovascular morbidity in Greek renal transplant recipients(RTRs) expressed as major advance cardiac event(MACE) rate. METHODS Two hundred and forty-two adult patients with a functioning graft for at least three months and availabledata that were followed up on the August 31, 2015 at two transplant centers of Western Greece were included in this study. Baseline recipients' data elements included demographics, clinical characteristics, history of comorbid conditions and laboratory parameters. Follow-up data regarding MACE occurrence were collected retrospectively from the patients' records and MACE risk score was calculated for each patient. RESULTS The mean age was 53 years(63.6% males) and 47 patients(19.4%) had a pre-existing cardiovascular disease(CVD) before transplantation. The mean estimated glomerular filtration rate was 52 ± 17 mL /min per 1.73 m2. During follow-up 36 patients(14.9%) suffered a MACE with a median time to MACE 5 years(interquartile range: 2.2-10 years). Recipients with a MACE compared to recipients without a MACE had a significantly higher mean age(59 years vs 52 years, P < 0.001) and a higher prevalence of pre-existing CVD(44.4% vs 15%, P < 0.001). The 7-year predicted mean risk for MACE was 14.6% ± 12.5% overall. In RTRs who experienced a MACE, the predicted risk was 22.3% ± 17.1% and was significantly higher than in RTRs without an event 13.3% ± 11.1%(P = 0.003). The discrimination ability of the model in the Greek database of RTRs was good with an area under the receiver operating characteristics curve of 0.68(95%CI: 0.58-0.78).CONCLUSION In this Greek cohort of RTRs, MACE occurred in 14.9% of the patients, pre-existing CVD was the main risk factor, while MACE risk model was proved a dependable utility in predicting CVD post RT.
文摘The recognition and management of risk in donation process and blood product is critical to ensure donor and patient safety. To achieve this goal, the failure mode and effects analysis (FMEA) is a convenient method;moreover it was used to prevent the occurrence of adverse events and look at what could go strong at each step. This study aimed to utilize FMEA in central blood bank in Khartoum to evaluate the potential risk and adverse event that may occur during the donation process. According to the severity, occurrence and the detection of each failure mode, the risk priority number (RPN) was calculated to determine which of the failures should take priority to find a solution and applying corrective action to reduce the failure risk. The statistical package for social sciences (SPSS) version 11 was used as descriptive and analytical statistics tool. The FMEA technique provides a systematic method for finding vulnerabilities in a process before they result in an error, and in this study a satisfactory outcome was reached.
基金the National Natural Science Foundation of China(Grant Nos.71731005,Nos.72101073)。
文摘Owing to information asymmetry,evaluating the credit risk of small-and mediumsized enterprises(SMEs)is difficult.While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs,our study considers both intrinsic and relational risks generated by neighbor firms’publicly available risk events.We propose a framework for quantifying relational risk based on publicly available risk events for SMEs’credit risk evaluation.Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type,risk event type,and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk.Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs,providing valuable managerial and practical implications for financial institutions.
