Objective:To evaluate the impact of predictive nursing on the care of acute myocardial infarction(AMI)patients in the Coronary Care Unit(CCU)after interventional therapy.Methods:From September 2021 to September 2023,8...Objective:To evaluate the impact of predictive nursing on the care of acute myocardial infarction(AMI)patients in the Coronary Care Unit(CCU)after interventional therapy.Methods:From September 2021 to September 2023,84 AMI patients admitted to the CCU were randomly divided into two groups:the experimental group(42 patients)received predictive nursing,and the reference group(42 patients)received conventional nursing.Cardiac function and clinical outcomes were compared between the groups.Results:Before nursing,there was no difference in cardiac function between the two groups(P>0.05).After nursing,the cardiac function of the experimental group was better than that of the reference group(P<0.05).The clinical outcomes of the experimental group were better than those of the reference group(P<0.05).Before nursing,there was no difference in psychological scores between the two groups(P>0.05).After nursing,the psychological scores of the experimental group were lower than those of the reference group(P<0.05).Conclusion:Predictive nursing can improve the cardiac function and clinical outcomes of AMI patients after interventional therapy and can also regulate patients’negative psychological states.展开更多
Background Acute myocarditis is a disease characterized by inflammation of the heart muscle, usually caused by viral infection, autoimmune reactions, or toxins, resulting in damage and dysfunction of cardiomyocytes. I...Background Acute myocarditis is a disease characterized by inflammation of the heart muscle, usually caused by viral infection, autoimmune reactions, or toxins, resulting in damage and dysfunction of cardiomyocytes. In recent years, the incidence of acute myocarditis has gradually increased, especially in young people and athletes, which can cause serious cardiac complications and even lead to heart failure, cardiac arrest or major adverse cardiovascular events(MACEs). Methods A total of 90 patients with acute myocarditis(acute myocarditis group) and 30 healthy subjects(control group) admitted to our hospital from November 2021 to November 2023 were selected. Cardiac magnetic resonance(CMR) T1 and T2 mapping sequence scanning and cardiac function parameter measurement was performed on all subjects. The acute myocarditis group was followed up for 12 months and divided into the MACEs group and the non-MACEs group according to whether MACEs occurred. The differences in CMR parameters were analyzed, and receiver operating characteristic(ROC) curve was applied to analyze the predictive value of cardiac CMR T1 and T2 techniques for the occurrence of MACEs in patients with acute myocarditis. Results Compared with the control group, the level of left ventricular ejection fraction(LVEF) in the acute myocarditis group was significantly decreased, while left ventricular end-diastole volume(LVEDV), left ventricular endsystolic volume(LVESV), lactate dehydrogenase(LDH) and creatine kinase MB isoenzyme(CK-MB) increased significantly(P<0.05);T1 and T2 values, late gadolinium enhancement(LGE) percentage of left ventricle, left ventricular entropy and extracellular volume(ECV) in acute myocarditis group were significantly higher than those in the control group(P<0.05). Pearson correlation analysis showed that T1 and T2 values were negatively correlated with LVEF, while T1 and T2 values were positively correlated with LVESV, CK-MB, left ventricular entropy and ECV in patients with acute myocarditis(P<0.05). Among patients with acute myocarditis, the values of T1 and T2, left ventricular entropy and ECV in MACEs group were higher than those in non-MACEs group(P<0.05). Further multivariate analysis also showed that T1 and T2 values had independent predictive capacity for MACEs in patients with acute myocarditis(P<0.05). The maximum area under ROC curve was 0.894 and 0.912, and the sensitivity was 89.35% and 90.40% in patients with acute myocarditis predicted by T1 and T2 mapping. The specificity was 80.34% and 84.25%. Conclusions CMR T1 and T2 mapping techniques can effectively evaluate the cardiac function status of patients with acute myocarditis, and provide an important reference for the prognosis assessment of acute myocarditis.展开更多
Coronary artery disease is the leading cause of death in advanced countries and its prevalence is increasing among the developing countries.Cardiac computed tomography(CT) has been increasingly used in the diagnosis o...Coronary artery disease is the leading cause of death in advanced countries and its prevalence is increasing among the developing countries.Cardiac computed tomography(CT) has been increasingly used in the diagnosis of coronary artery disease due to its rapid improvements in multislice CT scanners over the last decade,and this less-invasive technique has become a potentially effective alternative to invasive coronary angiography.Quantifying the amount of coronary artery calcium with cardiac CT has been widely accepted as a reliable non-invasive technique for predicting risk of future cardiovascular events.However,the main question that remains uncertain is whether routine,widespread coronary artery calcium scoring in an individual patient will result in an overall improvement in quality of care and clinical outcomes.In this commentary,we discuss a current issue of the clinical value of coronary artery calcium scoring with regard to its value of predicting adverse cardiac events.We also discuss the applications of coronary artery calcium scores in patients with different risk groups.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
Heart rate variability （HRV） refers to the variations between consecutive heartbeats, which depend on the continuous modulation of the sympathetic and parasympathetic branches of the autonomic nervous system. HRV ha...Heart rate variability （HRV） refers to the variations between consecutive heartbeats, which depend on the continuous modulation of the sympathetic and parasympathetic branches of the autonomic nervous system. HRV has been shown to be effective as a predictor of risk after myocardial infarction and an early warning sign of diabetic neuropathy, and in the cardiology setting is now recognized to be a useful tool for risk-stratification after hospital admission and after discharge. Recent evidences suggest that HRV analysis might predict complications even in patients undergoing cardiac surgery, and the present review summarizes the importance of HRV analysis in adult cardiac surgery and the perspectives for HRV use in current clinical practice. Although future larger studies are warranted before HRV can be included into daily clinical practice in adult cardiac surgery, HRV is a novel tool which might detect autonomic instability in the early postoperative phase and during hospital stay, thus predicting or prompt-diagnosing many of the post-operative complications.展开更多
Objectives: To determine the predictive value of the ECG for sudden death in the general population. Design: In the Copenhagen City Heart Study, a randomly selected population sample in Copenhagen, Denmarkhas been fol...Objectives: To determine the predictive value of the ECG for sudden death in the general population. Design: In the Copenhagen City Heart Study, a randomly selected population sample in Copenhagen, Denmarkhas been followed prospectively since 1976. From this population sample, we analyzed ECGs of individuals who had suffered sudden cardiac death (SCD) before the age of 50 years and compared them with ECGs of a randomly selected control individuals from the same population sample. Specific ECG signs that could point toward a condition associated with a risk of SCD were noted. Results: From a total of 18,974 individuals in the cohort, 207 had died at an age younger than 50 years. Among these, 24 persons with SCD were identified. The most prevalent ECG abnormality was QRS fragmentation. We found no ECGs with long or short QTc, Brugada sign or WPW. The prevalence of signs of left ventricular hyper-trophy, early repolarization, or fragmentation was not different from the prevalence of these signs in the control group. Conclusion: In the Copenhagen City Heart Study, the ECG failed to predict SCD in persons who died before the age of 50 years.展开更多
Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,...Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,identify independent risk factors significantly associated with frailty,and construct an effective risk prediction model.The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients.Methodss Using convenience sampling,we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024.General demographic data were collected via questionnaires,alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL)scale.Clinical data were obtained by reviewing medical records.Participants were categorized into a frail group(n=65)and a non-frail group(n=119)based on frailty status.Clinical risk factors were compared between groups.Multivariate logistic regression was used to identify independent risk factors.A prediction model was constructed,and a receiver operating characteristic(ROC)curve was plotted to evaluate its predictive value.Results A total of 184 hospitalized CHF patients were included,with 65(35.33%)exhibiting frailty.Multivariate logistic regression analysis showed that independent risk factors for frailty included:age,ADL score,N-terminal pro-brain natriuretic peptide(NT-pro-BNP),left ventricular ejection fraction(LVEF),New York Heart Association(NYHA)class II/IV,≥3 comorbidities,comorbid diabetes mellitus(DM),comorbid valvular heart disease(VHD),smoking history,hemoglobin(Hb),albumin,high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),creatinine(Cr),and blood urea nitrogen(BUN).The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built.The prediction model showed quite strong predictive performance.Its area under the ROC curve was 0.904(95%CI:0.857-0.951),with a sensitivity of98.5%and a specificity of 85.7%.ConclusionssThe frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration.It provided substantial reference value for clinical management of CHF,offering a basis for early assessment,risk stratification,and targeted interventions to prevent frailty by identifying high-risk patients.展开更多
文摘Objective:To evaluate the impact of predictive nursing on the care of acute myocardial infarction(AMI)patients in the Coronary Care Unit(CCU)after interventional therapy.Methods:From September 2021 to September 2023,84 AMI patients admitted to the CCU were randomly divided into two groups:the experimental group(42 patients)received predictive nursing,and the reference group(42 patients)received conventional nursing.Cardiac function and clinical outcomes were compared between the groups.Results:Before nursing,there was no difference in cardiac function between the two groups(P>0.05).After nursing,the cardiac function of the experimental group was better than that of the reference group(P<0.05).The clinical outcomes of the experimental group were better than those of the reference group(P<0.05).Before nursing,there was no difference in psychological scores between the two groups(P>0.05).After nursing,the psychological scores of the experimental group were lower than those of the reference group(P<0.05).Conclusion:Predictive nursing can improve the cardiac function and clinical outcomes of AMI patients after interventional therapy and can also regulate patients’negative psychological states.
文摘Background Acute myocarditis is a disease characterized by inflammation of the heart muscle, usually caused by viral infection, autoimmune reactions, or toxins, resulting in damage and dysfunction of cardiomyocytes. In recent years, the incidence of acute myocarditis has gradually increased, especially in young people and athletes, which can cause serious cardiac complications and even lead to heart failure, cardiac arrest or major adverse cardiovascular events(MACEs). Methods A total of 90 patients with acute myocarditis(acute myocarditis group) and 30 healthy subjects(control group) admitted to our hospital from November 2021 to November 2023 were selected. Cardiac magnetic resonance(CMR) T1 and T2 mapping sequence scanning and cardiac function parameter measurement was performed on all subjects. The acute myocarditis group was followed up for 12 months and divided into the MACEs group and the non-MACEs group according to whether MACEs occurred. The differences in CMR parameters were analyzed, and receiver operating characteristic(ROC) curve was applied to analyze the predictive value of cardiac CMR T1 and T2 techniques for the occurrence of MACEs in patients with acute myocarditis. Results Compared with the control group, the level of left ventricular ejection fraction(LVEF) in the acute myocarditis group was significantly decreased, while left ventricular end-diastole volume(LVEDV), left ventricular endsystolic volume(LVESV), lactate dehydrogenase(LDH) and creatine kinase MB isoenzyme(CK-MB) increased significantly(P<0.05);T1 and T2 values, late gadolinium enhancement(LGE) percentage of left ventricle, left ventricular entropy and extracellular volume(ECV) in acute myocarditis group were significantly higher than those in the control group(P<0.05). Pearson correlation analysis showed that T1 and T2 values were negatively correlated with LVEF, while T1 and T2 values were positively correlated with LVESV, CK-MB, left ventricular entropy and ECV in patients with acute myocarditis(P<0.05). Among patients with acute myocarditis, the values of T1 and T2, left ventricular entropy and ECV in MACEs group were higher than those in non-MACEs group(P<0.05). Further multivariate analysis also showed that T1 and T2 values had independent predictive capacity for MACEs in patients with acute myocarditis(P<0.05). The maximum area under ROC curve was 0.894 and 0.912, and the sensitivity was 89.35% and 90.40% in patients with acute myocarditis predicted by T1 and T2 mapping. The specificity was 80.34% and 84.25%. Conclusions CMR T1 and T2 mapping techniques can effectively evaluate the cardiac function status of patients with acute myocarditis, and provide an important reference for the prognosis assessment of acute myocarditis.
文摘Coronary artery disease is the leading cause of death in advanced countries and its prevalence is increasing among the developing countries.Cardiac computed tomography(CT) has been increasingly used in the diagnosis of coronary artery disease due to its rapid improvements in multislice CT scanners over the last decade,and this less-invasive technique has become a potentially effective alternative to invasive coronary angiography.Quantifying the amount of coronary artery calcium with cardiac CT has been widely accepted as a reliable non-invasive technique for predicting risk of future cardiovascular events.However,the main question that remains uncertain is whether routine,widespread coronary artery calcium scoring in an individual patient will result in an overall improvement in quality of care and clinical outcomes.In this commentary,we discuss a current issue of the clinical value of coronary artery calcium scoring with regard to its value of predicting adverse cardiac events.We also discuss the applications of coronary artery calcium scores in patients with different risk groups.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
文摘Heart rate variability （HRV） refers to the variations between consecutive heartbeats, which depend on the continuous modulation of the sympathetic and parasympathetic branches of the autonomic nervous system. HRV has been shown to be effective as a predictor of risk after myocardial infarction and an early warning sign of diabetic neuropathy, and in the cardiology setting is now recognized to be a useful tool for risk-stratification after hospital admission and after discharge. Recent evidences suggest that HRV analysis might predict complications even in patients undergoing cardiac surgery, and the present review summarizes the importance of HRV analysis in adult cardiac surgery and the perspectives for HRV use in current clinical practice. Although future larger studies are warranted before HRV can be included into daily clinical practice in adult cardiac surgery, HRV is a novel tool which might detect autonomic instability in the early postoperative phase and during hospital stay, thus predicting or prompt-diagnosing many of the post-operative complications.
文摘Objectives: To determine the predictive value of the ECG for sudden death in the general population. Design: In the Copenhagen City Heart Study, a randomly selected population sample in Copenhagen, Denmarkhas been followed prospectively since 1976. From this population sample, we analyzed ECGs of individuals who had suffered sudden cardiac death (SCD) before the age of 50 years and compared them with ECGs of a randomly selected control individuals from the same population sample. Specific ECG signs that could point toward a condition associated with a risk of SCD were noted. Results: From a total of 18,974 individuals in the cohort, 207 had died at an age younger than 50 years. Among these, 24 persons with SCD were identified. The most prevalent ECG abnormality was QRS fragmentation. We found no ECGs with long or short QTc, Brugada sign or WPW. The prevalence of signs of left ventricular hyper-trophy, early repolarization, or fragmentation was not different from the prevalence of these signs in the control group. Conclusion: In the Copenhagen City Heart Study, the ECG failed to predict SCD in persons who died before the age of 50 years.
基金supported by Guangdong Medical Science and Technology Research Fund Project(No.A2022458)Guangdong Provincial People's Medical Climbing Plan(Nursing Research Project)(No.DFJH2020011)。
文摘Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,identify independent risk factors significantly associated with frailty,and construct an effective risk prediction model.The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients.Methodss Using convenience sampling,we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024.General demographic data were collected via questionnaires,alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL)scale.Clinical data were obtained by reviewing medical records.Participants were categorized into a frail group(n=65)and a non-frail group(n=119)based on frailty status.Clinical risk factors were compared between groups.Multivariate logistic regression was used to identify independent risk factors.A prediction model was constructed,and a receiver operating characteristic(ROC)curve was plotted to evaluate its predictive value.Results A total of 184 hospitalized CHF patients were included,with 65(35.33%)exhibiting frailty.Multivariate logistic regression analysis showed that independent risk factors for frailty included:age,ADL score,N-terminal pro-brain natriuretic peptide(NT-pro-BNP),left ventricular ejection fraction(LVEF),New York Heart Association(NYHA)class II/IV,≥3 comorbidities,comorbid diabetes mellitus(DM),comorbid valvular heart disease(VHD),smoking history,hemoglobin(Hb),albumin,high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),creatinine(Cr),and blood urea nitrogen(BUN).The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built.The prediction model showed quite strong predictive performance.Its area under the ROC curve was 0.904(95%CI:0.857-0.951),with a sensitivity of98.5%and a specificity of 85.7%.ConclusionssThe frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration.It provided substantial reference value for clinical management of CHF,offering a basis for early assessment,risk stratification,and targeted interventions to prevent frailty by identifying high-risk patients.
