Coarctation of the aorta(CoA)is a relatively common congenital cardiac defect often causing few symptoms and therefore can be challenging to diagnose.The hallmark finding on physical examination is upper extremity hyp...Coarctation of the aorta(CoA)is a relatively common congenital cardiac defect often causing few symptoms and therefore can be challenging to diagnose.The hallmark finding on physical examination is upper extremity hypertension,and for this reason,CoA should be considered in any young hypertensive patient,justifying measurement of lower extremity blood pressure at least once in these individuals.The presence of a significant pressure gradient between the arms and legs is highly suggestive of the diagnosis.Early diagnosis and treatment are important as long-term data consistently demonstrate that patients with CoA have a reduced life expectancy and increased risk of cardiovascular complications.Surgical repair has traditionally been the mainstay of therapy for correction,although advances in endovascular technology with covered stents or stent grafts permit nonsurgical approaches for the management of older children and adults with native CoA and complications.Persistent hypertension and vascular dysfunction can lead to an increased risk of coronary disease,which,remains the greatest cause of long-term mortality.Thus,blood pressure control and periodic reassessment with transthoracic echocardiography and threedimensional imaging(computed tomography or cardiac magnetic resonance)for should be performed regularly as cardiovascular complications may occur decades after the intervention.展开更多
BACKGROUND Atrioventricular block requiring permanent pacemaker(PPM)implantation is an important complication of transcatheter aortic valve replacement(TAVR).Application of machine learning could potentially be used t...BACKGROUND Atrioventricular block requiring permanent pacemaker(PPM)implantation is an important complication of transcatheter aortic valve replacement(TAVR).Application of machine learning could potentially be used to predict preprocedural risk for PPM.AIM To apply machine learning to be used to predict pre-procedural risk for PPM.METHODS A retrospective study of 1200 patients who underwent TAVR(January 2014-December 2017)was performed.964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis.After the exclusion of variables with near-zero variance or≥50%missing data,167 variables were included in the random forest gradient boosting algorithm(GBM)optimized using 5-fold cross-validations repeated 10 times.The receiver operator curve(ROC)for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year.RESULTS Of 964 patients included in the 30-d analysis without prior PPM,19.6%required PPM post-TAVR.The mean age of patients was 80.9±8.7 years.42.1%were female.Of 657 patients included in the 1-year analysis,the mean age of the patients was 80.7±8.2.Of those,42.6%of patients were female and 26.7%required PPM at 1-year post-TAVR.The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model(0.66 and 0.72)was superior to that of the PPM risk score(0.55 and 0.54)with a P value<0.001.CONCLUSION The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.展开更多
文摘Coarctation of the aorta(CoA)is a relatively common congenital cardiac defect often causing few symptoms and therefore can be challenging to diagnose.The hallmark finding on physical examination is upper extremity hypertension,and for this reason,CoA should be considered in any young hypertensive patient,justifying measurement of lower extremity blood pressure at least once in these individuals.The presence of a significant pressure gradient between the arms and legs is highly suggestive of the diagnosis.Early diagnosis and treatment are important as long-term data consistently demonstrate that patients with CoA have a reduced life expectancy and increased risk of cardiovascular complications.Surgical repair has traditionally been the mainstay of therapy for correction,although advances in endovascular technology with covered stents or stent grafts permit nonsurgical approaches for the management of older children and adults with native CoA and complications.Persistent hypertension and vascular dysfunction can lead to an increased risk of coronary disease,which,remains the greatest cause of long-term mortality.Thus,blood pressure control and periodic reassessment with transthoracic echocardiography and threedimensional imaging(computed tomography or cardiac magnetic resonance)for should be performed regularly as cardiovascular complications may occur decades after the intervention.
基金funded by Mayo Clinic Arizona Cardiovascular Clinical Research Center (MCA CV CRC)
文摘BACKGROUND Atrioventricular block requiring permanent pacemaker(PPM)implantation is an important complication of transcatheter aortic valve replacement(TAVR).Application of machine learning could potentially be used to predict preprocedural risk for PPM.AIM To apply machine learning to be used to predict pre-procedural risk for PPM.METHODS A retrospective study of 1200 patients who underwent TAVR(January 2014-December 2017)was performed.964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis.After the exclusion of variables with near-zero variance or≥50%missing data,167 variables were included in the random forest gradient boosting algorithm(GBM)optimized using 5-fold cross-validations repeated 10 times.The receiver operator curve(ROC)for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year.RESULTS Of 964 patients included in the 30-d analysis without prior PPM,19.6%required PPM post-TAVR.The mean age of patients was 80.9±8.7 years.42.1%were female.Of 657 patients included in the 1-year analysis,the mean age of the patients was 80.7±8.2.Of those,42.6%of patients were female and 26.7%required PPM at 1-year post-TAVR.The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model(0.66 and 0.72)was superior to that of the PPM risk score(0.55 and 0.54)with a P value<0.001.CONCLUSION The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.
