scholarly journals Dynamic predictive accuracy of electrocardiographic biomarkers of sudden cardiac death within a survival framework: the Atherosclerosis Risk in Communities (ARIC) study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Erick A. Perez-Alday ◽  
Aron Bender ◽  
David German ◽  
Srini V. Mukundan ◽  
Christopher Hamilton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Short Term ◽  
Clinical Risk ◽  
Atherosclerosis Risk

Abstract Background The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Methods Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63–1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37–2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526–0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303–0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526–0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515–0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668–0.753 for ECG recorded within 10 years before SCD). Conclusion Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

scholarly journals Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study

2019 ◽  
Author(s):  
Erick Perez-Alday ◽  
Aron Bender ◽  
David German ◽  
Srini Mukundan ◽  
Christopher Hamilton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Short Term ◽  
Clinical Risk ◽  
Atherosclerosis Risk

Abstract Background—The risk of sudden cardiac death (SCD) is known to be dynamic. However, an accuracy of a dynamic SCD prediction is unknown. We aimed to measure dynamic predictive accuracy of ECG biomarkers of SCD and competing non-SCD. Methods—Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n=15,716; 55% female, 73% white, age 54.2±5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics were measured. Adjudicated SCD was the primary outcome; non-SCD was competing outcome. Time-dependent area under the (receiver operating characteristic) curve (AUC) analysis was performed to assess prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years, using survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results—Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1,000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. Short-term, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD. Short-term, upward and more likely forward–directed SVG vector predicted SCD, whereas backward-directed SVG predicted non-SCD. Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors (18% SCD events reclassified up). Long-term, backward–directed SVG predicted both SCD and non-SCD. Conclusion—Short-term predictors of SCD, non-SCD, and biomarkers of long-term SCD risk differed, reflecting differences in transient vs. persistent SCD substrates.

scholarly journals Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study

2019 ◽  
Author(s):  
Erick Perez-Alday ◽  
Aron Bender ◽  
David German ◽  
Srini Mukundan ◽  
Christopher Hamilton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Short Term ◽  
Clinical Risk ◽  
Atherosclerosis Risk

Abstract Background—The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Methods—Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n=15,716; 55% female, 73% white, age 54.2±5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results—Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1,000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303-0.75). SVG elevation more accurately predicted SCD if ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526-0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515-0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% SCD events were reclassified from low or intermediate risk to a high-risk category. Long-term, QRS-T angle was the strongest predictor of SCD (AUC 0.710; 95%CI 0.668-0.753 for ECG recorded within 10 years before SCD). Conclusion—Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

scholarly journals Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study

2019 ◽  
Author(s):  
Erick Perez-Alday ◽  
Aron Bender ◽  
David German ◽  
Srini Mukundan ◽  
Christopher Hamilton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Short Term ◽  
Clinical Risk ◽  
Atherosclerosis Risk

Abstract Background—The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Methods—Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n=15,716; 55% female, 73% white, age 54.2±5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results—Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1,000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303-0.75). SVG elevation more accurately predicted SCD if ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526-0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515-0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% SCD events were reclassified from low or intermediate risk to a high-risk category. Long-term, QRS-T angle was the strongest predictor of SCD (AUC 0.710; 95%CI 0.668-0.753 for ECG recorded within 10 years before SCD). Conclusion—Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

scholarly journals Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study

2019 ◽  
Author(s):  
Erick A. Perez-Alday ◽  
Aron Bender ◽  
Yin Li-Pershing ◽  
David German ◽  
Tuan Mai ◽  
...  
Keyword(s):  
Heart Disease ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Structural Heart Disease ◽  
Expiration Date ◽  
Short Term ◽  
Atherosclerosis Risk

AbstractBackgroundThe risk of sudden cardiac death (SCD) is known to be dynamic. However, an accuracy of a dynamic SCD prediction, and “expiration date” of ECG biomarkers is unknown. Our goal was to measure dynamic predictive accuracy of ECG biomarkers of SCD and competing outcomes.MethodsAtherosclerosis Risk In Community study participants with analyzable digital ECGs were included (n=15,768; 55% female, 73% white, age 54.2±5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity (GEH) and traditional ECG metrics were measured. Adjudicated SCD served as the primary outcome; non-sudden cardiac death served as competing outcome. Time-dependent area under the (receiver operating characteristic) curve (AUC) analysis was performed to assess prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years, using survival analysis framework.ResultsOver a median 24.4 y follow-up, there were 581 SCDs (incidence 1.77 (95%CI 1.63-1.92)/1,000 person-years), and 838 nonSCDs [2.55 (95%CI 2.39-2.73)]. Resting heart rate was the strongest (AUC 0.930) short-term (3-month) non-specific SCD predictor, whereas spatial peak QRS-T angle predicted specifically SCD 15 years after ECG recording (AUC 0.719). QRS duration (AUC 0.885) and QTc (AUC 0.711) short-term predicted advanced structural heart disease better than SCD. “Expiration date” for most ECG biomarkers was two years after ECG recording. GEH significantly improved reclassification of SCD risk beyond age, sex, race, diabetes, hypertension, coronary heart disease and stroke.ConclusionShort-term predictors of SCD, nonSCD, and biomarkers of long-term SCD risk differed, reflecting differences in transient vs. persistent SCD substrates.

Long-Term Follow-Up of Patients With Isolated Side Branch Coronary Artery Disease

Angiology ◽  
2021 ◽  
pp. 000331972110280
Author(s):  
Sukru Arslan ◽  
Ahmet Yildiz ◽  
Okay Abaci ◽  
Urfan Jafarov ◽  
Servet Batit ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Vessel Diameter ◽  
Side Branch ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Artery Disease

The data with respect to stable coronary artery disease (SCAD) are mainly confined to main vessel disease. However, there is a lack of information and long-term outcomes regarding isolated side branch disease. This study aimed to evaluate long-term major adverse cardiac and cerebrovascular events (MACCEs) in patients with isolated side branch coronary artery disease (CAD). A total of 437 patients with isolated side branch SCAD were included. After a median follow-up of 38 months, the overall MACCE and all-cause mortality rates were 14.6% and 5.9%, respectively. Among angiographic features, 68.2% of patients had diagonal artery and 82.2% had ostial lesions. In 28.8% of patients, the vessel diameter was ≥2.75 mm. According to the American College of Cardiology lesion classification, 84.2% of patients had either class B or C lesions. Age, ostial lesions, glycated hemoglobin A1c, and neutrophil levels were independent predictors of MACCE. On the other hand, side branch location, vessel diameter, and lesion complexity did not affect outcomes. Clinical risk factors seem to have a greater impact on MACCE rather than lesion morphology. Therefore, the treatment of clinical risk factors is of paramount importance in these patients.

Long-term Clinical Follow-up of Japanese Heart Failure Patients Received ICD Therapy for Primary Prevention of Sudden Cardiac Death

2008 ◽  
Vol 14 (7) ◽  
pp. S140-S141
Author(s):  
Kenji Ando ◽  
Yoshimitsu Soga ◽  
Masahiko Goya ◽  
Shinichi Shirai ◽  
Shinya Nagayama ◽  
...  
Keyword(s):  
Heart Failure ◽  
Primary Prevention ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Icd Therapy ◽  
Heart Failure Patients

scholarly journals Vital exhaustion and sudden cardiac death in the Atherosclerosis Risk in Communities Study

Heart ◽  
2017 ◽  
Vol 104 (5) ◽  
pp. 423-429 ◽  
Author(s):  
Brittany M Bogle ◽  
Nona Sotoodehnia ◽  
Anna M Kucharska-Newton ◽  
Wayne D Rosamond
Keyword(s):  
Risk Factors ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Disease Risk ◽  
Ventricular Tachyarrhythmia ◽  
Atherosclerosis Risk In Communities ◽  
Vital Exhaustion ◽  
Increased Risk ◽  
Atherosclerosis Risk ◽  
Aric Study

ObjectiveVital exhaustion (VE), a construct defined as lack of energy, increased fatigue and irritability, and feelings of demoralisation, has been associated with cardiovascular events. We sought to examine the relation between VE and sudden cardiac death (SCD) in the Atherosclerosis Risk in Communities (ARIC) Study.MethodsThe ARIC Study is a predominately biracial cohort of men and women, aged 45–64 at baseline, initiated in 1987 through random sampling in four US communities. VE was measured using the Maastricht questionnaire between 1990 and 1992 among 13 923 individuals. Cox proportional hazards models were used to examine the hazard of out-of-hospital SCD across tertiles of VE scores.ResultsThrough 2012, 457 SCD cases, defined as a sudden pulseless condition presumed due to a ventricular tachyarrhythmia in a previously stable individual, were identified in ARIC by physician record review. Adjusting for age, sex and race/centre, participants in the highest VE tertile had an increased risk of SCD (HR 1.48, 95% CI 1.17 to 1.87), but these findings did not remain significant after adjustment for established cardiovascular disease risk factors (HR 0.94, 95% CI 0.73 to 1.20).ConclusionsAmong participants of the ARIC study, VE was not associated with an increased risk for SCD after adjustment for cardiovascular risk factors.

scholarly journals Influence of lifestyle risk factors on work ability and sick leave in a general working population in Norway: a 5-year longitudinal study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e045678
Author(s):  
Marit Müller De Bortoli ◽  
Inger M. Oellingrath ◽  
Anne Kristin Moeller Fell ◽  
Alex Burdorf ◽  
Suzan J. W. Robroek
Keyword(s):  
Risk Factors ◽  
Longitudinal Study ◽  
Sick Leave ◽  
Work Ability ◽  
Working Population ◽  
Short Term ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

ObjectivesThe aim of this study is to assess (1) whether lifestyle risk factors are related to work ability and sick leave in a general working population over time, and (2) these associations within specific disease groups (ie, respiratory diseases, cardiovascular disease and diabetes, and mental illness).SettingTelemark county, in the south-eastern part of Norway.DesignLongitudinal study with 5 years follow-up.ParticipantsThe Telemark study is a longitudinal study of the general working population in Telemark county, Norway, aged 16 to 50 years at baseline in 2013 (n=7952) and after 5-year follow-up.Outcome measureSelf-reported information on work ability (moderate and poor) and sick leave (short-term and long-term) was assessed at baseline, and during a 5-year follow-up.ResultsObesity (OR=1.64, 95% CI: 1.32 to 2.05) and smoking (OR=1.62, 95% CI: 1.35 to 1.96) were associated with long-term sick leave and, less strongly, with short-term sick leave. An unhealthy diet (OR=1.57, 95% CI: 1.01 to 2.43), and smoking (OR=1.67, 95% CI: 1.24 to 2.25) were associated with poor work ability and, to a smaller extent, with moderate work ability. A higher lifestyle risk score was associated with both sick leave and reduced work ability. Only few associations were found between unhealthy lifestyle factors and sick leave or reduced work ability within disease groups.ConclusionLifestyle risk factors were associated with sick leave and reduced work ability. To evaluate these associations further, studies assessing the effect of lifestyle interventions on sick leave and work ability are needed.

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