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Automated detection of proximal right coronary artery occlusion in ST-elevation myocardial infarction
744
Poster Session 1 / Journal of Electrocardiology 44 (2011) 742–747
Background: End-stage renal disease (ESRD) is a costly and disabling condition that is associated with a high mortality rate (230/1000 patients per year). Today, there are approximately half a million people in the United States with ESRD on hemodialysis, and the annual mortality rate among these patients is close to 25%. Cardiac disease is implicated in as many as 44% of these deaths. Among those, cardiac arrhythmias represent 61% of all cardiac deaths. The objective of this study is to test the hypothesis that electrocardiographic (ECG) parameters measuring ventricular instability and repolarization heterogeneity could correlate with cardiac death when monitored during or after hemodialysis session (HS). Method: We enrolled patients with ESRD with high risk of cardiac arrhythmias and sudden cardiac death over a period of 1 year. The inclusion criteria were age older than 40 years and confirmed history of hypertension requiring treatment or confirmed history of diabetes requiring treatment. Twelve-lead Holter ECGs were recorded for 48 hours starting 30 minutes before the onset of the HS. The ECG measurements include VPCs frequency, QTc, T-wave complexity, and QRS-T angle, among others. We used linear mixed-effect models with autoregressive covariance structure to investigate the differences in ECG trends during and after the HS between the groups. Results: Forty-two patients with ESRD were enrolled and survived the 13-month follow-up period (age, 63 ± 12 years; EF, 59% ± 15%; 25 women), whereas 8 enrolled patients did not (age, 60 ± 12 years; EF, 58% ± 22%; 5 women). No differences in dialysis methods and patients' electrolytes were found, but the duration of the HS was shorter in nonsurvivors (203 ± 24 vs 240 ± 29 minutes, P = .023). Frequency of ventricular ectopic beats was significantly higher during the second hours of the dialysis in patients who did not survive (26 ± 20 vs 3 ± 14 VPCs per hour, P = .02); no statistical differences were found for other parameters during the HS. During the 48 hours after the HS, the nonsurvivor group had lower heart rate (R-R intervals, 855 ± 91 vs 775 ± 134 milliseconds; P = .01), increased T-wave complexity (0.29 ± 0.15 vs 0.23 ± 0.17, P = .05), and a trend toward less acute QRS-T angle (94° ± 38° vs 63° ± 45°, P = .09). Conclusions: More frequent dialysis-induced ventricular ectopy, lower heart rate, and increased T-wave complexity indicate increased risk of probability of mortality in patients with ESRD. These findings require confirmation in larger studies. doi:10.1016/j.jelectrocard.2011.09.012
Quantification of hospital cardiac monitoring alarms Richard Fidler CRNA, CRNP, MSN, David Pickham RN, PhD, Barbara J. Drew RN, PhD University of California, San Francisco, CA Cardiac monitors measure multiple physiologic parameters and audible alarms sound when algorithms detect changes in cardiac rhythm, ST segments, QT intervals, respiratory rate, SpO2, and other. “Alarm fatigue” occurs when staff are barraged by an excessive number of alarms that are false or do not require treatment. Numerous deaths have been reported because of alarm fatigue; either the alarm is ignored or, worse, alarm capabilities are disabled by the staff who find the noise bothersome. Study aims: We aimed to (1) analyze a large, real-world database of hospital monitoring data to identify the most common sources of alarms and (2) compare the number of critical alarms with the actual number of “code blue” events and deaths. Methods: We obtained cardiac monitor alarm “full disclosure” data from all consecutive patients (n = 1537) admitted to 1 of 6 adult hospital units with continuous electrocardiographic monitoring (154 beds) over a 2-month period in a large academic medical center. None of the units had alarms activated for ST-segment or QT interval monitoring. One surgical intensive care unit had a unit default setting to inhibit all alarms for premature ventricular contractions (PVCs). Results: A total of 318 009 alarms occurred (∼883 alarms per unit per day); 83% were electrocardiographic alarms. The largest single source of alarms was PVC alarms (Table). Two comparable size/type intensive care units with and without PVC alarm inhibition had 0 vs 23 671 PVC alarms. In total, there were 11 567 critical alarms (shaded rows, Table); however, many were
false alarms as evidenced by only 19 actual code blue events occurring in 17 patients, resulting in 7 deaths. Alarm type
No.
(%)
PVC alarms (paired, multiform, R-on-T, bigeminy, trigeminy, N10/min, etc) Multiple noncritical (↑↓HR, ↑↓QT, ↑↓ respiratory rate, missed beat, etc) Desaturation/low SpO2 Apnea Ventricular tachycardia or ventricular fibrillation Asystole Invasive/noninvasive blood pressure, CVP, PA pressures, ↑↓ preset limits Nurse call (wireless telemetry units) Total
120 732
38.0
136 908
43.1
43 447 4761 4557 2249 3631
13.7 1.5 1.4 0.7 1.1
1724 318 009
0.5 100
Conclusions: Without ST-segment or QT-interval alarms activated, we find a high alarm burden of nearly 900 alarms per unit per day. Most (78%) alarms are triggered by clinically insignificant events that are not “actionable” such as PVCs and heart rate changes. Although a large number of critical alarms occur (nearly 12 000 in a 2-month period), very few (b0.02%) signal a true life-threatening event. Unit default settings to disable nonactionable alarms may be a strategy to decrease the number of alarms and reduce alarm fatigue. doi:10.1016/j.jelectrocard.2011.09.013
Automated detection of proximal right coronary artery occlusion in ST-elevation myocardial infarction Richard E. Gregg a, Miquel Fiol b, Kjell C. Nikus c, Andrés Carrillo b, Simon Cheng-hao Chien a, James M. Lindauer a, Victoria Barbara d, Sophia H. Zhou a a Advanced Algorithm Research Center, Philips Healthcare, Thousand Oaks, CA b Hospital Son Dureta, Palma de Mallorca, Spain c Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland d Heart Institute, Long Beach Memorial Medical Center, Long Beach, CA Background: Classification of the occlusion location in the culprit artery during ST-elevation myocardial infarction (STEMI), especially as it relates to the amount of tissue downstream from the blocked segment, is important for risk stratification to optimize treatment. This study introduces a new algorithm for classifying occlusion location as proximal right, middle-distal right, or left circumflex (LCx) artery in inferior myocardial infarction. We compare the Philips automated algorithm to recently published culprit artery classification criteria. Methods: The new algorithm was developed and tested on a mixed set of electrocardiograms (ECGs) using logistic regression and a “leave-out” bootstrap technique with a random 70%/30% split between training and test subsets for each iteration of 200. The ECGs came from the ambulance or emergency departments of 2 hospitals using inclusion criteria of STEMI and angiogram confirmation of culprit artery and occlusion location (n = 132). All LCx locations (n = 31) were lumped together. The split between proximal right coronary artery (RCA) lesion location (n = 51) and middledistal location (n = 50) was defined as above the first large acute marginal. All patients met current STEMI criteria according to age and sex. For each bootstrap iteration, 6 from a total of 12 ECG features based on ST deviation were selected for use in the logistic regression classifier using forward stepwise feature selection. The final classifier was the average of the logistic regression coefficients across the bootstrap iterations. The table below shows a comparison of the logistic regression classifier and recently published criteria for classifying occlusion location (Fiol 2008). Results: See the following table for sensitivity and specificity of the Philips automated algorithm and recently published criteria for
Poster Session 1 / Journal of Electrocardiology 44 (2011) 742–747 classification of proximal RCA occlusion vs LCx and middle-distal RCA occlusion lumped together. Algorithm
Sensitivity (%)
Specificity (%)
Philips Fiol 2008
73 69
84 57
The difference in specificity is statistically significant. Conclusion: Automated discrimination of proximal RCA lesion location from LCx or middle-distal RCA using the new logistic regression classifier shows improvement over recently published criteria. Automated identification of proximal RCA occlusion could speed up risk stratification of patients with STEMI. doi:10.1016/j.jelectrocard.2011.09.014
Mirror leads revisited: a simulation study D. Guldenring a, D.D. Finlay a, C.D. Nugent a, M.P. Donnelly a, P.M. van Dam b a University of Ulster, Belfast, Northern Ireland, UK b Radboud University Medical Center, Nijmegen, the Netherlands Introduction: A mirror lead is one that has an identical but inverse appearance to a particular lead somewhere else on the body surface. Use of mirror leads has been suggested as a means to increase the low sensitivity of the ST-elevation myocardial infarction criteria. In this study, we illustrate that mirror leads that are identified by waveform similarity measures may be built up from components that are from different anatomical regions as their “nonmirror” counterparts. Methods: Our research is based on simulations that are performed on the realistically shaped piecewise homogenous human torso model that is used in ECGSIM (stITPro, Nijmegen, NL). Every lead on the body surface is calculated by a weighted sum overall simulated cardiac potentials. The weights (or transformation coefficients) for this calculation are based on the geometry and the conductivity of the torso model. Firstly, mirror leads of the precordial leads were identified by waveform similarity. A lead/mirror lead pair was considered to be the 2 leads with the most negative Pearson correlation coefficient (CC). This calculation was performed for a normal electrocardiogram (ECG) and for 4 different ischemic ECGs (transmural anterior ischemia, transmural anterior septal ischemia, transmural lateral ischemia, and transmural posterior ischemia). All simulated ischemic regions were approximately 3 cm in diameter. Based on these simulations, 5 lead/mirror lead pairs (1 for each simulated ECG) were identified for each of the precordial leads. Secondly, mirror leads of the precordial leads were identified by transformation coefficients. This method considered the 2 leads with the most negative CC over their associated transformation coefficients as a lead/mirror lead pair. The rationale behind this method is grounded on the following consideration: a mirror lead must contain the same components
Table 1 Median CCs for a lead/mirror lead pair identified by waveform similarity calculated over the 5 simulated ECGs (med_CC_WS), CCs for a lead/mirror lead pair identified by transformation coefficients (CC_TC), minimum and maximum Euclidean distance between the 5 mirror lead positions based on the waveform similarity method (5 locations for each precordial lead, 1 for each simulated ECG), and those obtained via the transformation coefficients
med_CC_WS CC_TS edmin (cm) edmax (cm)
V1
V2
V3
V4
V5
V6
−0.9323 −0.5889 4.12 12.81
−0.9160 −0.4698 3.70 16.41
−0.9429 −0.4541 3.73 21.12
−0.9785 −0.6132 5.55 18.43
−0.9918 −0.7829 6.72 17.12
−0.9895 −0.8343 6.85 27.61
edmin indicates minimum Euclidean distance; edmax, maximum Euclidean distance.
745
(although in an opposite direction) and in equal proportions as its nonmirror counterpart. Results: Table 1 details the results of the previously described simulations. Conclusion: Higher CCs achieved by the waveform similarity-based method do not indicate superiority of this method. The high correlations observed are caused by nonmirror components that are of similar timing and shape as mirror components; however, they are from different anatomical regions. A pathologic change that alters the heart's depolarization and repolarization will therefore affect the lead and its mirror differently. The observed movement of the mirror lead location is a result of this effect. doi:10.1016/j.jelectrocard.2011.09.015
Comparison of beat-to-beat 3-dimensional electrocardiographic variability in healthy subjects and patients with structural heart disease and systolic dysfunction Lichy Han, Larisa G. Tereshchenko The Johns Hopkins University, Baltimore, MD Introduction: Previous experiments have shown that temporal variability of repolarization is markedly different in patients with structural heart disease and systolic dysfunction that are at risk for ventricular arrhythmia (VA). We previously reported that our novel marker T/R peaks cloud volumes ratio, which was shown to predict VA in patients with structural heart disease, but has not been studied in a healthy population. Concurrently, it has been shown that large spatial QRS-T angle is also predictive of VA, although its variability has not been explored. In this study, we further investigate a 3-dimensional (3D) approach in the assessment of temporal beat-to-beat variability of cardiac signals in patients with structural heart disease in comparison with healthy subjects. Methods: Surface orthogonal electrocardiograms were recorded for 5 minutes at rest in 523 patients with structural heart disease (age, 59.8 ± 12.5 years; ischemic cardiomyopathy, 285 [55%]; EF, 22.2% ± 8.7%) and 11 minutes at rest in 168 healthy subjects. Only sinus beats were eligible for analysis, which was performed using customized MATLAB (MathWorks, Natick, MA) software. Peaks of R-loop and T-loop were detected for each beat in 3D, and the QRS-T spatial angle, T to T’ angle, and convex hull volumes for R and T-loops were also calculated. T/R peaks cloud volumes ratio was calculated after adjusting for the number of beats, and the root mean square successive difference (rMSSD) of the QRS-T and T to T’ spatial angles were calculated. Results: All statistics were calculated using the Wilcoxon rank sum test. Spatial QRS-T angle was significantly larger in patients with structural heart disease vs healthy subjects, as shown in previous experiments (median [interquartile range {IQR}], 150.0 [128.1-164.2] vs 49.0 [29.3-67.3]; P b .0001). T to T’ angle was also significantly larger in patients with structural heart disease (6.4 [4.0-11.7] vs 3.0 [2.2-4.4], P b .0001). In addition, adjusted by the number of analyzed beats, T/R convex hull volume ratio was significantly larger in patients with structural heart disease (0.003 [0.0010.007] vs 0.001 [0.0006-0.002], P b .0001). Patients with structural heart disease and systolic dysfunction had significantly higher QRS-T angle rMSSD (6.1 [3.7-9.9] vs 4.2 [2.9-5.5], P b .0001) and T to T’ angle rMSSD (4.8 [2.9-8.8] vs 2.5 [1.7-3.6], P b .0001). Conclusion: Patients with structural heart disease and systolic dysfunction are characterized by significantly increased temporal repolarization lability as measured by beat-to-beat 3D approach in comparison with healthy subjects. doi:10.1016/j.jelectrocard.2011.09.016
Estimated lower limit of the reference value of QT interval in healthy young Japanese men using the bootstrap method Osamu Hemmi PhD a ,c, Hideo Miyahara MD, PhD b, Hiroshi Goto PhD b ,c, Noriaki Ikeda PhD c, Noritaka Mamorita PhD c ,d, Akihiro Takeuchi MD, PhD c, Tetsuya Komatsuzaki MD e
Poster Session 1 / Journal of Electrocardiology 44 (2011) 742–747
Background: End-stage renal disease (ESRD) is a costly and disabling condition that is associated with a high mortality rate (230/1000 patients per year). Today, there are approximately half a million people in the United States with ESRD on hemodialysis, and the annual mortality rate among these patients is close to 25%. Cardiac disease is implicated in as many as 44% of these deaths. Among those, cardiac arrhythmias represent 61% of all cardiac deaths. The objective of this study is to test the hypothesis that electrocardiographic (ECG) parameters measuring ventricular instability and repolarization heterogeneity could correlate with cardiac death when monitored during or after hemodialysis session (HS). Method: We enrolled patients with ESRD with high risk of cardiac arrhythmias and sudden cardiac death over a period of 1 year. The inclusion criteria were age older than 40 years and confirmed history of hypertension requiring treatment or confirmed history of diabetes requiring treatment. Twelve-lead Holter ECGs were recorded for 48 hours starting 30 minutes before the onset of the HS. The ECG measurements include VPCs frequency, QTc, T-wave complexity, and QRS-T angle, among others. We used linear mixed-effect models with autoregressive covariance structure to investigate the differences in ECG trends during and after the HS between the groups. Results: Forty-two patients with ESRD were enrolled and survived the 13-month follow-up period (age, 63 ± 12 years; EF, 59% ± 15%; 25 women), whereas 8 enrolled patients did not (age, 60 ± 12 years; EF, 58% ± 22%; 5 women). No differences in dialysis methods and patients' electrolytes were found, but the duration of the HS was shorter in nonsurvivors (203 ± 24 vs 240 ± 29 minutes, P = .023). Frequency of ventricular ectopic beats was significantly higher during the second hours of the dialysis in patients who did not survive (26 ± 20 vs 3 ± 14 VPCs per hour, P = .02); no statistical differences were found for other parameters during the HS. During the 48 hours after the HS, the nonsurvivor group had lower heart rate (R-R intervals, 855 ± 91 vs 775 ± 134 milliseconds; P = .01), increased T-wave complexity (0.29 ± 0.15 vs 0.23 ± 0.17, P = .05), and a trend toward less acute QRS-T angle (94° ± 38° vs 63° ± 45°, P = .09). Conclusions: More frequent dialysis-induced ventricular ectopy, lower heart rate, and increased T-wave complexity indicate increased risk of probability of mortality in patients with ESRD. These findings require confirmation in larger studies. doi:10.1016/j.jelectrocard.2011.09.012
Quantification of hospital cardiac monitoring alarms Richard Fidler CRNA, CRNP, MSN, David Pickham RN, PhD, Barbara J. Drew RN, PhD University of California, San Francisco, CA Cardiac monitors measure multiple physiologic parameters and audible alarms sound when algorithms detect changes in cardiac rhythm, ST segments, QT intervals, respiratory rate, SpO2, and other. “Alarm fatigue” occurs when staff are barraged by an excessive number of alarms that are false or do not require treatment. Numerous deaths have been reported because of alarm fatigue; either the alarm is ignored or, worse, alarm capabilities are disabled by the staff who find the noise bothersome. Study aims: We aimed to (1) analyze a large, real-world database of hospital monitoring data to identify the most common sources of alarms and (2) compare the number of critical alarms with the actual number of “code blue” events and deaths. Methods: We obtained cardiac monitor alarm “full disclosure” data from all consecutive patients (n = 1537) admitted to 1 of 6 adult hospital units with continuous electrocardiographic monitoring (154 beds) over a 2-month period in a large academic medical center. None of the units had alarms activated for ST-segment or QT interval monitoring. One surgical intensive care unit had a unit default setting to inhibit all alarms for premature ventricular contractions (PVCs). Results: A total of 318 009 alarms occurred (∼883 alarms per unit per day); 83% were electrocardiographic alarms. The largest single source of alarms was PVC alarms (Table). Two comparable size/type intensive care units with and without PVC alarm inhibition had 0 vs 23 671 PVC alarms. In total, there were 11 567 critical alarms (shaded rows, Table); however, many were
false alarms as evidenced by only 19 actual code blue events occurring in 17 patients, resulting in 7 deaths. Alarm type
No.
(%)
PVC alarms (paired, multiform, R-on-T, bigeminy, trigeminy, N10/min, etc) Multiple noncritical (↑↓HR, ↑↓QT, ↑↓ respiratory rate, missed beat, etc) Desaturation/low SpO2 Apnea Ventricular tachycardia or ventricular fibrillation Asystole Invasive/noninvasive blood pressure, CVP, PA pressures, ↑↓ preset limits Nurse call (wireless telemetry units) Total
120 732
38.0
136 908
43.1
43 447 4761 4557 2249 3631
13.7 1.5 1.4 0.7 1.1
1724 318 009
0.5 100
Conclusions: Without ST-segment or QT-interval alarms activated, we find a high alarm burden of nearly 900 alarms per unit per day. Most (78%) alarms are triggered by clinically insignificant events that are not “actionable” such as PVCs and heart rate changes. Although a large number of critical alarms occur (nearly 12 000 in a 2-month period), very few (b0.02%) signal a true life-threatening event. Unit default settings to disable nonactionable alarms may be a strategy to decrease the number of alarms and reduce alarm fatigue. doi:10.1016/j.jelectrocard.2011.09.013
Automated detection of proximal right coronary artery occlusion in ST-elevation myocardial infarction Richard E. Gregg a, Miquel Fiol b, Kjell C. Nikus c, Andrés Carrillo b, Simon Cheng-hao Chien a, James M. Lindauer a, Victoria Barbara d, Sophia H. Zhou a a Advanced Algorithm Research Center, Philips Healthcare, Thousand Oaks, CA b Hospital Son Dureta, Palma de Mallorca, Spain c Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland d Heart Institute, Long Beach Memorial Medical Center, Long Beach, CA Background: Classification of the occlusion location in the culprit artery during ST-elevation myocardial infarction (STEMI), especially as it relates to the amount of tissue downstream from the blocked segment, is important for risk stratification to optimize treatment. This study introduces a new algorithm for classifying occlusion location as proximal right, middle-distal right, or left circumflex (LCx) artery in inferior myocardial infarction. We compare the Philips automated algorithm to recently published culprit artery classification criteria. Methods: The new algorithm was developed and tested on a mixed set of electrocardiograms (ECGs) using logistic regression and a “leave-out” bootstrap technique with a random 70%/30% split between training and test subsets for each iteration of 200. The ECGs came from the ambulance or emergency departments of 2 hospitals using inclusion criteria of STEMI and angiogram confirmation of culprit artery and occlusion location (n = 132). All LCx locations (n = 31) were lumped together. The split between proximal right coronary artery (RCA) lesion location (n = 51) and middledistal location (n = 50) was defined as above the first large acute marginal. All patients met current STEMI criteria according to age and sex. For each bootstrap iteration, 6 from a total of 12 ECG features based on ST deviation were selected for use in the logistic regression classifier using forward stepwise feature selection. The final classifier was the average of the logistic regression coefficients across the bootstrap iterations. The table below shows a comparison of the logistic regression classifier and recently published criteria for classifying occlusion location (Fiol 2008). Results: See the following table for sensitivity and specificity of the Philips automated algorithm and recently published criteria for
Poster Session 1 / Journal of Electrocardiology 44 (2011) 742–747 classification of proximal RCA occlusion vs LCx and middle-distal RCA occlusion lumped together. Algorithm
Sensitivity (%)
Specificity (%)
Philips Fiol 2008
73 69
84 57
The difference in specificity is statistically significant. Conclusion: Automated discrimination of proximal RCA lesion location from LCx or middle-distal RCA using the new logistic regression classifier shows improvement over recently published criteria. Automated identification of proximal RCA occlusion could speed up risk stratification of patients with STEMI. doi:10.1016/j.jelectrocard.2011.09.014
Mirror leads revisited: a simulation study D. Guldenring a, D.D. Finlay a, C.D. Nugent a, M.P. Donnelly a, P.M. van Dam b a University of Ulster, Belfast, Northern Ireland, UK b Radboud University Medical Center, Nijmegen, the Netherlands Introduction: A mirror lead is one that has an identical but inverse appearance to a particular lead somewhere else on the body surface. Use of mirror leads has been suggested as a means to increase the low sensitivity of the ST-elevation myocardial infarction criteria. In this study, we illustrate that mirror leads that are identified by waveform similarity measures may be built up from components that are from different anatomical regions as their “nonmirror” counterparts. Methods: Our research is based on simulations that are performed on the realistically shaped piecewise homogenous human torso model that is used in ECGSIM (stITPro, Nijmegen, NL). Every lead on the body surface is calculated by a weighted sum overall simulated cardiac potentials. The weights (or transformation coefficients) for this calculation are based on the geometry and the conductivity of the torso model. Firstly, mirror leads of the precordial leads were identified by waveform similarity. A lead/mirror lead pair was considered to be the 2 leads with the most negative Pearson correlation coefficient (CC). This calculation was performed for a normal electrocardiogram (ECG) and for 4 different ischemic ECGs (transmural anterior ischemia, transmural anterior septal ischemia, transmural lateral ischemia, and transmural posterior ischemia). All simulated ischemic regions were approximately 3 cm in diameter. Based on these simulations, 5 lead/mirror lead pairs (1 for each simulated ECG) were identified for each of the precordial leads. Secondly, mirror leads of the precordial leads were identified by transformation coefficients. This method considered the 2 leads with the most negative CC over their associated transformation coefficients as a lead/mirror lead pair. The rationale behind this method is grounded on the following consideration: a mirror lead must contain the same components
Table 1 Median CCs for a lead/mirror lead pair identified by waveform similarity calculated over the 5 simulated ECGs (med_CC_WS), CCs for a lead/mirror lead pair identified by transformation coefficients (CC_TC), minimum and maximum Euclidean distance between the 5 mirror lead positions based on the waveform similarity method (5 locations for each precordial lead, 1 for each simulated ECG), and those obtained via the transformation coefficients
med_CC_WS CC_TS edmin (cm) edmax (cm)
V1
V2
V3
V4
V5
V6
−0.9323 −0.5889 4.12 12.81
−0.9160 −0.4698 3.70 16.41
−0.9429 −0.4541 3.73 21.12
−0.9785 −0.6132 5.55 18.43
−0.9918 −0.7829 6.72 17.12
−0.9895 −0.8343 6.85 27.61
edmin indicates minimum Euclidean distance; edmax, maximum Euclidean distance.
745
(although in an opposite direction) and in equal proportions as its nonmirror counterpart. Results: Table 1 details the results of the previously described simulations. Conclusion: Higher CCs achieved by the waveform similarity-based method do not indicate superiority of this method. The high correlations observed are caused by nonmirror components that are of similar timing and shape as mirror components; however, they are from different anatomical regions. A pathologic change that alters the heart's depolarization and repolarization will therefore affect the lead and its mirror differently. The observed movement of the mirror lead location is a result of this effect. doi:10.1016/j.jelectrocard.2011.09.015
Comparison of beat-to-beat 3-dimensional electrocardiographic variability in healthy subjects and patients with structural heart disease and systolic dysfunction Lichy Han, Larisa G. Tereshchenko The Johns Hopkins University, Baltimore, MD Introduction: Previous experiments have shown that temporal variability of repolarization is markedly different in patients with structural heart disease and systolic dysfunction that are at risk for ventricular arrhythmia (VA). We previously reported that our novel marker T/R peaks cloud volumes ratio, which was shown to predict VA in patients with structural heart disease, but has not been studied in a healthy population. Concurrently, it has been shown that large spatial QRS-T angle is also predictive of VA, although its variability has not been explored. In this study, we further investigate a 3-dimensional (3D) approach in the assessment of temporal beat-to-beat variability of cardiac signals in patients with structural heart disease in comparison with healthy subjects. Methods: Surface orthogonal electrocardiograms were recorded for 5 minutes at rest in 523 patients with structural heart disease (age, 59.8 ± 12.5 years; ischemic cardiomyopathy, 285 [55%]; EF, 22.2% ± 8.7%) and 11 minutes at rest in 168 healthy subjects. Only sinus beats were eligible for analysis, which was performed using customized MATLAB (MathWorks, Natick, MA) software. Peaks of R-loop and T-loop were detected for each beat in 3D, and the QRS-T spatial angle, T to T’ angle, and convex hull volumes for R and T-loops were also calculated. T/R peaks cloud volumes ratio was calculated after adjusting for the number of beats, and the root mean square successive difference (rMSSD) of the QRS-T and T to T’ spatial angles were calculated. Results: All statistics were calculated using the Wilcoxon rank sum test. Spatial QRS-T angle was significantly larger in patients with structural heart disease vs healthy subjects, as shown in previous experiments (median [interquartile range {IQR}], 150.0 [128.1-164.2] vs 49.0 [29.3-67.3]; P b .0001). T to T’ angle was also significantly larger in patients with structural heart disease (6.4 [4.0-11.7] vs 3.0 [2.2-4.4], P b .0001). In addition, adjusted by the number of analyzed beats, T/R convex hull volume ratio was significantly larger in patients with structural heart disease (0.003 [0.0010.007] vs 0.001 [0.0006-0.002], P b .0001). Patients with structural heart disease and systolic dysfunction had significantly higher QRS-T angle rMSSD (6.1 [3.7-9.9] vs 4.2 [2.9-5.5], P b .0001) and T to T’ angle rMSSD (4.8 [2.9-8.8] vs 2.5 [1.7-3.6], P b .0001). Conclusion: Patients with structural heart disease and systolic dysfunction are characterized by significantly increased temporal repolarization lability as measured by beat-to-beat 3D approach in comparison with healthy subjects. doi:10.1016/j.jelectrocard.2011.09.016
Estimated lower limit of the reference value of QT interval in healthy young Japanese men using the bootstrap method Osamu Hemmi PhD a ,c, Hideo Miyahara MD, PhD b, Hiroshi Goto PhD b ,c, Noriaki Ikeda PhD c, Noritaka Mamorita PhD c ,d, Akihiro Takeuchi MD, PhD c, Tetsuya Komatsuzaki MD e