SYSTEMS AND METHODS FOR QUANTITATIVE ECG HETEROGENEITY-GUIDED OPTIMIZATION OF THERAPEUTIC EFFICACY OF IMPLANTABLE CARDIAC DEVICES

Disclosed herein are example methods and systems for predicting efficacy of pacemakers or cardiac resynchronization therapy (CRT) devices prior to implantation in patients based on electrocardiogram (ECG) heterogeneity analysis. A method of determining or predicting efficacy of implanting a pacemaker or cardiac resynchronization therapy (CRT) device in a patient includes receiving a first set of electrocardiogram (ECG) signals associated with the patients heart from spatially separated leads, analyzing data from the first set of ECG signals, quantifying a spatio-temporal heterogeneity of the first set of ECG signals based on the analysis, and determining or predicting efficacy of implanting the pacemaker or cardiac re-synchronization therapy (CRT) device in the patient based on the quantified spatio-temporal heterogeneity.

BACKGROUND OF THE INVENTION

Field of the Invention

Embodiments herein relate to systems and methods for predicting efficacy of pacemakers or cardiac resynchronization therapy (CRT) devices prior to implantation in patients based on electrocardiogram (ECG) heterogeneity analysis.

Background

Cardiac resynchronization therapy (CRT) is an important treatment in patients with advanced heart failure and left ventricular dyssynchrony caused by left bundle branch block (LBBB). CRT has been proven to relieve symptoms, increase functional capacity, and prolong life in many cases, but the response rates ranged from 32% to 91% depending on the criteria used and were even lower if the strong placebo effect observed in the control group in most studies of CRT efficacy was subtracted from the treatment effect.

Despite availability of useful qualitative predictors of mechanical response to

CRT, including no prior history of myocardial infarction (MI) and non-ischemic etiology of cardiomyopathy, quantitative predictors, whether related to electrical or mechanical dyssynchrony, are suboptimal. Given the high cost and persistently high rate of nonresponse or suboptimal response to cardiac resynchronization therapy (CRT), more reliable quantitative predictors of response are needed. The most commonly used electrocardiographic criterion, QRS complex duration >150 ms, performs more poorly than qualitative clinical predictors. Therefore, more reliable quantitative predictors that might be useful independently or in combination with clinical indicators are needed. Additionally, there is a need for methods for determining optimum positioning of devices in a reliable and enhanced manner.

SUMMARY OF THE INVENTION

Described herein are example methods and systems for quantitative ECG heterogeneity-guided optimization of therapeutic efficacy of implantable pacemaker and cardiac resynchronization therapy devices. Measurements and analysis of quantitative ECG heterogeneity may be utilized to improve the therapeutic efficacy of cardiac pacemakers and cardiac resynchronization therapy (CRT) devices in patients with heart failure. In particular, quantitative ECG heterogeneity may be utilized prior to implantation to determine which patients are likely to benefit from CRT. Quantitative ECG heterogeneity may also be utilized to guide placement of a lead in the coronary vein, as well as determining the optimum positioning of electrodes for improved stimulation efficacy.

In the embodiments presented herein, a method for determining or predicting efficacy of implanting a pacemaker or cardiac resynchronization therapy (CRT) device in a patient is described. The method includes receiving a first set of electrocardiogram (ECG) signals associated with the patient's heart from spatially separated leads, analyzing data from the first set of ECG signals, quantifying a spatio-temporal heterogeneity of the first set of ECG signals based on the analysis, and determining or predicting efficacy of implanting the pacemaker or cardiac resynchronization therapy (CRT) device in the patient based on the quantified spatio-temporal heterogeneity.

Further features and advantages, as well as the structure and operation of various embodiments, are described in detail below with reference to the accompanying drawings. It is noted that the specific embodiments described herein are not intended to be limiting. Such embodiments are presented herein for illustrative purposes only. Additional embodiments will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein.

DETAILED DESCRIPTION OF THE INVENTION

This specification discloses one or more embodiments that incorporate the features of this invention. The disclosed embodiment(s) merely exemplify the present invention. The scope of the present invention is not limited to the disclosed embodiment(s).

Before describing such embodiments in more detail, however, it is instructive to present an example environment in which embodiments of the present invention may be implemented.

FIG. 1illustrates a patient102that is attached to various leads of an ECG recording device, according to an embodiment. The leads may be used to monitor a standard12-lead ECG. In this example, six leads (leads104a-f) may be placed across the chest of patient102while four other leads (leads104g-j) are placed with two near the wrists and two near the ankles of patient102.

It should be understood that the exact placement of the leads is not intended to be limiting. For example, the two lower leads104iand104jmay be placed higher on the body, such as on the outer thighs. In another example, leads104gand104hare placed closer to the shoulders while leads104iand104jare placed closer to the hips of patient102. In still other examples, not all ten leads are required to be used in order to monitor ECG signals from patient102.

In an embodiment, signals are monitored from each of leads104a-jduring a standard 12-lead ECG recording. The resulting ECG signal may be analyzed over time to determine various health factors such as heart rate, strength of heart beat, and any indicators of abnormalities. However, changes in the various signals received amongst leads104a-jmay be very small and difficult to detect. Any trend in the changing signal amplitude for certain areas of the ECG morphology could be vital in predicting a patient's response to cardiac resynchronization therapy (CRT). For example, prediction of efficacy of pacemakers or CRT devices prior to implantation in a patient may be possible by observing trends in the R-wave heterogeneity, T-wave heterogeneity, P-wave heterogeneity and/or T-wave alternans from the monitored ECG signals. In particular, ECG heterogeneity provides a spatial measure of T-wave wave morphology across different lead locations, and this metric may be at the root of dyssynchrony of mechanical contraction. Quantifying alternans in ECG signals is described in further detail in U.S. Pat. No. 6,169,919, which is incorporated by reference herein in its entirety.

The challenge is to separate these biologically significant microvolt-level changes from the intrinsic differences in ECG morphology. In an embodiment, the technique employed herein utilizes a multi-lead ECG median-beat baseline for each lead, which allows for the determination of ECG residua by subtraction of the baseline from the collected ECG signals. These residua may be evaluated in association with R-wave and T-wave heterogeneity analysis and other parameters for heart arrhythmia prediction, myocardial ischemia assessment, or determination of coronary artery stenosis.

Ultimately, the implementation of embodiments described herein may lead to improved identification of individuals/patients who are less likely or more likely to benefit from CRT and survive and may serve as a guide to determining efficacy of therapy.

Quantitative ECG heterogeneity may be used prior to implantation to determine which patients are likely to benefit from CRT over standard right ventricular (RV) pacing. Patients with a lower range of T-wave heterogeneity (TWH) respond more favorably to CRT implantation than those with high levels of TWH. Quantitative ECG heterogeneity may further be used to guide placement of a lead in the coronary vein of a patient as well as to determine the optimum electrode to be used in a quadripolar electrode catheter. In some embodiments, TWH may be used to determine optimum stimulation parameters.

ECG-guided CRT may also be helpful in determining optimum positioning of His bundle pacemaker electrodes compared to RV pacemaker electrodes. In particular, accurate positioning improves stimulation efficacy so that the activation wave will more uniformly propagate throughout the conducting system, and mechanical function will be synchronized.

In some embodiments, quantitative ECG heterogeneity-guided methods for optimization may be implemented for real-time monitoring in an electrophysiology study lab. For example, the electrophysiology study may include monitoring stations (e.g., computing devices, computer systems, or the like) which are coupled to ECG recording devices (e.g., configured with the leads shown inFIG. 1) to record full12-lead ECGs of patients or individuals, as well as signals from cardiac catheters. In some embodiments, real-time monitoring of the quantified spatio-temporal heterogeneity may be visualized by an operator of the monitoring station on a user interface (e.g., a display, computer monitor, flat screen monitor, or the like).

In additional embodiments, methods for utilizing quantitative ECG heterogeneity to predict efficacy of pacemakers or CRT devices may be implemented in external stimulation units. For example, external stimulation units may be employed to optimize delivery of stimuli prior to implantation of a CRT or His bundle pacing device in a patient. These units may be equipped with software to provide a real-time readout of ECG heterogeneity. Once a predetermined or desired reduction in ECG heterogeneity is achieved using the external stimulation unit, the parameters and lead sites obtained from the heterogeneity readout data may be implemented in the implantable pacemaker or CRT device. In other words, the implantable pacemaker or CRT device may be implanted in a patient based on the parameters and lead sites identified during quantification of the ECG heterogeneity data.

Study of ECG Heterogeneity for Predicting CRT Benefit and Survival

Cardiac resynchronization therapy (CRT) is an important treatment in patients with advanced heart failure and left ventricular dyssynchrony caused by left bundle branch block (LBBB). CRT has been proven to relieve symptoms, increase functional capacity, and prolong life in many cases, but the response rates ranged from 32% to 91% depending on the criteria used and were even lower if the strong placebo effect observed in the control group in most studies of CRT efficacy was subtracted from the treatment effect.

Despite availability of useful qualitative predictors of mechanical response to

CRT, including no history of myocardial infarction or non-ischemic etiology of cardiomyopathy, quantitative predictors, whether related to electrical or mechanical dyssynchrony, may be suboptimal. The most commonly used electrocardiographic (ECG) criterion, QRS complex duration, may perform more poorly than qualitative clinical predictors. Therefore, more reliable quantitative predictors that might be useful independently or in combination with clinical indicators are desirable.

The present retrospective study tested the hypothesis that quantitative assessment of R-wave and T-wave heterogeneity (RWH and TWH, respectively) at pre-implantation could be employed both in predicting mechanical super-response to CRT and in assessing mortality risk. The scientific rationale underlying prediction of mechanical response is based on the close linkage between nonuniformities in excitation and contraction coupling in diseased myocardium and the recent finding that mechanical dyssynchrony as assessed by global longitudinal strain is significantly correlated with TWH. The inference for mortality risk prediction in this population derives from two recent studies. The first was the sizeable Health Survey 2000 study of 5600 subjects in which TWH predicted adjudicated SCD as well as total and cardiovascular mortality (Kenttä T V, et al 2016). The second study enrolled patients with cardiomyopathy from the institution in whom RWH and TWH in 12-lead ECG recordings were found to predict sustained ventricular arrhythmia, appropriate implantable cardioverter defibrillator (ICD) therapies, and arrhythmic death or cardiac arrest independent of age, sex, and left ventricular ejection fraction (LVEF) (Tan A Y, et al. 2017).

The present study involved direct comparison of RWH and TWH against the mainstay clinical ECG variable, QRS complex duration, for prediction of both mechanical super-response to CRT and survival.

Methods

Study Population

The inclusion criteria for this retrospective study were Class I and IIA ACC/AHA/HRS guideline-based indications for CRT device implantation (LBBB, QRS complex duration ≥120 ms, LVEF≥35% and New York Heart Association (NYHA) functional class≥II or non-LBBB, QRS≥150 ms, LVEF≤35%, NYHA functional class ≥III); echocardiograms before (average 4.1±1 months) and at one year after implantation; and ECGs before implantation and at least one of four post-implantation time points (1 day, 3 months, 6 months, and 1 year). Of the patients who received CRT devices from 2006 to 2018 at the institution, ECGs from all 155 patients who met these criteria were retrospectively analyzed. This investigation was approved as a medical records study by the Institutional Review Board of Beth Israel Deaconess Medical Center.

Left ventricular systolic dysfunction was categorized as being due to coronary artery disease (CAD) if there was regional wall motion abnormality and/or scar on echocardiography, cardiac magnetic resonance imaging, or nuclear stress imaging indicative of prior myocardial infarction, and any of the following criteria were fulfilled: (1) documented prior myocardial infarction, (2) >70% stenosis of a major epicardial coronary artery, (3) documented prior coronary artery bypass graft surgery, or (4) documented prior percutaneous coronary intervention. All other patients with left ventricular systolic dysfunction were considered to have a nonischemic etiology.

Super-responders (n=35) were defined as having a ≤20% increase in LVEF and/or ≥20% decrease in left ventricular end-systolic diameter (LVESD) across 1 year while non super-responders (n=120) did not meet these criteria. These criteria were based on the echocardiographic results obtained by Rickard et al (2010) in their investigation of potential predictors of super-response. See Rickard J, Kumbhani D J, Popovic Z, et al. Characterization of super-response to cardiac resynchronization Therapy. Heart Rhythm 2010;7:885-889. A subset analysis using a 5% LVEF cut off was also performed.

ECG and Echocardiographic Analyses

Interlead heterogeneity of depolarization and repolarization morphology was assessed from 10-sec 12-lead ECG recordings (GE Healthcare, Milwaukee Wis., USA) by an investigator blinded to clinical status and outcomes using second central moment analysis. This method quantifies variability about the mean morphology of ECGs from adjoining leads on a beat-to-beat basis. Specifically, after the signals are processed to filter noise and remove baseline wander, the software generates mean waveforms separately for the QRS complex and T waves (to include the J point and entire T wave) of adjoining precordial leads V1-3(RWHV1-3, TWHV1-3) and V4-6(RWHV4-6, TWHV4-6) and also including leads I and II (RWHV1-3LILII, RWHV4-6LILII, TWHV1-3LILII, TWHV4-6LILII). Use of the extended lead sets enhances the electrophysiologic field of view for ECG heterogeneity determination. This mean interlead morphology constitutes the first moment or central axis in the terminology of Newtonian physics. The second central moment, or mean-square deviation, was then determined to quantify the variability or splay about the mean morphology. Finally, the maximum square root of the second central moment was calculated to obtain the RWH and TWH values in microvolts. Using this analytic technique, ECG heterogeneity measurement is not unduly weighted by protracted termination or inflections in the waveforms, ST-segment changes, or presence of U waves, features that limit accurate dispersion measurement by conventional analyses. The maximum RWH and TWH levels for each patient were determined.

QRS complex duration was determined in the longest representative non-premature beat and the clinical criterion of ≥150 ms was used. All ECG parameters were analyzed by investigators blinded to the echocardiographic parameters and clinical characteristics.

Statistics

Statistical analysis was performed with XLSTAT (Addinsoft, Inc., New York, N.Y., USA) and GraphPad Prism (San Diego, Calif., USA). Data are reported as means±standard error of the mean. Statistical differences in quantitative measurements between super-responders and non super-responders were calculated using 2-tailed unpaired Student's t-test or Welch's t-test, chosen according to differences in variances calculated by F-test as well as by Mann-Whitney's test for non-parametric data. Differences in qualitative measurements were calculated using Fisher's exact test. Receiver-operating characteristic (ROC) curves were plotted for the ECG parameters, and the cutpoints for RWH, TWH, and QRS complex duration were chosen as the most optimized value, that is, the measurement level with the maximum sensitivity plus specificity. Logistic regression was used to calculate odds ratios, with multivariate analysis being used to adjust the ECG parameters for confounding by clinical parameters in which the difference between the groups had a p-value of <0.1. Two-way repeated measures analysis of variance (ANOVA) was used to analyze progression of ECG parameters over time in the two groups of patients. Kaplan-Meier curves for mortality were determined and statistically evaluated using the log-rank test.

RESULTS

Clinical Characteristics

The clinical characteristics of super-responders (N=35) and non super-responders (N=120) are presented in Table 1.

Non super-responders exhibited a significantly higher prevalence of CAD, history of myocardial infarction, and ischemic cardiomyopathy, which are well-established predictors of poor response to CRT. The p value for the difference in hypertension and use of beta blockers was <0.1; therefore, these criteria were included in the multi-variate analysis as well. Significant differences in characteristics after 1 year comparing super-responders to non super-responders included NYHA functional class (1.5±0.1 vs 1.9±0.1, respectively, p=0.01). The number of patients with 12-lead ECG recordings at each time point are presented in Table 2.

TABLE 2Number of patients with ECGs and each time pointPre-CRTDay361implantationaftermonthsmonthsyearSuper-Responders3524191920Non Super-Responders12083716567

Echocardiogram Parameters

There were no significant differences in the pre-implantation echocardiographic parameters when comparing the super-responders and non super-responders (Table 3). In post-treatment measurements, there were significant differences between super-responders and non super-responders in terms of LVEF, LVESD, left ventricular end-diastolic diameter (LVEDD), and mitral valve regurgitation.

ECG Parameters

Pre-implantation QRS complex duration did not differ between super-responders and non super-responders (166±3 vs. 167±2 ms, p=0.81). Lower pre-implantation RWHV1-3(470±23 vs 563±37 p=0.04) and TWHV1-3levels (185±10 vs 241±20 p=0.009) were also associated with positive response to CRT using the less restrictive 5% LVEF increase criterion.

FIG. 3shows superimposed ECGs from leads V4-6from one representative super-responder and one representative non super-responder demonstrating reductions in RWH, TWH, and QRS complex duration comparing baseline to 1 year after implantation.

In the entire cohort, at follow-up after one year, RWH and TWH in all lead sets tested were significantly lower among super-responders than non super-responders (FIG. 4), but there was no significant difference between the groups in QRS complex duration (150±4 vs 159±3 ms, p=0.28). Specifically,FIG. 4shows the time course of R-wave and T-wave heterogeneity (RWH, TWH) and QRS complex duration at pre-implantation and at day one and at 3, 6, and 12 months after implantation, according to embodiments of the present disclosure. Numerical data for significant differences in lead sets at 1 year following cardiac resynchronization therapy (CRT) device implantation are: RWHV1-3(349±56 vs 577±33 p=0.005), RWHV1-3LILII(340±52 vs 593±29 p=0.0001), RWHV4-6(163±30 vs 300±36 μV, p=0.007), RWHV4-6LILII(196±27 vs 344±27 μV, p=0.0008), TWHV1-3(153±29 vs 202±14 μV, p=0.01), TWHV1-3LILII(159±33 vs 213±13 μV, p=0.003), TWHV4-6(67±15 vs 133±16 μV, p=0.004), and TWHV4-6LILII(84±12 vs 137±13 μV, p=0.003). †p<0.05 comparing super-responders and non super-responders. *p<0.05 comparing the time point and baseline.

Prediction of CRT Super-Response

FIGS. 5A, 5B, and 5Cillustrate example diagrams showing receiver-operating characteristic (ROC) curves for the capacity of R-wave and T-wave heterogeneity (RWH, TWH) and QRS complex duration to predict mechanical super-response to cardiac resynchronization therapy (CRT) in the entire cohort (FIG. 5A) and in patients with (FIG. 5B) and without LBBB (FIG. 5C). RWHV1-3and TWH in all lead sets showed significance in the entire cohort, according to embodiments of the present disclosure. The p-value is based on difference between the areas under the curve (AUC) in each graph and a 0.5 (random) AUC.

Specifically, areas under the curve (AUC) were significantly different from random in RWHV1-3(p=0.02) and in all TWH lead sets tested (p=0.002 to 0.03) (FIG. 5A). However, the AUC for QRS complex duration (0.48) did not differ significantly from random (p=0.74).

Table 4 shows the unadjusted odds ratios, optimized cutpoints, and test performances for ECG parameters in predicting super-response.

When adjusted for confounding by absence of CAD, hypertension, and history of acute myocardial infarction, and presence of nonischemic cardiomyopathy (Table 5), the optimized cutpoints for RWH and TWH in all lead sets yielded significant odds ratios (2.91 to 13.05, p<0.0001 to 0.02) for predicting mechanical super-response to CRT. RWHV4-6LILIIproduced the highest odds ratio and greatest significance level. Neither the standard (150 ms) nor the optimized cutpoint (155 ms) for QRS complex duration yielded a significant odds ratio.

TABLE 5Adjusted odds ratios for performance of different pre-implantationcriteria in predicting mechanical super-response to CRTAdjusted Odds Ratios*CriteriacutpointORCI 95%PRWHv1-3(μV)2967.822.86-21.360.0001RWHv1-3LILII(μV)4086.212.29-16.860.0003RWHv4-6(μV)1982.911.14-7.440.02RWHV4-6LILII(μV)16413.053.58-47.56<0.0001TWHv1-3(μV)1194.021.62-9.930.003TWHv1-3LILII(μV)1824.21.77-9.940.001TWHv4-6(μV)1163.031.3-7.080.01TWHv4-6LILII(μV)1765.381.98-14.640.001QRS complex (ms)1501.660.58-4.740.35QRS complex (ms)1552.40.86-6.70.09

Odds ratios were adjusted for absence of coronary artery disease, history of acute myocardial infarction, and hypertension, and presence of non-ischemic cardiomyopathy. RWH=R-wave heterogeneity; TWH-T-wave heterogeneity.

Prediction of All-Cause Mortality

During the follow-up period of 3 years, there were 39 deaths (32.5%) among the 120 non super-responders and 7 deaths (20%) among the 35 super-responders (p=0.005). Kaplan-Meier analysis revealed that 3-year mortality was significantly increased in patients with pre-implantation RWHV1-3LILII≥420 μV (p=0.037) with a hazard ratio of 7.440 (95% CI: 1.015-54.527, p=0.048) (shown inFIG. 6A) in ECGs recorded prior to cardiac resynchronization therapy (CRT) device implantation, according to embodiments of the present disclosure. In this analysis, neither pre-implantation QRS complex duration>150 ms (p=0.27) (shown inFIG. 6B) nor TWH in any lead set predicted 3-year all-cause mortality (p=0.20).

Discussion

Main findings

The study demonstrated that reduced levels of ECG repolarization heterogeneity at pre-implantation predict mechanical response to CRT and survival. The findings indicate that RWHV1-3and TWH in all lead combinations are superior to QRS complex duration, the mainstay ECG marker currently used to predict response to CRT. This predictive capacity remains significant even after adjustment for clinical characteristics. Furthermore, the results reveal that CRT reduced RWH and TWH in all patients, particularly among super-responders. Importantly, RWHV1-3LILII≥420 μV was associated with increased risk of total 3-year mortality (p=0.037) with a hazard ratio of 7.440 (95% CI: 1.015-54.527, p=0.048).

Prior Studies

Preclinical studies reported an increase in QT interval and transmural dispersion of repolarization (Tpeak-Tendinterval) in ventricular wedge preparations during epicardial pacing. Fish, Di Diego, Nesterenko, and Antzelevitch (2004) made the fundamental observation that under conditions in which QT interval is prolonged, epicardial to endocardial activation may predispose to malignant arrhythmias such as torsades de pointes by increasing transmural dispersion of repolarization. See Fish J M, Di Diego J M, Nesterenko V, Antzelevitch C. Epicardial activation of left ventricular wall prolongs QT interval and transmural dispersion of repolarization. Implications for biventricular pacing. Circulation 2004;109:2136-2142. Clinical studies have indicated a decrease in heterogeneity in terms of QT dispersion and/or Tpeak-Tend interval, etc., following CRT, especially in the long-term. Cvijic and colleagues (2018) reported in a prospective study of 64 patients that lower repolarization heterogeneity in terms of QT interval, Tpeak-Tend, and Tpeak-Tend/QT ratio characterized patients with better mechanical response to CRT at one year following implantation. See Cvijic M, Antolic B, Klemen L, Zupan I. Repolarization heterogeneity in patients with cardiac resynchronization therapy and its relation to ventricular tachyarrhythmias. Heart Rhythm 2018;15:1784-1790. Moreover, reduced Tpeak-Tend/QT ratio distinguished patients with lower incidence of ventricular tachycardia and ventricular fibrillation during follow-up. However, they did not report that any of these indices at pre-implantation helped to distinguish patients with improved mechanical response to CRT or improved survival.

Current Study

The central question addressed is whether or not RWH and/or TWH is superior to the mainstay measurement of QRS complex duration in predicting mechanical super-response and mortality in patients receiving CRT devices. The rationale for the study was the well-established excitation-contraction coupling relationship as well as a recent finding in patients with newly developed conduction abnormalities after transcatheter aortic valve replacement that TWH is significantly correlated with mechanical dyssynchrony as measured by global longitudinal strain. Thus, it was hypothesized that TWH could help to reveal electro-mechanical dyssynchrony and global cardiac pathology and therefore to predict which patients might not experience enhanced mechanical response to CRT. Also, in light of studies showing the capacity of TWH to predict sudden cardiac death and cardiac and total mortality, it was postulated that RWH and/or TWH at the time of CRT implantation would predict premature demise.

Pre-implantation levels of RWH and TWH in some leads were found to be superior to QRS complex duration in predicting mechanical super-response to CRT (Table 5). The optimized RWH and TWH cutpoints yielded significant odds ratios that were >7-fold greater than the non-significant odds ratio produced by QRS complex duration of 150 ms (Table 5). Furthermore, at the end of 1-year follow up, the super-responders were found to have significantly lower levels of RWH and TWH in all analyzed lead sets than the non super-responders, while QRS complex duration did not discriminate between the groups (FIG. 4). Moreover, the AUC for prediction of super-response by RWH and TWH was significant while the AUC achieved by QRS complex duration was not (FIGS. 5A, 5B, and 5C). It is noteworthy that low RWH and TWH levels also predicted CRT response in both the presence and absence of LBBB (FIGS. 5B and 5C). These observations carry important practical implications in light of evidence that patients with non-LBBB may benefit from CRT (19). The ENHANCE-CRT trial is designed to shed further light on this issue (Singh et al 2018). See Singh J P, Berger R D, Doshi R N, Lloyd M, Moore D, Daoud E G, for the ENHANCE CRT study group. Rationale and design for ENHANCE CRT: QLV implant strategy for non-left bundle branch block patients. ESC Heart Failure 2018;4:1184-1190.

A major finding of the present study is that quantification of RWH in leads V1-3, LI, and LII, from a single 12-lead ECG recorded at the pre-implantation of a CRT device predicted overall mortality during a 36-month follow-up (FIGS. 6A and 6B). By comparison, the Kaplan-Meier curve for QRS complex duration ≥150 ms did not achieve statistical significance nor did TWH in in any of the lead sets. The basis for the superiority of RWH in predicting survival is unclear. A possible explanation is that depolarization exerts a greater influence on mechanical synchrony than does repolarization. It is unclear why lower levels of RWH are associated with super-response. A potential explanation is that excessive degrees of heterogeneity may inhibit the capacity to confer improvement in electrical and mechanical dyssynchrony. Detailed study of the excitation-contraction relationships in patients with implanted CRT devices will be required to address this question and the underlying mechanisms.

The capacity to obtain a significant level of prediction from a single, routine 12-lead ECG recording is consistent with prior studies. Specifically, in Health Survey 2000, ECG heterogeneity achieved odds ratios of 3.2 among the 5600 individuals surveyed as representative of the entire Finnish population who underwent a routine health screening (Kentta et al2016). See Kenttä T V, Nearing B D, Porthan K, Tikkanen J T, Viitasalo M, Nieminen M S, Salomaa V, Oikarinen L, Huikuri H V, Verrier R L. Prediction of sudden cardiac death with automated high throughput analysis of heterogeneity in standard resting 12-lead electrocardiogram. Heart Rhythm 2016; 13:713-720. Tan et al (2017) found that RWH and/or TWH recorded from a single 12-lead ECG obtained in the cardiac electrophysiology study laboratory at the time of ICD implantation or battery replacement predicted arrhythmia-free and total survival independent of age, sex, and LVEF. See Tan A Y, Nearing B D, Rosenberg M, Nezafat R, Josephson M E, Verrier R L. Interlead heterogeneity of R- and T-wave morphology in standard 12-lead ECGs predicts sustained ventricular tachycardia/fibrillation and arrhythmic death in patients with cardiomyopathy. J Cardiovasc Electrophysiol 2017; 28:1324-1333.

An important question for future investigation is the precise electrophysiologic basis for the capacity of pre-implantation RWH and TWH to predict super-mechanical response, especially in comparison to QRS complex duration. A salient feature of RWH and TWH is that they provide temporo-spatial information as they involve signals from 3 to 5 leads compared to a single lead in the case of QRS complex duration. There is mounting evidence that interlead heterogeneity of R- and T-wave morphology is more informative than conventional markers of repolarization abnormality, such as QT prolongation and dispersion, which are affected by the uncertain determination of the end of the T wave (Kentta et al 2016; Verrier and Huikuri 2017; Porthan et al 2013). See Verrier R L, Huikuri H V. Tracking interlead heterogeneity of R- and T-wave morphology to disclose latent risk for sudden cardiac death. Heart Rhythm 2017; 14:1466-1475; Porthan K, Viitasalo M, Toivonen L, et al. Predictive value of electrocardiographic T-wave morphology parameters and T-wave peak to T-wave end interval for sudden cardiac death in the general population. Circ Arrhythm Electrophysiol 2013;6:690-696. The basic assumption is that multilead ECG morphology analysis provides an improved assessment of the underlying action potential patterns and their linkage to contractile synergy through the biophysical factors at the root of excitation-contraction coupling. In other words, the greater the electrical dyssynchrony, the greater the mechanical dyssynchrony. The underlying principle of CRT is to mitigate this adverse condition.

CONCLUSION

Pre-implantation ECG heterogeneity is superior to QRS complex duration in predicting mechanical super-response to CRT and survival. Patients with higher RWH and TWH levels are less likely to benefit from CRT than those with lower levels. Ultimately, ECG heterogeneity could also be utilized, along with clinical and echocardiographic parameters, to monitor patients' response to treatment.

Exemplary Embodiments of ECG Systems and Methods of Operation

FIG. 7illustrates an example ECG system700configured to perform the electrocardiogram (ECG) heterogeneity procedures, according to embodiments of the present disclosure. ECG system700may be used at a hospital or may be a portable device for use wherever the patient may be. In another example, ECG system700may be an implantable biomedical device with leads implanted in various locations around the body of a patient. ECG system700may be part of or may be coupled with other implantable biomedical devices such as a cardiac pacemaker, an implantable cardioverter-defibrillator (ICD) or a cardiac resynchronization therapy (CRT) device.

ECG system700includes leads702and a main unit704. Leads702may comprise any number and type of electrical lead. For example, leads702may comprise ten leads to be used with a standard 12-lead ECG. Leads702may be similar to leads104a-jas illustrated inFIG. 1and described previously. In another example, leads702may comprise implanted electrical leads, such as insulated wires placed throughout the body.

Main unit704may include an input module706, a processor708, a memory module710and a display712. Input module706includes suitable circuitry and hardware to receive the signals from leads702. As such, input module706may include components such as, for example, analog-to-digital converters, de-serializers, filters, and amplifiers. These various components may be implemented to condition the received signals to a more suitable form for further signal processing to be performed by processor708.

It should be understood that in the case of the embodiment where ECG system700is an implantable biomedical device, display712may be replaced with a transceiver module configured to send and receive signals such as radio frequency (RF), optical, inductively coupled, or magnetic signals. In one example, these signals may be received by an external display for providing visual data related to measurements performed by ECG system700and analysis performed after receiving the signal and quantifying a spatio-temporal heterogeneity of the ECG signals based on the analysis.

Processor708may include one or more hardware microprocessor units. In an embodiment, processor708is configured to perform signal processing procedures on the signals received via input module706. For example, processor708may perform the ECG heterogeneity procedures, such as R-wave and T-wave heterogeneity analysis for prediction of efficacy of implanting a pacemaker or CRT device in a patient. Processor708may also comprise a field-programmable gate array (FPGA) that includes configurable logic. The configurable logic may be programmed to perform the ECG heterogeneity procedures using configuration code stored in memory module710. Likewise, processor708may be programmed via instructions stored in memory module710.

Memory module710may include any type of memory including random access memory (RAM), read-only memory (ROM), electrically-erasable programmable read-only memory (EEPROM), FLASH memory, etc. Furthermore, memory module710may include both volatile and non-volatile memory. For example, memory module710may contain a set of coded instructions in non-volatile memory for programming processor708. The calculated baseline signal may also be stored in either the volatile or non-volatile memory depending on how long it is intended to be maintained. Memory module710may also be used to save data related to the calculated TWH or RWH, including trend data for each.

In an embodiment, main unit704includes display712for providing a visual representation of the received signals from leads702. Display712may utilize any of a number of different display technologies such as, for example, liquid crystal display (LCD), light emitting diode (LED), plasma or cathode ray tube (CRT). An ECG signal from each of leads702may be displayed simultaneously on display712. In another example, a user may select which ECG signals to display via a user interface associated with main unit704. Display712may also be used to show data trends over time, such as displaying trends of the calculated RWH and TWH.

FIG. 8illustrates a flowchart depicting a method800for predicting efficacy of implanting a pacemaker or CRT device in a patient, according to embodiments of the present disclosure. Method800may be performed by the various components of ECG system700. It is to be appreciated that method800may not include all operations shown or perform the operations in the order shown.

Method800begins at step802where a first set of ECG signals of a patient is received. In particular, the ECG signals may be monitored via leads such as those illustrated inFIG. 1, or via implantable leads, and received by an ECG recording device or ECG system, such as ECG system700. In some cases, the first set of ECG signals may be obtained via spatially separated leads such as V1, V2, and V3 or V4, V5, and V6 of a standard 12-lead ECG.

At step802, the data from the first set of ECG signals may be analyzed. The analysis may be implemented by the ECG system700and may include second central moment analysis techniques.

At step804, the spatio-temporal heterogeneity of the first set of ECG signals may be quantified based on the analysis. In particular, the processor708of the ECG system700may calculate at least one of the R-wave heterogeneity (RWH) and T-wave heterogeneity (TWH) of the first set of ECG signals.

At step806, the efficacy of implanting a pacemaker or CRT device in the patient may be determined based on the quantified spatio-temporal heterogeneity. In particular, if the patient has a lower range of TWH that is below a predetermined threshold or range of threshold values, then the EEG system700may generate a prediction that the patient is more likely to benefit from CRT implantation than other patients.

At step808, a pacemaker or CRT device may be implanted in the patient based on the determination. In some embodiments, the quantified spatio-temporal heterogeneity, including parameters and lead sites identified during quantification of the ECG heterogeneity data, may be utilized to guide placement of the device in the patient.

Exemplary Computer Implementation

FIGS. 1-8as described herein are illustrative examples allowing an explanation of the present invention. It should be understood that embodiments of the present invention could be implemented in hardware, firmware, software, or a combination thereof. In such an embodiment, the various components and steps would be implemented in hardware, firmware, and/or software to perform the functions of the present invention. That is, the same piece of hardware, firmware, or module of software could perform one or more of the illustrated blocks (i.e., components or steps).

The present invention may be implemented in one or more computer systems capable of carrying out the functionality described herein. Referring toFIG. 9, an example computer system900useful in implementing the present invention is shown. Various embodiments of the invention are described in terms of this example computer system900. After reading this description, it will become apparent to one skilled in the relevant art(s) how to implement the invention using other computer systems and/or computer architectures.

The computer system900includes one or more processors, such as processor904. The processor904is connected to a communication infrastructure906(e.g., a communications bus, crossover bar, or network).

Computer system900may include a display interface902that forwards graphics, text, and other data from the communication infrastructure906(or from a frame buffer not shown) for display on the display unit930.

Computer system900also includes a main memory908, preferably random access memory (RAM), and may also include a secondary memory910. The secondary memory910may include, for example, a hard disk drive912and/or a removable storage drive914, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. The removable storage drive914reads from and/or writes to a removable storage unit918in a well-known manner. Removable storage unit918, represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to removable storage drive914. As will be appreciated, the removable storage unit918includes a computer usable storage medium having stored therein computer software (e.g., programs or other instructions) and/or data.

In alternative embodiments, secondary memory910may include other similar means for allowing computer software and/or data to be loaded into computer system900. Such means may include, for example, a removable storage unit922and an interface920. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units922and interfaces920which allow software and data to be transferred from the removable storage unit922to computer system900.

Computer system900may also include a communications interface924. Communications interface924allows software and data to be transferred between computer system900and external devices. Examples of communications interface924may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via communications interface924are in the form of signals928which may be electronic, electromagnetic, optical, or other signals capable of being received by communications interface924. These signals928are provided to communications interface924via a communications path (i.e., channel)926. Communications path926carries signals928and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, free-space optics, and/or other communications channels.

In this document, the terms “computer program medium” and “computer usable medium” are used to generally refer to media such as removable storage unit918, removable storage unit922, a hard disk installed in hard disk drive912, and signals928. These computer program products are means for providing software to computer system900. The invention is directed to such computer program products.

Computer programs (also called computer control logic or computer readable program code) are stored in main memory908and/or secondary memory910. Computer programs may also be received via communications interface924. Such computer programs, when executed, enable the computer system900to implement the present invention as discussed herein. In particular, the computer programs, when executed, enable the processor904to implement the processes of the present invention described above. Accordingly, such computer programs represent controllers of the computer system900.

In an embodiment where the invention is implemented using software, the software may be stored in a computer program product and loaded into computer system900using removable storage drive914, hard disk drive912, interface920, or communications interface924. The control logic (software), when executed by the processor904, causes the processor904to perform the functions of the invention as described herein.

In another embodiment, the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to one skilled in the relevant art(s).

In one example embodiment, the present invention may be implemented in a computer-based monitor unit for use in a clinical setting. In another embodiment, the present invention may be implemented in an ambulatory unit akin to a Holter monitor, personal computing device, or similar portable device. In yet another embodiment, the present invention may be implemented in an implantable medical device such as an implantable cardioverter defibrillator (ICD).