Clinical impact of multi-parameter continuous non-invasive monitoring in hospital wards: a systematic review and meta-analysis

Lin Sun, Meera Joshi, Sadia N Khan, Hutan Ashrafian, Ara Darzi

Journal of the Royal Society of Medicine, Volume: 113 issue: 6, page(s): 217-224




Delayed response to clinical deterioration as a result of intermittent vital sign monitoring is a cause of preventable morbidity and mortality. This review focuses on the clinical impact of multi-parameter continuous non-invasive monitoring of vital signs (CoNiM) in non-intensive care unit patients.


Systematic review and meta-analysis of primary studies. Embase, MEDLINE, HMIC, PsycINFO and Cochrane were searched from April 1964 to 18 June 2019 with no language restriction.


The search was limited to hospitalised, non-intensive care unit adult patients who had two or more vital signs continuously monitored.


All primary studies that evaluated the clinical impact of using multi-parameter CoNiM in adult hospital wards outside of the intensive care unit.

Main outcome measures

Clinical impact of multi-parameter CoNiM.


This systematic review identified 14 relevant studies from 3846 search results. Five studies were classified as Group A – associations found between measured vital signs and clinical parameters. Nine studies were classified as Group B – comparison between clinical outcomes of patients with and without multi-parameter CoNiM. Vital signs data from CoNiM were found to associate with type of presenting complaint, level of renal function and incidence of major clinical events. CoNiM also assisted in diagnosis by differentiating between patients with acute heart failure, stroke and sepsis (with sub-clustering of septic patients). In the meta-analysis, patients on multi-parameter CoNiM had a 39% decrease in risk of mortality (risk ratio [RR] 0.61; 95% confidence interval [95% CI] 0.39, 0.95) when compared to patients with regular intermittent monitoring. There was a trend of reduced intensive care unit transfer (RR 0.86; 95% CI 0.67, 1.11) and reduced rapid response team activation (RR 0.61; 95% CI 0.26–1.43). A trend towards reduced hospital length of stay was also found using weighted mean difference (WMD –3.32 days; 95% CI -8.82–2.19 days).


There is evidence of clinical benefit in implementing CoNiM in non-intensive care unit patients. This review supports the use of multi-parameter CoNiM outside of intensive care unit with further large-scale RCTs required to further affirm clinical impact.

Three‐Dimensional Global Left Ventricular Myocardial Strain Reduced in All Directions in Subclinical Diabetic Cardiomyopathy: A Systematic Review and Meta‐Analysis

Seyed‐Mohammad Ghoreyshi‐Hefzabad, MD; Prajith Jeyaprakash, MBBS, MMed; Alpa Gupta, MBBS; Ha Q. Vo, MSc, PhD; Faraz Pathan, MBBS, PhD; Kazuaki Negishi, MD, PhD

Journal of the American Heart Association, Volume 10, Issue 19, 5 October 2021


Three‐dimensional (3D) speckle tracking echocardiography can identify subclinical diabetic cardiomyopathy without geometric assumption and loss of speckle from out‐of‐plane motions. There is, however, significant heterogeneity among the previous reports. We performed a systematic review and meta‐analysis to compare 3D strain values between adults with asymptomatic, subclinical diabetes mellitus (ie, patients with diabetes mellitus without known clinical manifestations of cardiac disease) and healthy controls.

Methods and Results
After systematic review of 5 databases, 12 valid studies (544 patients with diabetes mellitus and 489 controls) were eligible for meta‐analysis. Pooled means and mean difference (MD) using a random‐effects model for 3D global longitudinal, circumferential, radial, and area strain were calculated. Patients with diabetes mellitus had an overall 2.31 percentage points lower 3D global longitudinal strain than healthy subjects (16.6%, 95% CI, 15.7–17.6 versus 19.0; 95% CI, 18.2–19.7; MD, −2.31, 95% CI, −2.72 to −2.03). Similarly, 3D global circumferential strain (18.9%; 95% CI, 17.5–20.3 versus 20.5; 95% CI, 18.9–22.1; MD, −1.50; 95% CI, −2.09 to −0.91); 3D global radial strain (44.6%; 95% CI, 40.2–49.1 versus 48.2; 95% CI, 44.7–51.8; MD, −3.47; 95% CI, −4.98 to −1.97), and 3D global area strain (30.5%; 95% CI, 29.2–31.8 versus 32.4; 95% CI, 30.5–34.3; MD, −1.76; 95% CI, −2.74 to −0.78) were also lower in patients with diabetes mellitus. Significant heterogeneity was noted between studies for all strain directions (inconsistency factor [I2], 37%–78%). Meta‐regression in subgroup analysis of studies using the most popular vendor found higher prevalence of hypertension as a significant contributor to worse 3D global longitudinal strain. Higher hemoglobulin A1c was the most significant contributor to worse 3D global circumferential strain in patients with diabetes mellitus.

Three‐dimensional myocardial strain was reduced in all directions in asymptomatic diabetic patients. Hypertension and hemoglobin A1c were associated with worse 3D global longitudinal strain and 3D global circumferential strain, respectively.

Prognostic Value of Electrocardiographic QRS
Diminution in Patients Hospitalized With COVID-19
or Influenza

Joshua Lampert, MD; Michael Miller, MSc; Jonathan Lee Halperin, MD; Connor Oates, MD; Gennaro Giustino, MD; Kyle Nelson, MD; Jason Feinman, MD; Nikola Kocovic, MD et al American Journal of Cardiology Published August 08, 2021


During the clinical care of hospitalized patients with COVID-19, diminished QRS amplitude on the surface electrocardiogram (ECG) was observed to precede clinical decompensation, culminating in death. This prompted investigation into the prognostic utility and
specificity of low QRS complex amplitude (LoQRS) in COVID-19. We retrospectively analyzed consecutive adults admitted to a telemetry service with SARS-CoV-2 (n = 140) or
influenza (n = 281) infection with a final disposition—death or discharge. LoQRS was
defined as a composite of QRS amplitude <5 mm or <10 mm in the limb or precordial
leads, respectively, or a ≥50% decrease in QRS amplitude on follow-up ECG during hospitalization. LoQRS was more prevalent in patients with COVID-19 than influenza
(24.3% vs 11.7%, p = 0.001), and in patients who died than survived with either COVID19 (48.1% vs 10.2%, p <0.001) or influenza (38.9% vs 9.9%, p <0.001). LoQRS was independently associated with mortality in patients with COVID-19 when adjusted for baseline
clinical variables (odds ratio [OR] 11.5, 95% confidence interval [CI] 3.9 to 33.8,
p <0.001), presenting and peak troponin, D-dimer, C-reactive protein, albumin, intubation, and vasopressor requirement (OR 13.8, 95% CI 1.3 to 145.5, p = 0.029). The median
time to death in COVID-19 from the first ECG with LoQRS was 52 hours (interquartile
range 18 to 130). Dynamic QRS amplitude diminution is a strong independent predictor
of death over not only the course of COVID-19 infection, but also influenza infection. In
conclusion, this finding may serve as a pragmatic prognostication tool reflecting evolving
clinical changes during hospitalization, over a potentially actionable time interval for clinical reassessment.

© 2021 Elsevier Inc. All rights reserved. (Am J Cardiol 2021;159:129−137)

Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction

Ambarish Pandey, Nobuyuki Kagiyama, Naveena Yanamala, Matthew W. Segar, Jung S. Cho, Márton Tokodi, and Partho P. Sengupta

J Am Coll Cardiol Img. May 19, 2021. Epublished DOI: 10.1016/j.jcmg.2021.04.010




The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF).


The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise.


The authors developed a DeepNN model to predict high- and low-risk phenogroups in a derivation cohort (n = 1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute–funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate’s Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346).


The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001).


This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone.

Early-Onset Atrial Fibrillation and the Prevalence of Rare Variants in Cardiomyopathy and Arrhythmia Genes

Zachary T. Yoneda, MD, MSCI1; Katherine C. Anderson, MS, CSG1; Joseph A. Quintana, MD1; Matthew J. O’Neill, BS2; Richard A. Sims, MD1; Andrew M. Glazer, PhD3; Christian M. Shaffer, BS3; Diane M. Crawford, RN1; Thomas Stricker, MD, PhD4; Fei Ye, PhD5; Quinn Wells, MD, MSCI1; Lynne W. Stevenson, MD1; Gregory F. Michaud, MD1; Dawood Darbar, MBChB, MD6; Steven A. Lubitz, MD, MPH7,8; Patrick T. Ellinor, MD, PhD7,8; Dan M. Roden, MD1,9,10,11; M. Benjamin Shoemaker, MD, MSCI1

1Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee

JAMA Cardiol. Published online September 8, 2021. doi:10.1001/jamacardio.2021.3370

Original Investigation

September 8, 2021


Importance  Early-onset atrial fibrillation (AF) can be the initial manifestation of a more serious underlying inherited cardiomyopathy or arrhythmia syndrome.

Objective  To examine the results of genetic testing for early-onset AF.

Design, Setting, and Participants  This prospective, observational cohort study enrolled participants from an academic medical center who had AF diagnosed before 66 years of age and underwent whole genome sequencing through the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine program. Participants were enrolled from November 23, 1999, to June 2, 2015. Data analysis was performed from October 24, 2020, to March 11, 2021.

Exposures  Rare variants identified in a panel of 145 genes that are included on cardiomyopathy and arrhythmia panels used by commercial clinical genetic testing laboratories.

Main Outcomes and Measures  Sequencing data were analyzed using an automated process followed by manual review by a panel of independent, blinded reviewers. The primary outcome was classification of rare variants using American College of Medical Genetics and Genomics criteria: benign, likely benign, variant of undetermined significance, likely pathogenic, or pathogenic. Disease-associated variants were defined as pathogenic/likely pathogenic variants in genes associated with autosomal dominant or X-linked dominant disorders.

Results  Among 1293 participants (934 [72.2%] male; median [interquartile range] age at enrollment, 56 [48-61] years; median [interquartile range] age at AF diagnosis, 50 [41-56] years), genetic testing identified 131 participants (10.1%) with a disease-associated variant, 812 (62.8%) with a variant of undetermined significance, 92 (7.1%) as heterozygous carriers for an autosomal recessive disorder, and 258 (20.0%) with no suspicious variant. The likelihood of a disease-associated variant was highest in participants with AF diagnosed before the age of 30 years (20 of 119 [16.8%; 95% CI, 10.0%-23.6%]) and lowest after the age of 60 years (8 of 112 [7.1%; 95% CI, 2.4%-11.9%]). Disease-associated variants were more often associated with inherited cardiomyopathy syndromes compared with inherited arrhythmias. The most common genes were TTN (n = 38), MYH7 (n = 18), MYH6 (n = 10), LMNA (n = 9), and KCNQ1 (n = 8).

Conclusions and Relevance  In this cohort study, genetic testing identified a disease-associated variant in 10% of patients with early-onset AF (the percentage was higher if diagnosed before the age of 30 years and lower if diagnosed after the age of 60 years). Most pathogenic/likely pathogenic variants are in genes associated with cardiomyopathy. These results support the use of genetic testing in early-onset AF.

User Perceptions and Experiences of a Handheld 12-Lead Electrocardiographic Device in a Clinical Setting: Usability Evaluation

Kam Cheong Wong 1, 2, 3, 4 ; Aravinda Thiagalingam 1, 3, 5 e ; Saurabh Kumar 1, 3, 5 ; Simone Marschner 1 ; Ritu Kunwar 1 ; Jannine Bailey 2 ; Cindy Kok 6 ; Tim Usherwood 1, 3, 7 ; Clara K Chow 1, 3, 5, 7, 8

JMIR Cardio 2021;5(2):e21186 doi:10.2196/21186



Background: Cardiac arrhythmias are a leading cause of death. The mainstay method for diagnosing arrhythmias (eg, atrial fibrillation) and cardiac conduction disorders (eg, prolonged corrected QT interval [QTc]) is by using 12-lead electrocardiography (ECG). Handheld 12-lead ECG devices are emerging in the market. In tandem with emerging technology options, evaluations of device usability should go beyond validation of the device in a controlled laboratory setting and assess user perceptions and experiences, which are crucial for successful implementation in clinical practice.

Objective: This study aimed to evaluate clinician and patient perceptions and experiences, regarding the usability of a handheld 12-lead ECG device compared to a conventional 12-lead ECG machine, and generalizability of this user-centered approach.

Methods: International Organization for Standardization Guidelines on Usability and the Technology Acceptance Model were integrated to form the framework for this study, which was conducted in outpatient clinics and cardiology wards at Westmead Hospital, New South Wales, Australia. Each patient underwent 2 ECGs (1 by each device) in 2 postures (supine and standing) acquired in random sequence. The times taken by clinicians to acquire the first ECG (efficiency) using the devices were analyzed using linear regression. Electrocardiographic parameters (QT interval, QTc interval, heart rate, PR interval, QRS interval) and participant satisfaction surveys were collected. Device reliability was assessed by evaluating the mean difference of QTc measurements within ±15 ms, intraclass correlation coefficient, and level of agreement of the devices in detecting atrial fibrillation and prolonged QTc. Clinicians’ perceptions and feedback were assessed with semistructured interviews based on the Technology Acceptance Model.

Results:A total of 100 patients (age: mean 57.9 years, SD 15.2; sex: male: n=64, female n=36) and 11 clinicians (experience acquiring ECGs daily or weekly 10/11, 91%) participated, and 783 ECGs were acquired. Mean differences in QTc measurements of both handheld and conventional devices were within ±15 ms with high intraclass correlation coefficients (range 0.90-0.96), and the devices had a good level of agreement in diagnosing atrial fibrillation and prolonged QTc (κ=0.68-0.93). Regardless of device, QTc measurements when patients were standing were longer duration than QTc measurements when patients were supine. Clinicians’ ECG acquisition times improved with usage (P<.001). Clinicians reported that device characteristics (small size, light weight, portability, and wireless ECG transmission) were highly desired features. Most clinicians agreed that the handheld device could be used for clinician-led mass screening with enhancement in efficiency by increasing user training. Regardless of device, patients reported that they felt comfortable when they were connected to the ECG devices.

Conclusions: Reliability and usability of the handheld 12-lead ECG device were comparable to those of a conventional ECG machine. The user-centered evaluation approach helped us identify remediable action to improve the efficiency in using the device and identified highly desirable device features that could potentially help mass screening and remote assessment of patients. The approach could be applied to evaluate and better understand the acceptability and usability of new medical devices.

3D Myocardial Mechanical Wave Measurements: Toward In Vivo 3D Myocardial Elasticity Mapping

Original Research

Sebastien Salles, Torvald Espeland, Alfonso Molares, Svein Arne Aase, Tommy Arild Hammer, Asbjørn Støylen, Svend Aakhus, Lasse Lovstakken, and Hans Torp

J Am Coll Cardiol Img. 2021 Aug, 14 (8) 1495–1505;



This study aimed to investigate the potential of a novel 3-dimensional (3D) mechanical wave velocity mapping technique, based on the natural mechanical waves produced by the heart itself, to approach a noninvasive 3D stiffness mapping of the left ventricle.


Myocardial fibrosis is recognized as a pathophysiological substrate of major cardiovascular disorders such as cardiomyopathies and valvular heart disease. As fibrosis leads to increased myocardial stiffness, ultrasound elastography measurements could provide important clinical information.


A 3D high frame rate imaging sequence was implemented on a high-end clinical ultrasound scanner to achieve 820 volumes/s when gating over 4 consecutive cardiac cycles. Five healthy volunteers and 10 patients with various degrees of aortic stenosis were included to evaluate feasibility and reproducibility. Mechanical waves were detected using the novel Clutter Filter Wave Imaging approach, shown to be highly sensitive to the weak tissue displacements caused by natural mechanical waves.


3D spatiotemporal maps of mechanical wave velocities were produced for all subjects. Only the specific mechanical wave at atrial contraction provided a full 3D coverage of the left ventricle (LV). The average atrial kick propagation velocity was 1.6 ± 0.2 m/s in healthy volunteers and 2.8 ± 0.8 m/s in patients (p = 0.0016). A high correlation was found between mechanical wave velocity and age (R2 = 0.88, healthy group), septal wall thickness (R2 = 0.73, entire group), and peak jet velocity across the aortic valve (R2 = 0.70). For 3 of the patients, the higher mechanical wave velocity coexisted with the presence of late gadolinium enhancement on cardiac magnetic resonance.


In this study, 3D LV mechanical wave velocities were visualized and measured in healthy volunteers and patients with aortic stenosis. The proposed imaging sequence and measurement technique allowed, for the first time, the measurement of full spatiotemporal 3D elasticity maps of the LV using ultrasound. (Ultrasonic markers for myocardial fibrosis and prognosis in aortic stenosis; NCT03422770)


Left atrial reservoir strain provides incremental value to left atrial volume index for evaluation of left ventricular filling pressure

Stephen Tomlinson MBBSGregory M. Scalia M Med ScVinesh Appadurai MBBSNatalie Edwards BExSciMichael Savage BAppScAlfred K-Y. Lam PhDJonathan Chan PhD

First published: 06 August 2021



Left atrial analysis is employed in diastolic assessment with left atrial volume index (LAVI) incorporated in the 2016 ASE/EACVI diastology guideline algorithm. LAVI has sub-optimal correlation with invasive left ventricular filling pressure (LVFP) and incorporation of left atrial reservoir strain (LASr) may improve diastolic assessment.


A cross-sectional prospective study of 139 patients was undertaken with all patients undergoing transthoracic echocardiography immediately prior to cardiac catheterization with invasive evaluation of LVFP. LASr by speckle tracking echocardiography and conventional echocardiographic parameters were assessed in relation to invasive LVFP. Modification of the 2016 guideline algorithm was performed with incorporation of LASr in place of LAVI (LASr ≤23% indicating elevated LVFP). Accuracy of the modified and conventional algorithm were assessed for predicting invasive LVFP.


The mean age was 63±12 years with 27% female. LASr demonstrated superior correlation and receiver operator characteristic for predicting LVFP than LAVI (LASr: r -.46 (p < 0.01), AUC: .82 vs LAVI: r .19 (p 0.02), AUC: .66). LASr of ≤23% was the optimal cut-off for discriminating elevated LVFP (sensitivity 80%, specificity 77%). Modification of the 2016 algorithm with incorporation of LASr in place of LAVI reclassified 12% of the patient cohort and improved concordance of echocardiographic and invasive LVFP assessment (modified algorithm κ .47 vs 2016 algorithm κ: .33). No patients were incorrectly reclassified by modified algorithm assessment.


LASr better predicts invasive LVFP than LAVI. Modification of the 2016 guideline algorithm with incorporation of LASr in place of LAVI improves accuracy of echocardiographic assessment of LVFP.

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

Original Investigation

Akhil Narang, MD1; Richard Bae, MD2; Ha Hong, PhD3; et al

JAMA Cardiol. 2021;6(6):624-632. doi:10.1001/jamacardio.2021.0185


Importance  Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.

Objective  To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.

Design, Setting, and Participants  This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.

Interventions  Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3–trained echocardiographers independently and blindly evaluated each acquisition.

Main Outcomes and Measures  Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.

Results  A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.

Conclusions and Relevance  This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

Echocardiography-Guided LQTS Risk Stratification

Sugrue, van Zyl M, Enger N, et al.

Echocardiography-Guided Risk Stratification for Long QT Syndrome. J Am Coll Cardiol 2020;76:2834-2843.

Study Questions:

Is it possible to identify a subset of patients at the highest risk for long QT syndrome (LQTS)–associated life-threatening cardiac events by examining the electromechanical window (EMW) negativity, as derived from continuous-wave Doppler echocardiography?


There were 651 patients with LQTS (mean age, 26 years; 60% females; 158 symptomatic; 51% LQTS type 1; 33% LQTS type 2; 11% LQTS type 3) and 50 healthy controls. EMW was calculated as the difference between the interval from QRS onset to aortic valve closure midline, as derived for continuous-wave Doppler, and the electrocardiogram-derived QT interval for the same beat.


A negative EMW was found among nearly all patients with LQTS compared to controls, with more profound EMW negativity in patients with symptomatic LQTS compared to those with asymptomatic LQTS (-52 ± 38 ms vs. -18 ± 29 ms; p < 0.0001). Logistic regression identified EMW, corrected QT interval, female sex, and LQTS genotype as univariate predictors of symptomatic status. After multivariate analysis, EMW remained an independent predictor of symptomatic status (odds ratio for each 10-ms decrease in EMW, 1.37; 95% confidence interval, 1.27-1.48; p < 0.0001). EMW outperformed corrected QT interval in predicting symptomatic patients. EMW correlation across sonographers showed excellent reliability.


In this validation study, patients with a history of LQTS-associated life-threatening cardiac events had a more profoundly negative EMW. EMW outperformed heart rate–corrected QT interval as a predictor of symptomatic status. EMW is now a clinically validated risk factor.


So far, efforts at risk prediction in LQTS have mainly focused on the electrical manifestations of this condition such as corrected QT interval and T-wave morphology. This fascinating manuscript ties together the abnormalities in electrical conduction with mechanical function abnormalities observable on echo with high interobserver reliability. In normal adults, the end of electrical systole occurs slightly before the end of mechanical systole, resulting in a positive EMW. A negative EMW occurs when there is a mismatch between the end of electrical and mechanical systole as a result of prolongation of electrical systole. The exact mechanism is speculative, and it is possible that EMW is a surrogate marker that highlights impaired relaxation and inhomogeneity across the ventricle as the substrate for arrhythmogenesis. EMW should be evaluated prospectively in the QTS population and potentially many other arrhythmogenic conditions.