In a previous cross-sectional screening study of 5,169 middle and high school students (mean age, 13.1 ± 1.78 yr) in which we estimated the prevalence of high-risk cardiovascular conditions associated with sudden cardiac death, we incidentally detected by cardiac magnetic resonance (CMR) 959 cases (18.6%) of left ventricular noncompaction (LVNC) that met the Petersen diagnostic criterion (noncompaction:compaction ratio >2.3). Short-axis CMR images were available for 511 of these cases (the Short-Axis Study Set). To determine how many of those cases were truly abnormal, we analyzed the short-axis images in terms of LV structural and functional variables and applied 3 published diagnostic criteria besides the Petersen criterion to our findings. The estimated prevalences were 17.5% based on trabeculated LV mass (Jacquier criterion), 7.4% based on trabeculated LV volume (Choi criterion), and 1.3% based on trabeculated LV mass and distribution (Grothoff criterion). Absent longitudinal clinical outcomes data or accepted diagnostic standards, our analysis of the screening data from the Short-Axis Study Set did not definitively differentiate normal from pathologic cases. However, it does suggest that many of the cases might be normal anatomic variants. It also suggests that cases marked by pathologically excessive LV trabeculation, even if asymptomatic, might involve unsustainable physiologic disadvantages that increase the risk of LV dysfunction, pathologic remodeling, arrhythmias, or mural thrombi. These disadvantages may escape detection, particularly in children developing from prepubescence through adolescence. Longitudinal follow-up of suspected LVNC cases to ascertain their natural history and clinical outcome is warranted.

During development, the left ventricle (LV) of the heart contains bundles of muscle called trabeculae. Compaction of these trabeculae eventually transforms the heart muscle from spongelike to smooth and solid. Left ventricular noncompaction (LVNC), a rare congenital cardiomyopathy, results when compaction does not occur. In some cases, it may coincide with another myocardial disease (hypertrophic, dilated, or restrictive cardiomyopathy).

Left ventricular noncompaction can be diagnosed at any age but in many individuals goes undiagnosed until later in life. Its prevalence in the general population is unknown but ranges, according to various qualified estimates, from <0.3% to 1.26% in young children1,2  and from 0.02% to 0.17% in adults.3,4 

The Texas Heart Institute recently completed a large population-based screening study (the Screen to Prevent [S2P] Study) of 5,169 middle and high school students from across the Harris County (Houston), Texas, area to estimate the prevalence of congenital conditions associated with sudden cardiac death in the young.5,6  Screening included cardiac magnetic resonance (CMR) imaging, a resting electrocardiogram (ECG), and a focused questionnaire.

Various CMR-based diagnostic criteria for LVNC have been proposed (Table I).718  Because these criteria are based on small samples, various assumptions, and no accepted standard, their reliability remains in question. Applying the Petersen criterion15  (noncompaction:compaction [NC:C] ratio >2.3) to CMR data from the S2P Study population identified 959 (18.6%) cases of LVNC.5,6  This observational finding, which is at odds with published estimates of LVNC prevalence,14  prompted us to examine information from the S2P Study more closely by using CMR-based metrics to determine whether the cases of LVNC detected by applying the Petersen criterion were truly abnormal or just normal anatomic variants.

TABLE I.

Cardiac Magnetic Resonance-Based Criteria for Diagnosis of Left Ventricular Noncompaction

Cardiac Magnetic Resonance-Based Criteria for Diagnosis of Left Ventricular Noncompaction
Cardiac Magnetic Resonance-Based Criteria for Diagnosis of Left Ventricular Noncompaction

Patients and Methods

Data for the present study were obtained from the S2P Study database. The 5,169 voluntary participants in the S2P Study (mean age, 13.1 ± 1.78 yr) were a representative sample of the general population of children 11 to 18 years old in Harris County (Houston), Texas. Of the 959 participants in the S2P Study identified as having LVNC, 511 had short-axis CMR imaging results available in the database. This cohort, called the Short-Axis Study Set, was the subject of the current analyses.

Data collected in the S2P Study included demographics, vital signs, brief cardiac medical histories, CMR-based screening data, resting ECGs, and results of overall screening evaluations by cardiology experts. The CMR imaging protocol for the S2P Study, which was designed for primary screening and not full-fledged clinical imaging, included acquisition of long-axis views (2–chamber, 4–chamber, and LV outflow tract), acquisition of short-axis views (for many but not all cases), and sequences for detecting coronary artery origins and proximal courses. Sixteen-lead ECGs were acquired and reviewed by an experienced electrophysiologist. These data were acquired, evaluated, deidentified, and used as provided for in the institutional review board-approved protocol for the S2P Study.5,6 

Short-Axis Image Measurements

The current analysis relied on the use of long– and short-axis CMR images acquired in the S2P Study. Long-axis CMR imaging enables rapid detection of a distinct layer of excessive ventricular trabeculation. Short-axis imaging permits more accurate delineation of the compact LV myocardial layer and the contained ventricular cavity; when present, the additional layer of ventricular trabeculae can also be distinguished from the compact layer. Of note, however, measurements derived from both long– and short-axis images are subject to bias and error due to imaging plane location and orientation; in addition, nonperpendicularity relative to the long axis of short-axis images may introduce systematic errors that might be accentuated in the apical region.

Although current imaging technology does not enable full resolution of the complexities of ventricular trabeculae, the trained observer can distinguish gross features, including the location, local apparent thickness, and distinctive layering of trabeculae against the compact myocardial walls and their irregular boundary with the ventricular cavity. Therefore, the conventional tools and methods for quantifying imaging-derived myocardial variables in the normal heart were used to evaluate LV trabeculation, especially the volume of the trabeculated region.

The short-axis imaging stacks used for screening in the S2P Study typically included 8 slices, each 8 mm thick and separated by 1 mm. The slices were acquired from just below the mitral valve to near the apex. Additional technical details of the CMR imaging procedure are available elsewhere.5,6 

Clear and consistent short-axis images of the LV cavity, along with clear delineations of the trabeculated layer and compact myocardium, were obtained for the Short-Axis Study Set at both end-diastole and end-systole. The epicardial and endocardial contours were drawn by 2 experienced cardiac radiologists (CU and BYC). To quantify areas of excessive trabeculation, borders were drawn between the compact myocardium and the trabeculated layer, as well as between the trabeculated layer and the open ventricular cavity (Fig. 1).

Fig. 1

Representative cardiac magnetic resonance images show A) 2-chamber and B) 4-chamber views of the epicardial contour of the left ventricle, as well as the endocardial contours of the compact layer and trabeculated (noncompact) region; short-axis views of the epicardial and endocardial contours at end-diastole at C) basal, D) mid, and E) apical levels; and short-axis views of the epicardial and endocardial contours at end-systole at F) basal, G) mid, and H) apical levels. Epicardial contours are green; endocardial contours, red; trabeculated (noncompact) region contours, blue. The dotted blue line shown in the 2-chamber view in A) indicates the left ventricle's conventionally defined basal limit. The numbered yellow line segments shown in A) through E) are included to illustrate compact and trabeculated (noncompact) layer thicknesses in the different images of the left ventricle as they might be used in applying the Petersen criterion.

Fig. 1

Representative cardiac magnetic resonance images show A) 2-chamber and B) 4-chamber views of the epicardial contour of the left ventricle, as well as the endocardial contours of the compact layer and trabeculated (noncompact) region; short-axis views of the epicardial and endocardial contours at end-diastole at C) basal, D) mid, and E) apical levels; and short-axis views of the epicardial and endocardial contours at end-systole at F) basal, G) mid, and H) apical levels. Epicardial contours are green; endocardial contours, red; trabeculated (noncompact) region contours, blue. The dotted blue line shown in the 2-chamber view in A) indicates the left ventricle's conventionally defined basal limit. The numbered yellow line segments shown in A) through E) are included to illustrate compact and trabeculated (noncompact) layer thicknesses in the different images of the left ventricle as they might be used in applying the Petersen criterion.

Epicardial and endocardial contours were analyzed to generate volume estimates for the LV cavity, trabeculated region, and compact myocardium according to established standards.19  From these measurements, values for other structural and functional variables were derived. Contouring and measurement of CMR images were done with use of cvi42 image-postprocessing software (Circle Cardiovascular Imaging Inc.). Conventional image-processing software was used to generate volume and mass estimates for the various structures delineated by the contours described above and displayed in Figure 1. For example, the green and red contours were analyzed with the cvi42 software to estimate the volume and mass of the compact LV myocardium. The green and blue contours were analyzed to obtain aggregate volume estimates of compact and trabeculated (noncompact) LV myocardium. The trabeculated LV volume was then obtained by difference. Last, estimates of trabeculated LV volume at end-diastole and end-systole were obtained and used to assess changes in trabeculated LV myocardium across the cardiac cycle.

Trabeculated Left Ventricular Mapping

For each case, the short-axis images were used to map the trabeculated LV as described above and to measure its areal extent and the thicknesses of noncompact (trabeculated) and compact myocardial tissue at end-diastole. Each short-axis image slice was subdivided into 6 segments for this purpose. The fraction of all segments in which trabeculation met the Petersen criterion (NC:C ratio, >2.3) served as a proxy for the interior LV surface area covered with trabeculae meeting that criterion.

Measurement Consistency

As a first step toward better characterizing the effects of excessive LV trabeculation, we used conventional volumetric measurements derived from short-axis LV images to generate a 2-dimensional, quantitative description of LV trabeculation in terms of the following:

  • EFe = LV ejection fraction (LVEF) estimated with trabeculae and papillary muscles excluded from the blood pool;

  • VdT = volume of trabeculae at end-diastole as estimated from LV images;

  • VsT = volume of trabeculae at end-systole estimated from LV images; and

  • LVMc = mass of LV compact myocardium estimated from short-axis LV images.

To ensure consistency and to avoid systematic errors, all imaging-based volumetric measurements were subjected to the following logic checks:

  • The aggregate volume of LV trabeculae and compact myocardium at end-diastole was greater than at end-systole.

  • The aggregate volume of LV trabeculae and compact myocardium at end-systole was greater than that of the compact myocardium alone.

  • The LV cavity volume with trabeculae included in the blood pool was greater than the LV cavity volume with trabeculae excluded from the blood pool, at both end-diastole and end-systole.

  • The LV stroke volume estimated from contours that included trabeculae in the blood pool was greater than the LV stroke volume estimated from contours that excluded trabeculae from the blood pool.

Net Relative Trabeculation Mass

We used short-axis LV images to estimate more accurately the trabeculated LV mass, specifically excluding the mass of the blood contained in the intertrabecular luminal spaces. This variable was defined as the net relative trabeculation mass (NRTM).

Estimates for NRTM were derived by using the values for EFe, VdT, and VsT derived from analysis of short-axis LV images and also by defining ΦdT as the fraction of trabeculated LV volume occupied by intertrabecular luminal spaces at end-diastole. Hence, this gave:  
formula
which describes the volume of blood contained in intertrabecular luminal spaces at end-diastole.
For intertrabecular luminal spaces open to the ventricular cavity, we assumed that the fraction of blood volume ejected at systole from those intertrabecular luminal spaces was equal to the fraction ejected from the ventricular cavity. Thus, equating the calculation of the former fraction with the separate estimate of the latter fraction enabled calculation of ΦdT as follows:  
formula
where 0 < ΦdT < 1.
The net mass of LV trabeculations (mT), which excluded the mass of the blood contained in the intertrabecular luminal spaces at end-diastole, was given by the following equation:  
formula
where the factor 1.05 is the assumed density (g/mL) of trabeculated tissue.
The value of NRTM, expressed as a percentage of total LV mass, was given by the following equation:  
formula
where LVMc is the mass of LV compact myocardium estimated from short-axis LV images.

Estimation of Left Ventricular Noncompaction Prevalence

To estimate the prevalence of LVNC, we applied the CMR-based criteria for LVNC diagnosis proposed by the Choi,8  Jacquier,11  and Grothoff9  groups (Table I) to the LV volume and mass estimates that we had derived from measurements of the short-axis images for the Short-Axis Study Set.

Statistical Analysis

Measurements of LV variables were indexed to body surface area (BSA) (computed by the Mosteller formula) where applicable. Continuous data were summarized as mean ± SD. Statistical comparisons were performed with use of t tests. P <0.05 was considered statistically significant. Pearson and intraclass correlation coefficient analyses and Bland-Altman analyses were performed to evaluate measurement repeatability. Stata version 14.0 software (StataCorp LLC) was used for all analyses.

Results

The Short-Axis Study Set (N=511) was comparable to the full S2P Study cohort (N=5,169) with regard to age, race, ethnicity, and sex distribution (Table II). Ninety-four (18.4%) participants in the Short-Axis Study Set reported symptoms: shortness of breath with exercise in 60 (11.7%), chest pain in 22 (4.3%), syncope with exercise in 7 (1.4%), and syncope at rest in 5 (1%). Thirteen participants had cardiac disorders: hypertrophic cardiomyopathy in 2; dilated cardiomyopathy in 3; anomalous origin of coronary arteries in 3; and the ECG abnormalities Wolff-Parkinson-White syndrome in 2, prolonged QT interval (QTc >470 ms) in 2, and fixed ventricular bigeminy in one.

TABLE II.

Demographic Characteristics of the Short-Axis Study Set

Demographic Characteristics of the Short-Axis Study Set
Demographic Characteristics of the Short-Axis Study Set

Left Ventricular Volume Estimates

Left ventricular end-diastolic volume (LVEDV) estimates were affected by the presence of trabeculae and their inclusion in or exclusion from the blood pool. Supplemental Table I presents data from 2 sample cases to demonstrate how we derived our estimates of LV trabeculation-related variables from conventional imaging-derived volumetric measurements. Supplemental Table II presents BSA-indexed summary statistics for those variables for the entire Short-Axis Study Set.

The mean indexed LVEDV was significantly greater when trabeculae were included in the blood pool (76.9 ± 12.2 vs 49.2 ± 9.6 mL/m2; P <0.001) (Fig. 2). The trabeculated LV region had a spectrum of presentations and a measurable volume at end-diastole (mean, 25.5 ± 8.1 mL/m2). The volume of blood contained in intertrabecular luminal spaces was therefore dependent on the extent and structure of the trabeculated region (Fig. 3).

Fig. 2

Graphs show the frequency of measurements of left ventricular end-diastolic volume (LVEDV) indexed to body surface area (BSA) with trabeculae A) included in the blood pool (mean, 76.9 ± 12.2 mL/m2) and B) excluded from the blood pool (mean, 49.2 ± 9.6 mL/m2) in the Short-Axis Study Set (N=511). The mean LVEDV was significantly greater when trabeculae were included (P <0.001).

Fig. 2

Graphs show the frequency of measurements of left ventricular end-diastolic volume (LVEDV) indexed to body surface area (BSA) with trabeculae A) included in the blood pool (mean, 76.9 ± 12.2 mL/m2) and B) excluded from the blood pool (mean, 49.2 ± 9.6 mL/m2) in the Short-Axis Study Set (N=511). The mean LVEDV was significantly greater when trabeculae were included (P <0.001).

Fig. 3

Graph shows the frequency of measurements of nonapical left ventricular (LV) trabeculation volume indexed to body surface area (BSA) (mean, 25.2 ± 8.1 mL/m2) in the Short-Axis Study Set (N=511).

Fig. 3

Graph shows the frequency of measurements of nonapical left ventricular (LV) trabeculation volume indexed to body surface area (BSA) (mean, 25.2 ± 8.1 mL/m2) in the Short-Axis Study Set (N=511).

The trabeculated region was apparently compressed across the cardiac cycle. The mean reduction in its volume at end-systole was 52.5% ± 17.2%, reflecting primarily compression of the intertrabecular luminal spaces (Fig. 4). Thus, blood ejected from the intertrabecular luminal spaces at end-systole necessarily became part of the total LV stroke volume, and the wide range of trabeculated region compression suggests a varied gross structure (that is, a spectrum of intertrabecular luminal space volumes).

Fig. 4

Graph shows the frequency of measurements of nonapical left ventricular (LV) trabeculated region compression in terms of percent reduction from end-diastole to end-systole (mean, 52.5 ± 17.2%), an indication of its contribution to LV end-systolic volume, in the Short-Axis Study Set (N=511).

Fig. 4

Graph shows the frequency of measurements of nonapical left ventricular (LV) trabeculated region compression in terms of percent reduction from end-diastole to end-systole (mean, 52.5 ± 17.2%), an indication of its contribution to LV end-systolic volume, in the Short-Axis Study Set (N=511).

Including papillary muscles and trabeculae in the LV blood pool resulted in the systematic underestimation of LVEF (Fig. 5).

Fig. 5

Scatter plot shows the distribution of left ventricular ejection fraction (LVEF) in the Short-Axis Study Set (N=511), as estimated with trabeculae and papillary muscles included in (x-axis) or excluded from (y-axis) the blood pool. The line of best fit is shown in purple; the line of identity, in black.

Fig. 5

Scatter plot shows the distribution of left ventricular ejection fraction (LVEF) in the Short-Axis Study Set (N=511), as estimated with trabeculae and papillary muscles included in (x-axis) or excluded from (y-axis) the blood pool. The line of best fit is shown in purple; the line of identity, in black.

Measurement Repeatability

The repeatability of imaging-derived volumetric measurements was evaluated in a randomly selected sample of 28 (5.5%) cases to check the consistency of the initial contouring done as described above. In brief, repeat measurements of 7 distinct variables derived from primary volumetric estimates were generated by the image-postprocessing software from the contours as initially drawn at baseline. Baseline and repeat measurements were made approximately one year apart. As expected, and as shown by correlation and bias analyses, measurements at end-systole were more susceptible to error (Table III). These errors were propagated because LV stroke volume estimates were affected by the presence of trabeculae.

TABLE III.

Imaging Measurement Repeatability

Imaging Measurement Repeatability
Imaging Measurement Repeatability

Estimated Prevalence of Left Ventricular Noncompaction

After applying the criterion of Jacquier and associates11  to the Short-Axis Study Set, we identified 482 cases as abnormal, a prevalence of 17.5%. In contrast, after applying the criterion of Choi and associates,8  we identified 205 cases as abnormal, a prevalence of 7.4%. This mismatch between these 2 estimates of LVNC prevalence is shown in Figure 6. In addition, our trabeculation modeling indicated that 163 (17%) of cases in the Short-Axis Study Set met the Petersen criterion15  but had trabeculae of no significant mass (that is, open, lace-like trabeculae that on long-axis images appeared to be very thick). Thus, relatively few of the LVNC cases in the Short-Axis Study Set that met the Petersen criterion also had high NRTM values (Fig. 7).

Fig. 6

Scatter plot shows the distribution of cases of left ventricular noncompaction identified by applying the criteria of Choi and colleagues (trabeculated LV volume >35% of total left ventricular [LV] volume)8  and Jacquier and associates (trabeculated LV mass >20% of total LV mass)11  to the Short-Axis Study Set (N=511).

Fig. 6

Scatter plot shows the distribution of cases of left ventricular noncompaction identified by applying the criteria of Choi and colleagues (trabeculated LV volume >35% of total left ventricular [LV] volume)8  and Jacquier and associates (trabeculated LV mass >20% of total LV mass)11  to the Short-Axis Study Set (N=511).

Fig. 7

Graph shows the frequency of estimates of left ventricular (LV) net relative trabeculation mass (NRTM), expressed as percentage of total LV mass, in the Short-Axis Study Set (N=511). The NRTM excludes the mass of the blood contained in intertrabecular luminal spaces.

Fig. 7

Graph shows the frequency of estimates of left ventricular (LV) net relative trabeculation mass (NRTM), expressed as percentage of total LV mass, in the Short-Axis Study Set (N=511). The NRTM excludes the mass of the blood contained in intertrabecular luminal spaces.

We also applied the CMR mass-based criteria proposed by Grothoff and associates9  and our own method for estimating trabeculated LV mass in terms of NRTM (Fig. 8). This identified 35 (3.7%) cases in the Short-Axis Study Set as abnormal, corresponding to a prevalence of 1.3% when this finding was extrapolated to the larger S2P Study cohort.

Fig. 8

Scatter plot shows the distribution of cases of left ventricular noncompaction (LVNC) in the Short-Axis Study Set (N=511) identified by applying the criterion of Grothoff and colleagues9  and our method for estimating trabeculated LV mass in terms of net relative trabeculation mass (NRTM). The estimated LVNC prevalence was 1.3% when these data were extrapolated to the original Screen to Prevent Study cohort.

Fig. 8

Scatter plot shows the distribution of cases of left ventricular noncompaction (LVNC) in the Short-Axis Study Set (N=511) identified by applying the criterion of Grothoff and colleagues9  and our method for estimating trabeculated LV mass in terms of net relative trabeculation mass (NRTM). The estimated LVNC prevalence was 1.3% when these data were extrapolated to the original Screen to Prevent Study cohort.

Discussion

Our detailed analysis of 511 CMR-detected cases of LVNC, originally identified according to the Petersen criterion,15  suggests that many were normal anatomic variants. After we applied the criteria of Grothoff and colleagues9  to our cohort, the estimated prevalence of LVNC was much lower than that in our original S2P Study. The Petersen criterion is too broad, but other criteria (for example, those proposed by the Grothoff,9  Jacquier,11  and Choi8  groups) may be no more reliable, as we have shown in this analysis.

Left ventricular noncompaction is a poorly understood cardiomyopathy20  with various reported phenotypes (for example, isolated or concomitant with hypertrophic or dilated cardiomyopathy) that may change over time.21  Its cause is uncertain.7,20,21  Multiple genetic markers have been identified, and potential familial involvement is often cited.2234  As annotated in the ClinVar public archive of genetic variants35  and reported elsewhere,2225,2736  more than 70 pathogenic or likely pathogenic variants in more than 20 genes have been deemed to be of clinical significance in LVNC. Not all of those genetic variants may cause disease, and mutations in several LVNC-linked genes are also linked with other cardiomyopathies.

Current diagnostic criteria for LVNC are unreliable,718,23  and treatment is driven by phenotype and symptoms and aimed at preventing or treating heart failure.3740  There is no cure. Neonates and very young children with more severe phenotypes have poor outcomes and low survival rates.2,39,41  Some young people with isolated LVNC may have a better prognosis.23,42 

It is unclear whether poor outcomes in the young are caused by LVNC, or whether LVNC is secondary to another cause of those outcomes.43,44  Case reports repeatedly point to the clinical complexity of LVNC cases,4557  and numerous other studies document the limited understanding of this condition.7,50,58,59  Asymptomatic LVNC is often identified incidentally, and its clinical management typically includes follow-up with prophylactic measures, such as anticoagulation and antiarrhythmia therapies, and restrictions on physical activity.3740  The effect of these restrictions on lifestyle choices for children is complex and not fully understood.60,61  The change in some LVNC phenotypes over time is of concern for children as they develop from prepubescence to early adolescence. Evolving LVNC phenotypes require adaptive clinical surveillance and treatment strategies.20 

Limitations

This work was based on cross-sectional CMR-based data obtained at primary screening during the S2P Study and focused on analyzing objective measurements derived from those data. Longitudinal measurements may provide more insight. Participants in the S2P primary screening study took part voluntarily and, therefore, do not constitute a rigorous sample of the general population. That said, the demographics of the S2P Study population compare favorably with those of the general population in the greater Houston, Texas, area.5 

In analyzing short-axis images from the 511 participants in the Short-Axis Study Set, we considered only the nonapical regions of the heart and thus may have underestimated trabeculated LV mass. We made no attempt to exclude the papillary muscles from the regional volume measurements of LV trabeculae. Although this may have introduced a systematic but limited bias into our estimates of trabeculated LV mass, we think that this bias had little effect on estimates of the volume of nonapical intertrabecular luminal spaces.

No clinical assessments were performed to confirm LVNC in study participants. In addition, we did not acquire any longitudinal follow-up data that would help to detect changes or provide information on clinical outcomes.

The cases of LVNC that we identified during screening in the S2P Study included several cardiac disorders associated with LVNC; however, confirmatory clinical evaluations and longitudinal follow-up are needed to ascertain their clinical course. One-time screening does not allow detection of changes in a cardiac disorder over time, and small sample size impedes reliable estimation of the underlying prevalence of each of those disorders.

Observer-related variability and measurement error, especially regarding the visual delineation of the irregular features of ventricular trabeculae, markedly affect the imaging-derived quantification of cardiac variables. Automated image quantification will improve measurement accuracy and precision and help provide information that could be used to reliably evaluate patients presenting with excessive trabeculation. Quantification of LV function and mass by contour drawing is widespread, and there is good understanding of measurement variability62  and the usefulness of CMR-based measurement in defining significant changes.63  We acknowledge that automation of CMR image analysis, although not routine, is advancing. Examples include automated endocardial border detection,6466  selection of short-axis slices for automated quantification,67  LV segmentation and finite element modeling,68,69  application of “machine learning” to contouring,70  and fractal analysis of myocardial trabeculation.71 

Conclusions

Our findings suggest that many of the 511 suspected cases of LVNC identified in our previous S2P Study may have been normal anatomic variants and that many could be reclassified as normal on the basis of our current findings. The remaining cases, including those that were largely asymptomatic, may have involved subclinical changes in LV structure and function. Therefore, the true prevalence of pathologically excessive LV trabeculation in our 11- to 18-year-old cohort in the S2P Study is probably <1%, while the severity and clinical significance of potential LVNC remain unknown. Young people presenting with excessive LV trabeculation, even if asymptomatic, may exhibit distinct structural features that are physiologically disadvantageous. Whether aggravated by exertion (for example, by participation in sports) or by normal development from puberty into early adolescence, the eventual cumulative effect may be reduced ventricular function, with its associated complications and risks. Excessive LV trabeculation, even if initially asymptomatic, may serve as a substrate for malignant arrhythmias or thrombus formation. In light of our findings based on a population sample, longitudinal follow-up of individuals with suspected LVNC is warranted in general. A more detailed analysis of other CMR-based metrics in the Short-Axis Study Set will be presented in the future.

Acknowledgments

Raja Muthupillai, PhD, provided the CMR imaging protocol and guidance regarding imaging quality control. Veronica V. Lenge De Rosen, MD, reviewed the CMR screening data to help identify potential abnormalities. Stephen N. Palmer, PhD, ELS, contributed to the editing of the manuscript.

Supplementary Material

Supplemental tables for this article are available at 10.14503_thij-19-7157.s1.pdf.

References

References
1.
Gati
S,
Papadakis
M,
Papamichael
ND,
Zaidi
A,
Sheikh
N,
Reed
M,
et al.
Reversible de novo left ventricular trabeculations in pregnant women: implications for the diagnosis of left ventricular noncompaction in low-risk populations
.
Circulation
2014
;
130
(
6
):
475
83
.
2.
Koh
C,
Lee
PW,
Yung
TC,
Lun
KS,
Cheung
YF.
Left ventricular noncompaction in children
.
Congenit Heart Dis
2009
;
4
(
4
):
288
94
.
3.
Stanton
C,
Bruce
C,
Connolly
H,
Brady
P,
Syed
I,
Hodge
D,
et al.
Isolated left ventricular noncompaction syndrome
.
Am J Cardiol
2009
;
104
(
8
):
1135
8
.
4.
Gati
S,
Rajani
R,
Carr-White
GS,
Chambers
JB.
Adult left ventricular noncompaction: reappraisal of current diagnostic imaging modalities
.
JACC Cardiovasc Imaging
2014
;
7
(
12
):
1266
75
.
5.
Angelini
P,
Cheong
BY,
Lenge De Rosen
VV,
Lopez
A,
Uribe
C,
Masso
AH,
et al.
High-risk cardiovascular conditions in sports-related sudden death: prevalence in 5,169 schoolchildren screened via cardiac magnetic resonance
.
Tex Heart Inst J
2018
;
45
(
4
):
205
13
.
6.
Angelini
P,
Cheong
BY,
Lenge De Rosen
VV,
Lopez
JA,
Uribe
C,
Masso
AH,
et al.
Magnetic resonance imaging-based screening study in a general population of adolescents
.
J Am Coll Cardiol
2018
;
71
(
5
):
579
80
.
7.
Anderson
RH,
Jensen
B,
Mohun
TJ,
Petersen
SE,
Aung
N,
Zemrak
F,
et al.
Key questions relating to left ventricular noncompaction cardiomyopathy: is the emperor still wearing any clothes?
Can J Cardiol
2017
;
33
(
6
):
747
57
.
8.
Choi
Y,
Kim
SM,
Lee
SC,
Chang
SA,
Jang
SY,
Choe
YH.
Quantification of left ventricular trabeculae using cardiovascular magnetic resonance for the diagnosis of left ventricular non-compaction: evaluation of trabecular volume and refined semi-quantitative criteria
.
J Cardiovasc Magn Reson
2016
;
18
(
1
):
24
.
9.
Grothoff
M,
Pachowsky
M,
Hoffmann
J,
Posch
M,
Klaassen
S,
Lehmkuhl
L,
Gutberlet
M.
Value of cardiovascular MR in diagnosing left ventricular non-compaction cardiomyopathy and in discriminating between other cardiomyopathies
.
Eur Radiol
2012
;
22
(
12
):
2699
709
.
10.
Ivanov
A,
Dabiesingh
DS,
Bhumireddy
GP,
Mohamed
A,
Asfour
A,
Briggs
WM,
et al.
Prevalence and prognostic significance of left ventricular noncompaction in patients referred for cardiac magnetic resonance imaging
.
Circ Cardiovasc Imaging
2017
;
10
(
9
):
e006174
.
11.
Jacquier
A,
Thuny
F,
Jop
B,
Giorgi
R,
Cohen
F,
Gaubert
JY,
et al.
Measurement of trabeculated left ventricular mass using cardiac magnetic resonance imaging in the diagnosis of left ventricular non-compaction
.
Eur Heart J
2010
;
31
(
9
):
1098
104
.
12.
Kini
V,
Ferrari
VA,
Han
Y,
Jha
S.
Adherence to thresholds: overdiagnosis of left ventricular noncompaction cardiomyopathy
.
Acad Radiol
2015
;
22
(
8
):
1016
9
.
13.
Niemann
M,
Stork
S,
Weidemann
F.
Left ventricular noncompaction cardiomyopathy: an overdiagnosed disease
.
Circulation
2012
;
126
(
16
):
e240
3
.
14.
Paterick
TE,
Tajik
AJ.
Left ventricular noncompaction: a diagnostically challenging cardiomyopathy
.
Circ J
2012
;
76
(
7
):
1556
62
.
15.
Petersen
SE,
Selvanayagam
JB,
Wiesmann
F,
Robson
MD,
Francis
JM,
Anderson
RH,
et al.
Left ventricular non-compaction: insights from cardiovascular magnetic resonance imaging
.
J Am Coll Cardiol
2005
;
46
(
1
):
101
5
.
16.
Stollberger
C,
Finsterer
J.
Pitfalls in the diagnosis of left ventricular hypertrabeculation/non-compaction
.
Postgrad Med J
2006
;
82
(
972
):
679
83
.
17.
Weir-McCall
JR,
Yeap
PM,
Papagiorcopulo
C,
Fitzgerald
K,
Gandy
SJ,
Lambert
M,
et al.
Left ventricular noncompaction: anatomical phenotype or distinct cardiomyopathy?
J Am Coll Cardiol
2016
;
68
(
20
):
2157
65
.
18.
Jenni
R,
Oechslin
E,
Schneider
J,
Attenhofer Jost
C,
Kaufmann
PA.
Echocardiographic and pathoanatomical characteristics of isolated left ventricular non-compaction: a step towards classification as a distinct cardiomyopathy
.
Heart
2001
;
86
(
6
):
666
71
.
19.
Schulz-Menger
J,
Bluemke
DA,
Bremerich
J,
Flamm
SD,
Fogel
MA,
Friedrich
MG,
et al.
Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) Board of Trustees Task Force on Standardized Post Processing
.
J Cardiovasc Magn Reson
2013
;
15
:
35
.
20.
Jefferies
JL,
Wilkinson
JD,
Sleeper
LA,
Colan
SD,
Lu
M,
Pahl
E,
et al.
Cardiomyopathy phenotypes and outcomes for children with left ventricular myocardial noncompaction: results from the Pediatric Cardiomyopathy Registry
.
J Card Fail
2015
;
21
(
11
):
877
84
.
21.
Braunwald
E.
Cardiomyopathies: an overview
.
Circ Res
2017
;
121
(
7
):
711
21
.
22.
Arbustini
E,
Favalli
V,
Narula
N,
Serio
A,
Grasso
M.
Left ventricular noncompaction: a distinct genetic cardiomyopathy?
J Am Coll Cardiol
2016
;
68
(
9
):
949
66
.
23.
Brescia
ST,
Rossano
JW,
Pignatelli
R,
Jefferies
JL,
Price
JF,
Decker
JA,
et al.
Mortality and sudden death in pediatric left ventricular noncompaction in a tertiary referral center
.
Circulation
2013
;
127
(
22
):
2202
8
.
24.
D'Amato
G,
Luxan
G,
del Monte-Nieto
G,
Martinez-Poveda
B,
Torroja
C,
Walter
W,
et al.
Sequential Notch activation regulates ventricular chamber development
.
Nat Cell Biol
2016
;
18
(
1
):
7
20
.
25.
Garcia-Pavia
P,
de la Pompa
JL
.
Left ventricular noncompaction: a genetic cardiomyopathy looking for diagnostic criteria [published erratum appears in J Am Coll Cardiol 2015;65(5):519]
.
J Am Coll Cardiol
2014
;
64
(
19
):
1981
3
.
26.
Jensen
B,
van der Wal
AC,
Moorman
AFM,
Christoffels
VM.
Excessive trabeculations in noncompaction do not have the embryonic identity
.
Int J Cardiol
2017
;
227
:
325
30
.
27.
Miszalski-Jamka
K,
Jefferies
JL,
Mazur
W,
Glowacki
J,
Hu
J,
Lazar
M,
et al.
Novel genetic triggers and genotype-phenotype correlations in patients with left ventricular noncompaction
.
Circ Cardiovasc Genet
2017
;
10
(
4
):
e001763
.
28.
Morcos
PN,
Andersson
UME,
Adler
ED.
Left ventricular non-compaction: current controversy and new insights
.
J Genet Syndr Gene Ther
2015
;
6
(
1
).
doi:
.
29.
Oechslin
E,
Jenni
R.
Left ventricular non-compaction revisited: a distinct phenotype with genetic heterogeneity?
Eur Heart J
2011
;
32
(
12
):
1446
56
.
30.
Quarta
G,
Papadakis
M,
Donna
PD,
Maurizi
N,
Iacovoni
A,
Gavazzi
A,
et al.
Grey zones in cardiomyopathies: defining boundaries between genetic and iatrogenic disease
.
Nat Rev Cardiol
2017
;
14
(
2
):
102
12
.
31.
Samsa
LA,
Yang
B,
Liu
J.
Embryonic cardiac chamber maturation: trabeculation, conduction, and cardiomyocyte proliferation
.
Am J Med Genet C Semin Med Genet
2013
;
163C
(
3
):
157
68
.
32.
Sasse-Klaassen
S,
Probst
S,
Gerull
B,
Oechslin
E,
Nurnberg
P,
Heuser
A,
et al.
Novel gene locus for autosomal dominant left ventricular noncompaction maps to chromosome 11p15
.
Circulation
2004
;
109
(
22
):
2720
3
.
33.
Towbin
JA,
Jefferies
JL.
Cardiomyopathies due to left ventricular noncompaction, mitochondrial and storage diseases, and inborn errors of metabolism
.
Circ Res
2017
;
121
(
7
):
838
54
.
34.
Zhang
W,
Chen
H,
Qu
X,
Chang
CP,
Shou
W.
Molecular mechanism of ventricular trabeculation/compaction and the pathogenesis of the left ventricular noncompaction cardiomyopathy (LVNC)
.
Am J Med Genet C Semin Med Genet
2013
;
163C
(
3
):
144
56
.
35.
Landrum
MJ,
Lee
JM,
Benson
M,
Brown
G,
Chao
C,
Chitipiralla
S,
et al.
ClinVar: public archive of interpretations of clinically relevant variants
.
Nucleic Acids Res
2016
;
44
(
D1
):
D862
8
.
36.
Richard
P,
Ader
F,
Roux
M,
Donal
E,
Eicher
JC,
Aoutil
N,
et al.
Targeted panel sequencing in adult patients with left ventricular non-compaction reveals a large genetic heterogeneity
.
Clin Genet
2019
;
95
(
3
):
356
67
.
37.
Bennett
CE,
Freudenberger
R.
The current approach to diagnosis and management of left ventricular noncompaction cardiomyopathy: review of the literature
.
Cardiol Res Pract
2016
;
2016
:
5172308
.
38.
Lee
TM,
Hsu
DT,
Kantor
P,
Towbin
JA,
Ware
SM,
Colan
SD,
et al.
Pediatric cardiomyopathies
.
Circ Res
2017
;
121
(
7
):
855
73
.
39.
Lipshultz
SE,
Cochran
TR,
Briston
DA,
Brown
SR,
Sambatakos
PJ,
Miller
TL,
et al.
Pediatric cardiomyopathies: causes, epidemiology, clinical course, preventive strategies and therapies
.
Future Cardiol
2013
;
9
(
6
):
817
48
.
40.
Pulignano
G,
Tinti
MD,
Tolone
S,
Musto
C,
De Lio
L,
Pino
PG,
et al.
Noncompaction and embolic myocardial infarction: the importance of oral anticoagulation
.
Rev Port Cardiol
2015
;
34
(
7–8
):
497.e1
4
.
41.
Pignatelli
RH,
McMahon
CJ,
Dreyer
WJ,
Denfield
SW,
Price
J,
Belmont
JW,
et al.
Clinical characterization of left ventricular noncompaction in children: a relatively common form of cardiomyopathy
.
Circulation
2003
;
108
(
21
):
2672
8
.
42.
Weisz
SH,
Limongelli
G,
Pacileo
G,
Calabro
P,
Russo
MG,
Calabro
R,
Vatta
M.
Left ventricular non compaction in children
.
Congenit Heart Dis
2010
;
5
(
5
):
384
97
.
43.
Greutmann
M,
Mah
ML,
Silversides
CK,
Klaassen
S,
Attenhofer Jost
CH,
Jenni
R,
Oechslin
EN.
Predictors of adverse outcome in adolescents and adults with isolated left ventricular noncompaction
.
Am J Cardiol
2012
;
109
(
2
):
276
81
.
44.
Thavendiranathan
P,
Dahiya
A,
Phelan
D,
Desai
MY,
Tang
WH.
Isolated left ventricular non-compaction controversies in diagnostic criteria, adverse outcomes and management
.
Heart
2013
;
99
(
10
):
681
9
.
45.
Aryal
MR,
Badal
M,
Giri
S,
Pradhan
R.
Left ventricular non-compaction presenting with heart failure and intramural thrombus
.
BMJ Case Rep
2013
;
2013
:
bcr2013009757
.
46.
Blinder
JJ,
Martinez
HR,
Craigen
WJ,
Belmont
J,
Pignatelli
RH,
Jefferies
JL.
Noncompaction of the left ventricular myocardium in a boy with a novel chromosome 8p23.1 deletion
.
Am J Med Genet A
2011
;
155A
(
9
):
2215
20
.
47.
Cohen
PJ,
Prahlow
JA.
Sudden death due to biventricular non-compaction cardiomyopathy in a 14-year-old
.
Forensic Sci Med Pathol
2015
;
11
(
1
):
92
8
.
48.
de Melo
MD,
Benvenuti
LA,
Mady
C,
Kalil-Filho
R,
Salemi
VM.
Left ventricular basal region involvement in noncompaction cardiomyopathy
.
Cardiovasc Pathol
2013
;
22
(
6
):
503
4
.
49.
Jacobs
K,
Giacobbe
L,
Aguilera
M,
Ramin
K,
Sivanandam
S.
A case of fetal diagnosis of noncompaction cardiomyopathy and coarctation of the aorta
.
AJP Rep
2014
;
4
(
1
):
45
8
.
50.
Oechslin
EN,
Attenhofer Jost
CH,
Rojas
JR,
Kaufmann
PA,
Jenni
R.
Long-term follow-up of 34 adults with isolated left ventricular noncompaction: a distinct cardiomyopathy with poor prognosis
.
J Am Coll Cardiol
2000
;
36
(
2
):
493
500
.
51.
Peters
F,
Dos Santos
C,
Essop
R.
Isolated left ventricular non-compaction with normal ejection fraction
.
Cardiovasc J Afr
2011
;
22
(
2
):
90
3
.
52.
Ridha
A,
Khan
A,
Al-Abayechi
S,
Yeung
E.
Clinical dilemma: recurrent syncope with isolated left ventricle non-compaction cardiomyopathy and preserved ejection fraction, a case report and review of the literature
.
J Med Cases
2015
;
6
(
2
):
77
80
.
53.
Ridha
M,
Batra
S,
Collins
J,
Montgomery
J,
Ho
D,
Voetsch
B.
Left ventricular non-compaction: a rare cause of stroke in the young
.
Neurology
2018
;
90
(
15 Suppl
):
p6.232
.
54.
Khanna
RR,
Garg
R,
Madan
H,
Mahajan
N,
Kumar
P.
Isolated ventricular non-compaction syndrome, rare cause of recurrent stroke in young - a case report
.
J Neurol Neurosci
2016
;
7
(
3
):
99
.
55.
Vinograd
CA,
Srivastava
S,
Panesar
LE.
Fetal diagnosis of left-ventricular noncompaction cardiomyopathy in identical twins with discordant congenital heart disease
.
Pediatr Cardiol
2013
;
34
(
6
):
1503
7
.
56.
Wald
R,
Veldtman
G,
Golding
F,
Kirsh
J,
McCrindle
B,
Benson
L.
Determinants of outcome in isolated ventricular noncompaction in childhood
.
Am J Cardiol
2004
;
94
(
12
):
1581
4
.
57.
Wats
K,
Chen
O,
Uppal
NN,
Batul
SA,
Moskovits
N,
Shetty
V,
Shani
J.
A rare case of renal infarct due to noncompaction cardiomyopathy: a case report and literature review
.
Case Rep Cardiol
2016
;
2016
:
6789149
.
58.
Kawel
N,
Nacif
M,
Arai
AE,
Gomes
AS,
Hundley
WG,
Johnson
WC,
et al.
Trabeculated (noncompacted) and compact myocardium in adults: the multi-ethnic study of atherosclerosis
.
Circ Cardiovasc Imaging
2012
;
5
(
3
):
357
66
.
59.
Shi
WY,
Moreno-Betancur
M,
Nugent
AW,
Cheung
M,
Colan
S,
Turner
C,
et al.
Long-term outcomes of childhood left ventricular noncompaction cardiomyopathy: results from a national population-based study
.
Circulation
2018
;
138
(
4
):
367
76
.
60.
Caselli
S,
Ferreira
D,
Kanawati
E,
Di Paolo
F,
Pisicchio
C,
Attenhofer Jost
C,
et al.
Prominent left ventricular trabeculations in competitive athletes: a proposal for risk stratification and management
.
Int J Cardiol
2016
;
223
:
590
5
.
61.
Gati
S,
Sharma
S.
CardioPulse: the dilemmas in diagnosing left ventricular non-compaction in athletes
.
Eur Heart J
2015
;
36
(
15
):
891
3
.
62.
Suinesiaputra
A,
Bluemke
DA,
Cowan
BR,
Friedrich
MG,
Kramer
CM,
Kwong
R,
et al.
Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours
.
J Cardiovasc Magn Reson
2015
;
17
:
63
.
63.
Moody
WE,
Edwards
NC,
Chue
CD,
Taylor
RJ,
Ferro
CJ,
Townend
JN,
Steeds
RP.
Variability in cardiac MR measurement of left ventricular ejection fraction, volumes and mass in healthy adults: defining a significant change at 1 year
.
Br J Radiol
2015
;
88
(
1049
):
20140831
.
64.
Chuang
ML,
Gona
P,
Hautvast
GL,
Salton
CJ,
Breeuwer
M,
O'Donnell
CJ,
Manning
WJ.
CMR reference values for left ventricular volumes, mass, and ejection fraction using computer-aided analysis: the Framingham Heart Study
.
J Magn Reson Imaging
2014
;
39
(
4
):
895
900
.
65.
Corsi
C,
Veronesi
F,
Lamberti
C,
Bardo
DM,
Jamison
EB,
Lang
RM,
Mor-Avi
V.
Automated frame-by-frame endocardial border detection from cardiac magnetic resonance images for quantitative assessment of left ventricular function: validation and clinical feasibility
.
J Magn Reson Imaging
2009
;
29
(
3
):
560
8
.
66.
Marino
M,
Veronesi
F,
Corsi
C.
Fully automated assessment of left ventricular volumes and mass from cardiac magnetic resonance images
.
Conf Proc IEEE Eng Med Biol Soc
2014
;
2014
:
1079
82
.
67.
Marino
M,
Corsi
C,
Maffessanti
F,
Patel
AR,
Mor-Avi
V.
Objective selection of short-axis slices for automated quantification of left ventricular size and function by cardiovascular magnetic resonance
.
Clin Imaging
2016
;
40
(
4
):
617
23
.
68.
Caiani
EG,
Colombo
A,
Pepi
M,
Piazzese
C,
Maffessanti
F,
Lang
RM,
Carminati
MC.
Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes
.
Europace
2014
;
16 Suppl 4
:
iv96
iv101
.
69.
Wang
L,
Pei
M,
Codella
NC,
Kochar
M,
Weinsaft
JW,
Li
J,
et al.
Left ventricle: fully automated segmentation based on spatiotemporal continuity and myocardium information in cine cardiac magnetic resonance imaging (LV-FAST)
.
Biomed Res Int
2015
;
2015
:
367583
.
70.
Bai
W,
Sinclair
M,
Tarroni
G,
Oktay
O,
Rajchl
M,
Vaillant
G,
et al.
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
.
J Cardiovasc Magn Reson
2018
;
20
(
1
):
65
.
71.
Cai
J,
Bryant
JA,
Le
TT,
Su
B,
de Marvao
A,
O'Regan
DP,
et al.
Fractal analysis of left ventricular trabeculations is associated with impaired myocardial deformation in healthy Chinese
.
J Cardiovasc Magn Reson
2017
;
19
(
1
):
102
.

Author notes

Funding for the Screen to Prevent (S2P) Study was generously supplied by the Kinder Foundation, the Brown Foundation in Houston, and the Texas Heart Institute's general funds.

Supplementary data