Early detection of left ventricular remodeling
Hendriks, Tom
DOI:
10.33612/diss.144600179
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Publication date: 2020
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Hendriks, T. (2020). Early detection of left ventricular remodeling. University of Groningen. https://doi.org/10.33612/diss.144600179
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General discussion and future
perspectives
GENERAL DISCUSSION
This thesis was aimed to provide novel insights into the effects of cardiovascular risk factors and myocardial infarction on LV remodeling. It also provides suggestions for improvement of early detection of adverse LV remodeling to improve primary prevention of cardiovascular events.
Part I: Cardiovascular risk factors
The first part of this thesis was aimed to provide more insight into mechanisms leading to LV remodeling by investigating associations between cardiovascular risk factors and LV remodeling. In Chapter 2, a dose-dependent association between smoking and impaired LV (and RV) systolic
function was observed, in terms of ejection fraction as well as myocardial strain measures, using a matched case-control approach. The observed associations were driven by daily smokers, suggesting a dose-dependent effect. Previous studies reporting on the effects of tobacco smoking on cardiac structure and function have shown contradictory results1-6. Most previous studies observed an
association between tobacco smoking and reduced systolic function and increased LV mass. In this study, tobacco smoking was not associated with increased LV mass. A possible explanation might be that LV hypertrophy has a very multifactorial etiology (e.g. metabolic, immunologic, vascular) and by using strict in- and exclusion criteria, a selection of relatively healthy population of UK Biobank participants was made, free of major cardiovascular disease and with body mass index and blood pressures within normal ranges. This strict pre-selection, combined with the further correction in the linear regression models, should more clearly show the independent effects of active tobacco smoking. More subtle changes in cardiac contractility were assessed using myocardial strain analyses. Several large epidemiological studies have suggested tobacco smoking to be a risk factor for impaired cardiac function and heart failure7,8, assumed to be driven
by myocardial infarctions. The observed results indicate that there is a direct relationship between tobacco smoking and subclinical impaired systolic function, although the possibility of myocardial ischemia or silent myocardial infarctions playing a role in the observed effects cannot be completely ruled out. Tobacco smoke consists of more than 5.000 toxic and carcinogenic chemicals9. The
(chronic) exposure to these toxic chemicals have potentially devastating effects on cardiac tissue, causing a complex cascade of inflammation, endothelial injury, dysfunction, cell death and fibro-fatty replacement10. Biochemically, tobacco smoking has been associated with increased levels of
biomarkers for increased wall stress and myocardial injury such as NT-proBNP and high-sensitive troponin T11. Although the study was not powered for it, no significant effect of occasional
smoking on cardiac structure and function was found. A large limiting factor for the study was the questionnaire design by the UK Biobank, the cohort in which the study was performed. Important parameters such as number of cigarettes smoked daily, as well as packyears, were not
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available in all participants. Besides smoking, hypertension is another important risk factor for myocardial infarction which generally does not lead to symptoms. This means that individuals who are affected often do not visit a medical professional until a symptomatic comorbidity such as myocardial ischemia due to coronary artery disease has manifested. Hypertension is the leading risk factor for deaths due to cardiovascular diseases, causing more than 40% of cardiovascular deaths12. The threshold of SBP and DBP that defines hypertension continues to be discussed, as
even small increases in blood pressure from thresholds of 115 mmHg SBP and 75 mmHg DBP have been associated with an increased risk of cardiovascular events13. In Chapter 3, the effect
of SBP on CMR-derived measures of LV structure and function was assessed using a Mendelian randomization approach by selecting 300 individuals with extremes of genetically predicted SBP. The study provided evidence for a causal relationship between lifelong exposure to increased SBP and adverse LV remodeling, namely an increased LV mass and increased LV global radial strain. To the authors’ knowledge, this study is the first to report associations between genetically predicted SBP and CMR-derived measures of cardiac structure and function. A large effect of genetically predicted SBP on LV mass was observed. These findings are in line with an earlier study that showed a significant association between genetically predicted SBP using 29 genetic variants and increased LV wall thickness as measured by echocardiography14. Similar associations
have been reported for phenotypic SBP15. LV mass and concentricity of the LV are known to be
strong predictors of incident cardiovascular events16. Although confidence intervals somewhat
overlapped, point estimates of the effect size of genetically predicted SBP were larger compared with phenotypic SBP. This is an expected result, as phenotypic SBP is a snapshot at a specific moment in time while genetically predicted SBP are stable and cumulative over a whole lifetime. In addition to the expected effects on LV mass, a strong association between genetically predicted SBP and increased LV radial strain was observed, which was also present for phenotypic SBP but to a much lesser extent. Previous studies have mostly reported associations between hypertension and impaired LV longitudinal strain, and in some cases also impaired circumferential strain17,18.
In these studies, myocardial strain was most significantly impaired in obese subjects with hypertension, while in the study presented in this thesis, obesity was an exclusion criterium. This could have affected the observed associations. Also, a prolonged exposure to high blood pressure can cause irreversible myocardial damage which can progress into to heart failure. The previously mentioned studies might have investigated individuals that had already suffered hypertension-related injury to the myocardium leading to subclinical functional impairment. So whereas genetically predicted SBP is initially associated with increased LV contractility and myocardial strain, a progressed stage of hypertensive disease resulting in myocardial damage will eventually lead to impairment of myocardial strain.
Part II: Myocardial infarction
In the second part of the thesis, the focus shifts from investigating subclinical LV remodeling in healthy individuals with cardiovascular risk factors to investigating a more severe phenotype of LV remodeling in patients who experienced a myocardial infarction. This part of the thesis was aimed towards identifying predictors of structural LV remodeling after myocardial infarction, assessing differences between imaging modalities in assessing LV remodeling, and investigating the empirical evidence supporting devices that were designed to restore the LV to its original shape. In Chapter 4, cardiac specific biochemical biomarkers were demonstrated to be the best
predictors of structural LV remodeling 4 months after myocardial infarction. LV wall thickness of non-infarcted myocardium was not correlated with infarct size, biomarkers for cardiac injury, or other LV remodeling parameters, suggesting that the presumed compensatory hypertrophic response of remote myocardium is not related to cardiac injury caused by ST-elevation myocardial infarction. The strongest predictor of LVEDV was NT-proBNP, a measure of LV wall stress, whereas the strongest predictor of LVESV was peak enzymatic infarct size. This is an expected result, as LVESV is the first to be affected in adverse LV remodeling post myocardial infarction19.
Predictors of LV dilatation after ST-elevation myocardial infarction in previous studies include infarct zone, wall motion score index, peak CK, extent of coronary artery disease, LV dyssynchrony, and early mitral regurgitation20-22. In daily clinical practice, physicians generally rely on enzymatic
infarct size to predict LV remodeling. The data from this study suggests that NT-proBNP 6-8 weeks after hospital admission is a better predictor of LV volume indices compared to (peak) Troponin T, CK, and CK-MB, and could be used more frequently in clinical practice for risk stratification and treatment optimization. LV mass was most strongly predicted by NT-proBNP at two weeks, in addition to age, sex, hypertension, and blood pressure. NT-proBNP has been frequently associated with LV mass and has been suggested to be used as a screening tool for LV hypertrophy23-25. In this study, predictors of remote LV wall thickness generally corresponded with
predictors of LV mass, apart from HbA1c and active smoking, which were both associated with a larger remote wall thickness. These results are counterintuitive, for both diabetes mellitus and smoking are associated with heart failure with reduced ejection fraction and not with heart failure with preserved ejection fraction7. Possibly, the proinflammatory effects of smoking and diabetes
mellitus increase the chances of a maladaptive hypertrophic response of the myocardium, in the long term increasing the chance of developing heart failure with reduced ejection fraction. After myocardial infarction, accurate measurements of LV structure and function are highly important as they are used for risk stratification. LVEF measurements are frequently used to determine clinical indications, e.g. for implantable cardioverter defibrillator implantation (LVEF ≤35%), heart failure pharmacotherapy (LVEF ≤40%), or classification of heart failure patients in the new category of heart failure with mid-range ejection fraction (LVEF 40-49%)26-28. Although
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function29, it currently has its disadvantages in terms of availability, time-consumption, and costs.
2D TTE is a widely available, bedside, time- and cost-effective alternative for CMR, and standard clinical care in patients hospitalized for ST-elevation myocardial infarction30,31. In Chapter 5, we
aimed to investigate the agreement between CMR and 2D TTE measurements of LV structure and function and assess potential sources of bias. In same-day 2D TTE and CMR assessments of a large ST-elevation myocardial infarction cohort, a substantial underestimation of LV volumes and overestimation of LV mass in 2D TTE compared to CMR was observed, which is consistent with existing literature6,32-35. Bias in LVEF measurements was small, but with a large range of
agreement. Only one similar study has been performed in myocardial infarction patients, including 150 patients, finding a slightly smaller bias in LV volumes with a similar range of agreement, and a roughly 10% wider range in limits of agreement between LVEF measurements34, highlighting the
importance of reproducible measurements in a core laboratory. To further understand the observed differences, the current study was, to the authors’ knowledge, the first study to assess the effect of potential confounders or sources of bias between 2D TTE and CMR measurements by applying linear regression analyses to find determinants of bias. Interestingly, body mass index was not associated with bias in LV mass, LV volumes, or LVEF, although it is known to negatively affect TTE image quality36. Even though it affects reliability of measurements due to reducing visibility
of endo- and epicardial borders, it appears to not lead to a structural under- or overestimation of 2D TTE measurements. A larger enzymatic infarct size (peak Troponin T) was associated with bias between 2D TTE and CMR-derived measurements of LVESV and LVEF, possibly resulting in an underestimation of LVESV and overestimation of LVEF by 2D TTE in patients with a large infarct size. This could explain the low sensitivity (52%) to detect LVEF <50% using 2D TTE and an even lower sensitivity (25%) to detect LVEF ≤35%, although this was only based on 8 patients. As image acquisition and post-processing was performed in adherence to clinical recommendations37,38, the observed differences are likely universal in character. These results
support the use of CMR in patients with large myocardial infarctions for clinical decision-making around implantable cardioverter-defibrillator implantation and pharmacologic treatment, and for accurate classification of heart failure categories in clinical trials. After myocardial infarction, drug therapies targeting neurohormonal pathways have shown to attenuate LV remodeling, which is associated with reduced long-term mortality39. The association between attenuation of LV
remodeling and improved outcome led to the hypothesis that surgical restoration of the original volume and ellipsoid shape of the LV could be beneficial in cases of severe LV remodeling. In Chapter 6, by critically reviewing existing literature, little empirical evidence was found for
the use of surgical and transcatheter LV restoration devices designed to restore the shape and function of the LV in cases of severe adverse LV remodeling. Both surgical and transcatheter LV restoration techniques consistently demonstrate improvements in quality-of-life measures and functional status, but currently fail to demonstrate a clear survival benefit. The umbrella-like
Parachute® device (Cardiokinetix, Redwood City, California, USA) could be beneficial in heart failure patients with a recent anterior MI, poor systolic function and a suitable LV anatomy by reducing cardiac dimensions and end diastolic wall stress. However, randomized controlled trials investigating its use have been terminated and it is uncertain whether investigation of the device will be continued. Theoretically, most potential benefit of LV restoration devices is early after myocardial infarction and should be aimed at preventing adverse LV remodeling, using devices that can alter LV mechanical properties, such as transcatheter injection of biomaterials in the infarcted region. This will require proper selection of patients at risk of adverse LV remodeling.
Part III: Improvements
In the third and final part of the thesis, the focus shifts from studying mechanisms of LV remodeling to investigating possible improvements to measures of LV structure of function detection in order to facilitate early detection of adverse LV remodeling. In Chapter 7 of
this thesis, an improved indexation of LV volumes using lean body mass and the R-R interval is proposed. In subjects of the UK Biobank with manually determined measures of cardiac structure and function, six independent determinants of both LVEDV and LVESV in the general population were observed. Both DXA-derived and impedance-derived lean body mass predicted substantially more variance in LV volumes than BSA, and the R-R interval during the imaging procedure was the second most important determinant of LV volumes, which can be accounted for using a simple correction. Narrow margins to account for physiological variation of clinical parameters are essential to early recognize subtle deviations from normal cardiac structure and function. In previous publications, LV volume indices showed large variation across sexes, age groups, and ethnicities, which has resulted in various categories of reference values29,40,41. If the
method of indexation used in clinical practice is improved, reference values could be simplified and possibly not require categories for age and ethnicities. In order to find the most optimal indexation methods, one has to understand why parameters such as cardiac output, cardiac size, glomerular filtration rate and respiratory function measures vary within individuals. In the human body, respiration, circulation and excretion need to be accommodated to the body’s metabolic rate. The ideal indexation would therefore be measurement of an individual’s metabolic rate using calorimetry42. Max Rubner, in 1883, found that BSA of dogs was proportional to their
metabolic rate42. Being unable to measure metabolic rate in a clinical setting, BSA seemed like
a good alternative to use for indexation. In the current study, DXA-corrected lean body mass explains substantially more variance of LVEDV and LVESV compared to BSA and height. This is understandable, as fat mass is mainly used for energy storage and is not very metabolically active. Lean body mass has been proposed before as a measure for indexation43,44. Indexation
to lean body mass has been applied previously in a population of adults free of cardiac disease, yielding greater volume and mass in women45. Possible reasons for this proposed by the authors
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are a greater cardiac structural response to demand in women, or underestimation of DXA-derived lean body mass in women46. In a study in 90 young athletes, indexation of LV volumes
to lean body mass reduced sex differences to a non-significant level47. Although DXA scans
to derive lean body mass might be feasible in scientific studies, in clinical practice it would be cumbersome strategy. Therefore, an alternative method to estimate lean body mass using more easily obtainable bioelectrical impedance measurements was also investigated. Even though impedance measurements were done at baseline visit, indexation to impedance-derived lean body mass also performed better compared with both BSA and height. The results also indicate that differences in LV volumes between sexes are reduced to non-significant levels when correcting for lean body mass. This might also hold true for differences between ethnicities. An optimized indexation method using lean body mass will therefore reduce differences in cardiac volume, mass and output measurements between sexes and ethnicities, resulting in easier applicable and more generalizable reference values for clinical practice. After lean body mass, the strongest physiological determinant of LVEDV was the R-R interval during CMR assessment. To facilitate implementation of heart rate adjustment, a simple correction equation was constructed that adds value in reducing variability in LV volume indices. Both indexation to lean body mass and application of the R-R interval correction reduced the variability in LV volumes compared to indexation to BSA. This could lead to improved early detection of adverse LV remodeling and recognition of clinical diagnoses such as dilated cardiomyopathy. Another way to improve early detection of LV remodeling is to make measurements more reproducible and accurate. One way to achieve this is using AI-based algorithms. In Chapter 8, a range of novel AI-based methods
is presented and their possible use for improving reproducibility and accuracy in medical imaging and providing tailored therapy are discussed.
FUTURE PERSPECTIVES
To minimize the relentlessly high societal burden of cardiovascular disease in an aging population, the focus in the cardiovascular field should shift towards primary prevention48. One of the possible
ways to achieve this is to select individuals at high risk of developing cardiovascular disease and using early pharmacological or lifestyle interventions to prevent cardiovascular disease from occurring. Early detection of cardiovascular disease is difficult due to the large differences in cardiac structure and function between ethnicities, sexes, age ranges, and due to large intra- and interobserver variability and differences between imaging techniques. Indexation of measures of cardiac structure to lean body mass and R-R interval, as proposed in this thesis, could be a method to reduce these differences and to improve early detection of adverse LV remodeling.
Genetic risk scores are another potential tool that can be used for the detections of individuals at risk and prevention of cardiovascular disease starting from an early stage in life. Because genetic variants are present from conception, they will have a cumulative burden on the cardiovascular system during one’s lifetime. Hypothetically, a hypertensive individual with a large genetic component could have less benefit from lifestyle interventions and require earlier pharmacologic intervention. Risk stratification based on genetic predisposition for hypertension (or myocardial infarction) might eventually lead to clinical trial designs where individuals with a high genetic risk receive early antihypertensive lifestyle or pharmacologic interventions, possibly even tailored to counter the specific mechanism of action. Future studies could also aim at determining whether hypertensive individuals with a large genetic component respond differently to pharmacologic treatment and whether a genetically predicted risk of hypertension also has additional value in predicting and preventing cardiovascular risk. It would also be interesting to investigate whether there is a causal effect of systolic blood pressure on diastolic function. A pitfall is that there appears to be a lack of consensus on the definition of diastolic dysfunction, although very recently a consensus document for the diagnosis of heart failure with preserved ejection fraction has been published49.
Further research is needed to unravel the mechanisms behind the observed link between tobacco smoking and depressed systolic function. It is unclear whether the observed effect of smoking on systolic function is a direct effect due to, for example, microvascular dysfunction, or whether the observed associations are due to the effects of myocardial ischemia or silent infarction. Ideally, subjects included in a follow-up study should also undergo either invasive testing of coronary flow reserve (a measure of microvascular dysfunction) or stress perfusion CMR imaging to visualize reversible and irreversible perfusion defects. An possible follow-up study would be to assess the effect of smoking on diastolic function using TTE assessments, although these were not available in the UK Biobank.
As heart failure with preserved ejection fraction appears to have a multifactorial etiology7, there is a
very realistic possibility that specific combinations of cardiovascular risk factors have a synergistic effect on adverse cardiac remodeling. For example, one study observed that LV myocardial strain was most significantly impaired in subjects with both obesity and hypertension50. Understanding
the interactions between cardiovascular risk factors on adverse LV remodeling could provide more insight into mechanisms leading to heart failure, thus providing distinct categories of patients that benefit most from early interventions.
Although there currently is no evidence for the use of LV restoration devices in patients who underwent myocardial infarction, there is more theoretical benefit of LV restoration at an
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early stage after myocardial infarction. Therefore, early assessment of myocardial viability after myocardial infarction could be a tool for risk stratification and determining the need for an early intervention.
To make medical imaging measurements more accurate and reproducible, AI-based algorithms are a great potential tool to remove interobserver variability from the equation. Many deep learning-based segmentation algorithms for automated derivation of cardiac parameters of structure and function from CMR assessments have been developed51,52. Interobserver variability
between algorithm and human observers is already comparable to human-human interobserver variability53. The more data becomes available for algorithms to train on, the more accurate and
reproducible algorithm-derived measurements will become. When AI-based algorithms become widely used in clinical practice, the improved reproducibility of measurements will lead to more precision in determining cut-off values of LVEF for pharmacologic treatment and implantable cardioverter-defibrillators, which should improve the outcome of heart failure patients. Machine learning algorithms might also have additional value in predicting treatment response, thus facilitating tailored therapy by identifying subgroups of patients that can benefit from specific therapies. Recently, an unsupervised machine learning clustering algorithm of clinical parameters and imaging data has shown that responders on cardiac resynchronisation therapy may be predicted by clustering them into subgroups54. In the future, these algorithms could identify new
parameters of adverse cardiac remodeling that improve a clinician’s ability to predict (and prevent) cardiovascular morbidity and mortality.
Another way to improve early detection of adverse LV remodeling is to make more use of myocardial strain measurements in clinical practice. A recent meta-analysis of patients with various cardiovascular diseases revealed that global longitudinal strain was superior to LVEF in predicting mortality, when using baseline measurements, and also when using changes over time55.
LVEF is only moderately reproducible because of limitations such as its reliance on geometric assumptions to determine LV volumes and dependency on loading conditions and heart rate56.
In clinical practice however, LVEF remains by far the most widely used biomarker for risk stratification. The most important hurdle that prevents myocardial strain measurements from being used in daily clinical practice is that evidence-based treatment algorithms with beta-blockers and ACE inhibitors are based on clinical trials that used inclusion criteria based on cut-off levels of LVEF. Ideally, all heart failure studies should be repeated using cut-off levels of myocardial strain values, but this would clearly be unethical. Because myocardial strain is superior in predicting cardiovascular morbidity and mortality, it is likely also superior in predicting pharmacologic treatment response for heart failure. It would be tempting to speculate that individuals with relatively preserved myocardial strain despite reduced LVEF receive less benefit from treatment.
Using insights from studying cardiovascular remodeling in cardiovascular risk factors and myocardial infarction, this thesis proposes novel ways to detect adverse LV remodeling by optimizing the accuracy and reproducibility of measurements of LV structure and function, as well as reducing interindividual differences. The proposed tools can be deployed to improve early detection of LV remodeling, thus identifying subjects at risk of developing adverse cardiovascular events who can benefit from targeted pharmacologic and lifestyle interventions. This has the potential to reduce cardiovascular morbidity and mortality and enhance healthy aging.
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REFERENCES
1. Rosen BD, Saad MF, Shea S, et al. Hypertension and smoking are associated with reduced regional left ventricular function in asymptomatic: Individuals the multi-ethnic study of atherosclerosis. J Am Coll Cardiol. 2006;47(6):1150-1158.
2. Nadruz W,Jr, Claggett B, Goncalves A, et al. Smoking and cardiac structure and function in the elderly: The ARIC study (atherosclerosis risk in communities). Circ Cardiovasc Imaging. 2016;9(9):e004950. 3. Kamimura D, Cain LR, Mentz RJ, et al. Cigarette smoking and incident heart failure: Insights from the
jackson heart study. Circulation. 2018;137(24):2572-2582.
4. Nham E, Kim SM, Lee SC, et al. Association of cardiovascular disease risk factors with left ventricular mass, biventricular function, and the presence of silent myocardial infarction on cardiac MRI in an asymptomatic population. Int J Cardiovasc Imaging. 2016;32 Suppl 1:173-181.
5. Heckbert SR, Post W, Pearson GD, et al. Traditional cardiovascular risk factors in relation to left ventricular mass, volume, and systolic function by cardiac magnetic resonance imaging: The multiethnic study of atherosclerosis. J Am Coll Cardiol. 2006;48(11):2285-2292.
6. Payne JR, James LE, Eleftheriou KI, et al. The association of left ventricular mass with blood pressure, cigarette smoking and alcohol consumption; data from the LARGE heart study. Int J Cardiol. 2007;120(1):52-58.
7. Ho JE, Enserro D, Brouwers FP, et al. Predicting heart failure with preserved and reduced ejection fraction: The international collaboration on heart failure subtypes. Circ Heart Fail. 2016;9(6):10.1161/ CIRCHEARTFAILURE.115.003116.
8. Gong FF, Jelinek MV, Castro JM, et al. Risk factors for incident heart failure with preserved or reduced ejection fraction, and valvular heart failure, in a community-based cohort. Open Heart. 2018;5(2):e000782-2018-000782. eCollection 2018.
9. Talhout R, Schulz T, Florek E, van Benthem J, Wester P, Opperhuizen A. Hazardous compounds in tobacco smoke. Int J Environ Res Public Health. 2011;8(2):613-628.
10. Morris PB, Ference BA, Jahangir E, et al. Cardiovascular effects of exposure to cigarette smoke and electronic cigarettes: Clinical perspectives from the prevention of cardiovascular disease section leadership council and early career councils of the american college of cardiology. J Am Coll Cardiol. 2015;66(12):1378-1391.
11. Nadruz W,Jr, Goncalves A, Claggett B, et al. Influence of cigarette smoking on cardiac biomarkers: The atherosclerosis risk in communities (ARIC) study. Eur J Heart Fail. 2016;18(6):629-637.
12. Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment. Lancet Diabetes Endocrinol. 2014;2(8):634-647.
13. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R, Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: A meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360(9349):1903-1913.
14. International Consortium for Blood Pressure Genome-Wide Association Studies, Ehret GB, Munroe PB, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478(7367):103-109.
15. Lieb W, Gona P, Larson MG, et al. The natural history of left ventricular geometry in the community: Clinical correlates and prognostic significance of change in LV geometric pattern. JACC Cardiovasc Imaging. 2014;7(9):870-878.
16. Bluemke DA, Kronmal RA, Lima JA, et al. The relationship of left ventricular mass and geometry to incident cardiovascular events: The MESA (multi-ethnic study of atherosclerosis) study. J Am Coll Cardiol. 2008;52(25):2148-2155.
17. Fung MJ, Thomas L, Leung DY. Left ventricular function and contractile reserve in patients with hypertension. Eur Heart J Cardiovasc Imaging. 2018;19(11):1253-1259.
18. Tadic M, Cuspidi C, Celic V, Ivanovic B, Pencic B, Grassi G. The influence of sex on left ventricular strain in hypertensive population. J Hypertens. 2019;37(1):50-56.
19. Cohn JN, Ferrari R, Sharpe N. Cardiac remodeling--concepts and clinical implications: A consensus paper from an international forum on cardiac remodeling. behalf of an international forum on cardiac remodeling. J Am Coll Cardiol. 2000;35(3):569-582.
20. Bolognese L, Neskovic AN, Parodi G, et al. Left ventricular remodeling after primary coronary angioplasty: Patterns of left ventricular dilation and long-term prognostic implications. Circulation. 2002;106(18):2351-2357.
21. Mollema SA, Liem SS, Suffoletto MS, et al. Left ventricular dyssynchrony acutely after myocardial infarction predicts left ventricular remodeling. J Am Coll Cardiol. 2007;50(16):1532-1540.
22. Carrabba N, Parodi G, Valenti R, et al. Clinical implications of early mitral regurgitation in patients with reperfused acute myocardial infarction. J Card Fail. 2008;14(1):48-54.
23. Ravassa S, Kuznetsova T, Varo N, et al. Biomarkers of cardiomyocyte injury and stress identify left atrial and left ventricular remodelling and dysfunction: A population-based study. Int J Cardiol. 2015;185:177-185.
24. Choi EY, Bahrami H, Wu CO, et al. N-terminal pro-B-type natriuretic peptide, left ventricular mass, and incident heart failure: Multi-ethnic study of atherosclerosis. Circ Heart Fail. 2012;5(6):727-734. 25. de Lemos JA, McGuire DK, Khera A, et al. Screening the population for left ventricular hypertrophy
and left ventricular systolic dysfunction using natriuretic peptides: Results from the dallas heart study. Am Heart J. 2009;157(4):746-53.e2.
26. Al-Khatib SM, Stevenson WG, Ackerman MJ, et al. 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: Executive summary: A report of the american college of cardiology/american heart association task force on clinical practice guidelines and the heart rhythm society. Circulation. 2017.
27. Priori SG, Blomstrom-Lundqvist C, Mazzanti A, et al. 2015 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The task force for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death of the european society of cardiology (ESC). endorsed by: Association for european paediatric and congenital cardiology (AEPC). Eur Heart J. 2015;36(41):2793-2867.
28. Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: The task force for the diagnosis and treatment of acute and chronic heart failure of the european society of cardiology (ESC). developed with the special contribution of the heart failure association (HFA) of the ESC. Eur J Heart Fail. 2016;18(8):891-975.
29. Alfakih K, Reid S, Jones T, Sivananthan M. Assessment of ventricular function and mass by cardiac magnetic resonance imaging. Eur Radiol. 2004;14(10):1813-1822.
30. O'Gara PT, Kushner FG, Ascheim DD, et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: Executive summary: A report of the american college of cardiology foundation/american heart association task force on practice guidelines. Circulation. 2013;127(4):529-555.
9
infarction in patients presenting with ST-segment elevation: The task force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the european society of cardiology (ESC). Eur Heart J. 2017.
32. Kusunose K, Kwon DH, Motoki H, Flamm SD, Marwick TH. Comparison of three-dimensional echocardiographic findings to those of magnetic resonance imaging for determination of left ventricular mass in patients with ischemic and non-ischemic cardiomyopathy. Am J Cardiol. 2013;112(4):604-611. 33. Heckbert SR, Post W, Pearson GD, et al. Traditional cardiovascular risk factors in relation to left
ventricular mass, volume, and systolic function by cardiac magnetic resonance imaging: The multiethnic study of atherosclerosis. J Am Coll Cardiol. 2006;48(11):2285-2292.
34. Mistry N, Halvorsen S, Hoffmann P, et al. Assessment of left ventricular function with magnetic resonance imaging vs. echocardiography, contrast echocardiography, and single-photon emission computed tomography in patients with recent ST-elevation myocardial infarction. Eur J Echocardiogr. 2010;11(9):793-800.
35. Perdrix L, Mansencal N, Cocheteux B, et al. How to calculate left ventricular mass in routine practice? an echocardiographic versus cardiac magnetic resonance study. Arch Cardiovasc Dis. 2011;104(5):343-351.
36. Siadecki SD, Frasure SE, Lewiss RE, Saul T. High body mass index is strongly correlated with decreased image quality in focused bedside echocardiography. J Emerg Med. 2016;50(2):295-301.
37. Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the american society of echocardiography and the european association of cardiovascular imaging. J Am Soc Echocardiogr. 2015;28(1):1-39.e14.
38. Schulz-Menger J, Bluemke DA, Bremerich J, 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;15:35-429X-15-35.
39. Kramer DG, Trikalinos TA, Kent DM, Antonopoulos GV, Konstam MA, Udelson JE. Quantitative evaluation of drug or device effects on ventricular remodeling as predictors of therapeutic effects on mortality in patients with heart failure and reduced ejection fraction: A meta-analytic approach. J Am Coll Cardiol. 2010;56(5):392-406.
40. Maceira AM, Prasad SK, Khan M, Pennell DJ. Normalized left ventricular systolic and diastolic function by steady state free precession cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2006;8(3):417-426.
41. Natori S, Lai S, Finn JP, et al. Cardiovascular function in multi-ethnic study of atherosclerosis: Normal values by age, sex, and ethnicity. AJR Am J Roentgenol. 2006;186(6 Suppl 2):S357-65.
42. Gibson S, Numa A. The importance of metabolic rate and the folly of body surface area calculations. Anaesthesia. 2003;58(1):50-55.
43. Kuch B, Hense HW, Gneiting B, et al. Body composition and prevalence of left ventricular hypertrophy. Circulation. 2000;102(4):405-410.
44. Hense HW, Gneiting B, Muscholl M, et al. The associations of body size and body composition with left ventricular mass: Impacts for indexation in adults. J Am Coll Cardiol. 1998;32(2):451-457. 45. Yeon SB, Salton CJ, Gona P, et al. Impact of age, sex, and indexation method on MR left
ventricular reference values in the framingham heart study offspring cohort. J Magn Reson Imaging. 2015;41(4):1038-1045.
46. Roche AF, Guo S, Wellens R, Chumlea WC, Wu X, Siervogel RM. Fat-free mass from dual-energy X-ray absorptiometry and from other procedures. Asia Pac J Clin Nutr. 1995;4(1):183-185.
47. Giraldeau G, Kobayashi Y, Finocchiaro G, et al. Gender differences in ventricular remodeling and function in college athletes, insights from lean body mass scaling and deformation imaging. Am J Cardiol. 2015;116(10):1610-1616.
48. Leong DP, Joseph PG, McKee M, et al. Reducing the global burden of cardiovascular disease, part 2: Prevention and treatment of cardiovascular disease. Circ Res. 2017;121(6):695-710.
49. Pieske B, Tschope C, de Boer RA, et al. How to diagnose heart failure with preserved ejection fraction: The HFA-PEFF diagnostic algorithm: A consensus recommendation from the heart failure association (HFA) of the european society of cardiology (ESC). Eur Heart J. 2019;40(40):3297-3317.
50. Tadic M, Cuspidi C, Pencic B, et al. The interaction between blood pressure variability, obesity, and left ventricular mechanics: Findings from the hypertensive population. J Hypertens. 2016;34(4):772-780. 51. Molaei S, Shiri M, Horan K, Kahrobaei D, Nallamothu B, Najarian K. Deep convolutional neural
networks for left ventricle segmentation. Conf Proc IEEE Eng Med Biol Soc. 2017;2017:668-671. 52. Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation
of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal. 2017;35:159-171.
53. Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018;20(1):65-018-0471-x.
54. Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2019;21(1):74-85. 55. Kalam K, Otahal P, Marwick TH. Prognostic implications of global LV dysfunction: A systematic
review and meta-analysis of global longitudinal strain and ejection fraction. Heart. 2014;100(21):1673-1680.
56. Cikes M, Solomon SD. Beyond ejection fraction: An integrative approach for assessment of cardiac structure and function in heart failure. Eur Heart J. 2016;37(21):1642-1650.
