Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment.

This study aims at identifying risk-related patterns of left ventricular contraction dynamics via novel volume transient characterization. A multicenter cohort of AMI survivors (n = 1021) who underwent Cardiac Magnetic Resonance (CMR) after infarction was considered for the study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE, n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. Cardiac function was characterized from CMR in 3 potential directions: by (1) volume temporal transients (i.e. contraction dynamics); (2) feature tracking strain analysis (i.e. bulk tissue peak contraction); and (3) 3D shape analysis (i.e. 3D contraction morphology). A fully automated pipeline was developed to extract conventional and novel artificial-intelligence-derived metrics of cardiac contraction, and their relationship with MACE was investigated. Any of the 3 proposed directions demonstrated its additional prognostic value on top of established CMR indexes, myocardial injury markers, basic characteristics, and cardiovascular risk factors (P < 0.001). The combination of these 3 directions of enhancement towards a final CMR risk model improved MACE prediction by 13% compared to clinical baseline (0.774 (0.771-0.777) vs. 0.683 (0.681-0.685) cross-validated AUC, P < 0.001). The study evidences the contribution of the novel contraction characterization, enabled by a fully automated pipeline, to post-infarction assessment.

Assessment of the cardiac output at rest and during exercise stress using real-time cardiovascular magnetic resonance imaging in HFpEF-patients.

This methodological study aimed to validate the cardiac output (CO) measured by exercise-stress real-time phase-contrast cardiovascular magnetic resonance imaging (CMR) in patients with heart failure and preserved ejection fraction (HFpEF). 68 patients with dyspnea on exertion (NYHA ≥ II) and echocardiographic signs of diastolic dysfunction underwent rest and exercise stress right heart catheterization (RHC) and CMR within 24 h. Patients were diagnosed as overt HFpEF (pulmonary capillary wedge pressure (PCWP) ≥ 15mmHg at rest), masked HFpEF (PCWP ≥ 25mmHg during exercise stress but < 15mmHg at rest) and non-cardiac dyspnea. CO was calculated using RHC as the reference standard, and in CMR by the volumetric stroke volume, conventional phase-contrast and rest and stress real-time phase-contrast imaging. At rest, the CMR based CO showed good agreement with RHC with an ICC of 0.772 for conventional phase-contrast, and 0.872 for real-time phase-contrast measurements. During exercise stress, the agreement of real-time CMR and RHC was good with an ICC of 0.805. Real-time measurements underestimated the CO at rest (Bias:0.71 L/min) and during exercise stress (Bias:1.4 L/min). Patients with overt HFpEF had a significantly lower cardiac index compared to patients with masked HFpEF and with non-cardiac dyspnea during exercise stress, but not at rest. Real-time phase-contrast CO can be assessed with good agreement with the invasive reference standard at rest and during exercise stress. While moderate underestimation of the CO needs to be considered with non-invasive testing, the CO using real-time CMR provides useful clinical information and could help to avoid unnecessary invasive procedures in HFpEF patients.

Assessment of oropharyngeal respiratory volume and sleep apnoea scores using peripheral arterial tonometry may improve diagnosis and treatment planning of combined dysgnathia therapy approaches.

BACKGROUND: Increased daytime sleepiness is a frequently reported symptom in patients with pronounced dysgnathia. OBJECTIVES: This study investigated possible correlations using home peripheral arterial tonometry (PAT) and oropharyngeal airway volume determination in patients with dysgnathia and daytime sleepiness. METHODS: Twenty patients (13 male, median age 27.6 ± 6.8 years) with abnormal sleep history and 10 skeletal neutral configured controls (6 male, median age 29.5 ± 4.2 years) with normal sleep history were examined. Patients and controls were evaluated for apnoea-hypopnoea index (AHI), respiratory disturbance index (RDI), oxygen desaturation index (ODI), snoring volume (dB), total sleep time (TST) and REM-percentage (REM). Airway volumetry was measured via CBCT. Individual user experience for PAT was assessed using the User Experience Questionnaire (UEQ). RESULTS: Patients had significantly higher respiratory scores than controls. AHI increased 4.6-fold (p = .006), RDI 2.5-fold (p = .008) and ODI 6.4-fold (p < .001). Oropharyngeal volumes showed a 30% decrease (p = .003). dB, TST and REM showed no significant differences. AHI (r = -.51; p = .005), ODI (r = -.60; p < .001) and RDI (r = -.45; p = .016) correlated negatively with pharyngeal volume. Wits appraisal correlated negatively with oropharyngeal volume (r = -.47; p = .010) and positively with AHI (r = .41; p = .03) and ODI (r = .49; p = .007). dB and TST (r = -.49; p = .008) and REM and RDI (r = -.43; p = .02) correlated negatively. UEQ-KPI (2.17 ± 0.24) confirmed excellent usability of PAT. CONCLUSION: Patients with mandibular retrognathia and abnormal sleep history showed significantly higher respiratory indices and smaller oropharyngeal volumes than neutrally configured controls. The dygnathia severity directly influenced the risk of obstructive sleep apnoea.

Hemodynamic force assessment by cardiovascular magnetic resonance in HFpEF: A case-control substudy from the HFpEF stress trial.

BACKGROUND: The diagnosis of heart failure with preserved ejection fraction (HFpEF) remains challenging. Exercise-stress testing is recommended in case of uncertainty; however, this approach is time-consuming and costly. Since preserved EF does not represent normal systolic function, we hypothesized comprehensive cardiovascular magnetic resonance (CMR) assessment of cardiac hemodynamic forces (HDF) may identify functional abnormalities in HFpEF. METHODS: The HFpEF Stress Trial (DZHK-17; Clinicaltrials.gov: NCT03260621) prospectively recruited 75 patients with exertional dyspnea, preserved EF (≥50%) and signs of diastolic dysfunction (E/e' ≥8) on echocardiography. Patients underwent rest and exercise-stress right heart catheterisation, echocardiography and CMR. The final study cohort consisted of 68 patients (HFpEF n = 34 and non-cardiac dyspnea n = 34 according to pulmonary capillary wedge pressure (PCWP)). HDF assessment included left ventricular (LV) longitudinal, systolic peak and impulse, systolic/diastolic transition, E-wave deceleration as well as A-wave acceleration forces. Follow-up after 24 months evaluated cardiovascular mortality and hospitalisation (CVH) - only two patients were lost to follow-up. FINDINGS: HDF assessment revealed impairment of LV longitudinal function in patients with HFpEF compared to non-cardiac dyspnoea (15.8% vs. 18.3%, p = 0.035), attributable to impairment of systolic peak (38.6% vs 51.6%, p = 0.003) and impulse (20.8% vs. 24.5%, p = 0.009) forces as well as late diastolic filling (-3.8% vs -5.4%, p = 0.029). Early diastolic filling was impaired in HFpEF patients identified at rest compared with patients identified during stress only (7.7% vs. 9.9%, p = 0.004). Impaired systolic peak was associated with CVH (HR 0.95, p = 0.016), and was superior to LV global longitudinal strain assessment in prediction of CVH (AUC 0.76 vs. 0.61, p = 0.048). INTERPRETATION: Assessment of HDF indicates impairment of LV systolic ejection force in HFpEF which is associated with cardiovascular events. FUNDING: German Centre for Cardiovascular Research (DZHK).

Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis.

BACKGROUND: Left ventricular ejection fraction (LVEF) and end-systolic volume (ESV) remain the main imaging biomarkers for post-acute myocardial infarction (AMI) risk stratification. However, they are limited to global systolic function and fail to capture functional and anatomical regional abnormalities, hindering their performance in risk stratification. OBJECTIVES: This study aimed to identify novel 3-dimensional (3D) imaging end-systolic (ES) shape and contraction descriptors toward risk-related features and superior prognosis in AMI. METHODS: A multicenter cohort of AMI survivors (n = 1,021; median age 63 years; 74.5% male) who underwent cardiac magnetic resonance (CMR) at a median of 3 days after infarction were considered for this study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE; n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. A fully automated pipeline was developed to segment CMR images, build 3D statistical models of shape and contraction in AMI, and find the 3D patterns related to MACE occurrence. RESULTS: The novel ES shape markers proved to be superior to ESV (median cross-validated area under the receiver-operating characteristic curve 0.681 [IQR: 0.679-0.684] vs 0.600 [IQR: 0.598-0.602]; P < 0.001); and 3D contraction to LVEF (0.716 [IQR: 0.714-0.718] vs 0.681 [IQR: 0.679-0.684]; P < 0.001) in MACE occurrence prediction. They also contributed to a significant improvement in a multivariable setting including CMR markers, cardiovascular risk factors, and basic patient characteristics (0.747 [IQR: 0.745-0.749]; P < 0.001). Based on these novel 3D descriptors, 3 impairments caused by AMI were identified: global, anterior, and basal, the latter being the most complementary signature to already known predictors. CONCLUSIONS: The quantification of 3D differences in ES shape and contraction, enabled by a fully automated pipeline, improves post-AMI risk prediction and identifies shape and contraction patterns related to MACE occurrence.

Impact of fully automated assessment on interstudy reproducibility of biventricular volumes and function in cardiac magnetic resonance imaging.

Cardiovascular magnetic resonance (CMR) imaging provides reliable assessments of biventricular morphology and function. Since manual post-processing is time-consuming and prone to observer variability, efforts have been directed towards novel artificial intelligence-based fully automated analyses. Hence, we sought to investigate the impact of artificial intelligence-based fully automated assessments on the inter-study variability of biventricular volumes and function. Eighteen participants (11 with normal, 3 with heart failure and preserved and 4 with reduced ejection fraction (EF)) underwent serial CMR imaging at in median 63 days (range 49-87) interval. Short axis cine stacks were acquired for the evaluation of left ventricular (LV) mass, LV and right ventricular (RV) end-diastolic, end-systolic and stroke volumes as well as EF. Assessments were performed manually (QMass, Medis Medical Imaging Systems, Leiden, Netherlands) by an experienced (3 years) and inexperienced reader (no active reporting, 45 min of training with five cases from the SCMR consensus data) as well as fully automated (suiteHEART, Neosoft, Pewaukee, WI, USA) without any manual corrections. Inter-study reproducibility was overall excellent with respect to LV volumetric indices, best for the experienced observer (intraclass correlation coefficient (ICC) > 0.98, coefficient of variation (CoV, < 9.6%) closely followed by automated analyses (ICC > 0.93, CoV < 12.4%) and lowest for the inexperienced observer (ICC > 0.86, CoV < 18.8%). Inter-study reproducibility of RV volumes was excellent for the experienced observer (ICC > 0.88, CoV < 10.7%) but considerably lower for automated and inexperienced manual analyses (ICC > 0.69 and > 0.46, CoV < 22.8% and < 28.7% respectively). In this cohort, fully automated analyses allowed reliable serial investigations of LV volumes with comparable inter-study reproducibility to manual analyses performed by an experienced CMR observer. In contrast, RV automated quantification with current algorithms still relied on manual post-processing for reliability.

Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction.

Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence-based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence-based, commercially available software analyses. Methods and Results Cardiovascular magnetic resonance data (n=1017 patients) from 2 myocardial infarction multicenter trials were included. Analyses of biventricular parameters including ejection fraction (EF) were manually and automatically assessed using conventional and artificial intelligence-based software. Obtained parameters entered regression analyses for prediction of major adverse cardiac events, defined as death, reinfarction, or congestive heart failure, within 1 year after the acute event. Both manual and uncorrected automated volumetric assessments showed similar impact on outcome in univariate analyses (left ventricular EF, manual: hazard ratio [HR], 0.93 [95% CI 0.91-0.95]; P<0.001; automated: HR, 0.94 [95% CI, 0.92-0.96]; P<0.001) and multivariable analyses (left ventricular EF, manual: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001; automated: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001). Manual correction of the automated contours did not lead to improved risk prediction (left ventricular EF, area under the curve: 0.67 automated versus 0.68 automated corrected; P=0.49). There was acceptable agreement (left ventricular EF: bias, 2.6%; 95% limits of agreement, -9.1% to 14.2%; intraclass correlation coefficient, 0.88 [95% CI, 0.77-0.93]) of manual and automated volumetric assessments. Conclusions User-independent volumetric analyses performed by fully automated software are feasible, and results are equally predictive of major adverse cardiac events compared with conventional analyses in patients following myocardial infarction. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT00712101 and NCT01612312.

Cardiac Magnetic Resonance Myocardial Feature Tracking for Optimized Risk Assessment After Acute Myocardial Infarction in Patients With Type 2 Diabetes.

Type 2 diabetes predicts outcome following acute myocardial infarction (AMI). Since underlying mechanics are incompletely understood, we investigated left ventricular (LV) and left atrial (LA) pathophysiological changes and their prognostic implications using cardiovascular magnetic resonance (CMR). Consecutive patients (N = 1,147; n = 265 with diabetes, n = 882 without diabetes) underwent CMR 3 days after AMI. Analyses included LV ejection fraction (LVEF); global longitudinal strain (GLS) and circumferential and radial strains; LA reservoir, conduit, and booster pump strains; and infarct size, edema, and microvascular obstruction. Predefined end points were major adverse cardiovascular events (MACE) within 12 months. Patients with diabetes had impaired LA reservoir (19.8% vs. 21.2%, P < 0.01) and conduit (7.6% vs. 9.0%, P < 0.01) strains but not ventricular function or myocardial damage. They were at higher risk of MACE than patients without diabetes (10.2% vs. 5.8%, P < 0.01), with most MACE occurring in patients with LVEF ≥35%. While LVEF (P = 0.045) and atrial reservoir strain (P = 0.024) were independent predictors of MACE in patients without diabetes, GLS was in patients with diabetes (P = 0.010). Considering patients with diabetes and LVEF ≥35% (n = 237), GLS and LA reservoir strain below median were significantly associated with MACE. In conclusion, in patients with diabetes, LA and LV longitudinal strain permit optimized risk assessment early after reperfused AMI with incremental prognostic value over and above that of LVEF.

Cardiac Magnetic Resonance Left Ventricular Mechanical Uniformity Alterations for Risk Assessment After Acute Myocardial Infarction.

Background Despite limitations as a stand-alone parameter, left ventricular (LV) ejection fraction is the preferred measure of myocardial function and marker for postinfarction risk stratification. LV myocardial uniformity alterations may provide superior prognostic information after acute myocardial infarction, which was the subject of this study. Methods and Results Consecutive patients with acute myocardial infarction (n=1082; median age: 63 years; 75% male) undergoing cardiac magnetic resonance at a median of 3 days after infarction were included in this multicenter observational study. Circumferential and radial uniformity ratio estimates were derived from cardiac magnetic resonance feature tracking as markers of mechanical uniformity alterations (values between 0 and 1 with 1 reflecting perfect uniformity). The clinical end point was the 12-month rate of major adverse cardiac events, consisting of all-cause death, reinfarction, and new congestive heart failure. Patients with major adverse cardiac events (n=73) had significantly impaired circumferential uniformity ratio estimates (0.76 [interquartile range: 0.67-0.86] versus 0.84 [interquartile range: 0.76-0.89]; P<0.001) and radial uniformity ratio estimates (0.69 [interquartile range: 0.60-0.79] versus 0.76 [interquartile range: 0.67-0.83]; P<0.001) compared with patients without events. Although uniformity estimates did not provide independent prognostic information in the overall cohort, a circumferential uniformity ratio estimate below the median of 0.84 emerged as an independent predictor of outcome in postinfarction patients with LV ejection fraction >35% (n=959), even after adjustment for established risk factors (hazard ratio: 1.99; 95% CI, 1.06-3.74; P=0.033 in multivariable Cox regression analysis). In contrast, LV ejection fraction was not associated with adverse events in this subgroup of patients with acute myocardial infarction. Conclusions Cardiac magnetic resonance-derived estimates of mechanical uniformity alterations are novel markers for risk assessment after acute myocardial infarction, and the circumferential uniformity ratio estimate provides independent prognostic information for patients with preserved or only moderately reduced LV ejection fraction.

Fast manual long-axis strain assessment provides optimized cardiovascular event prediction following myocardial infarction.

AIMS: Cardiovascular magnetic resonance feature tracking (CMR-FT) global longitudinal strain (GLS) provides incremental prognostic value following acute myocardial infarction (AMI) but requires substantial post-processing. Alternatively, manual global long-axis strain (LAS) can be easily assessed from standard steady state free precession images. We aimed to define the prognostic value of LAS in a large multicentre study in patients following AMI. METHODS AND RESULTS: A total of 1235 patients with myocardial infarction [n = 795 with ST-elevation myocardial infarction (STEMI) and 440 with non-ST-elevation myocardial infarction (NSTEMI)] underwent cardiovascular magnetic resonance imaging after primary percutaneous coronary intervention in eight centres across Germany. Assessment of LAS was performed in a blinded core-laboratory measuring the systolic shortening between the epicardial apical border and the middle of a line connecting the origins of the mitral leaflets. Primary clinical endpoint was the occurrence of major adverse clinical events (MACE) including death, reinfarction, and congestive heart failure within 1 year after AMI. During 1-year follow-up, 76 patients suffered from MACE. Impaired LAS was associated with higher MACE occurrence both in STEMI (P < 0.001) and NSTEMI (P = 0.001) patients. Association of LAS remained significant (P = 0.017) after correction for univariate significant parameters for MACE prediction. C-statistics revealed incremental value of additional LAS assessment for optimized event prediction compared with left ventricular ejection fraction (MACE P = 0.044; mortality P = 0.013) and a combination of established clinical and imaging parameters (MACE P = 0.084; mortality P = 0.027), but not CMR-FT GLS (MACE P = 0.075; mortality P = 0.380). CONCLUSION: LAS provides software independent, widely available, easy and fast approximation of longitudinal left ventricular shortening early after reperfused AMI with incremental prognostic value beyond established risk stratification parameters. CLINICAL TRIALS.GOV: NCT00712101 and NCT01612312.