BACKGROUND:End-stage hypertrophic cardiomyopathy (HCM) is a distinct and advanced form of HCM, defined by a left ventricular ejection fraction <50% and associated with a markedly poor prognosis. Evidence on the prognostic relevance of late gadolinium enhancement (LGE) and its key features in end-stage HCM remains limited. The aim of our study was to evaluate the prognostic value of the LGE granularity model, including its location, extent, and pattern in patients with end-stage HCM. METHODS:All patients referred for cardiovascular magnetic resonance assessment of HCM at 3 French tertiary university hospitals between 2008 and 2024 were retrospectively screened, and all patients with a left ventricular ejection fraction <50% were included. The LGE granularity model was defined as a model combining LGE extent (unique versus multiple involvement), location (septal versus other), and pattern (subepicardial versus midwall). The primary end point was all-cause mortality. RESULTS:Among 2873 patients with HCM, 691 (24%) with end-stage HCM were included (52±7 years, 54% male). After a median follow-up of 9 years (interquartile range, 6-11), 226 patients died (33%). LGE was observed in 259 (37%) patients and was associated with mortality, even after adjustment for classical prognostic factors (hazard ratio, 1.52 [95% CI, 1.07-2.18]; P=0.02). Each LGE granularity component was independently associated with mortality after adjustment: LGE extent (hazard ratio, 2.85 [95% CI, 1.03-7.85]; P=0.02), septal location (hazard ratio, 1.73 [95% CI, 1.10-2.73]; P<0.001), and midwall pattern (hazard ratio, 4.15 [95% CI, 1.79-9.61]; P<0.001). CONCLUSIONS:In this large multicenter cohort of patients with end-stage HCM, the LGE granularity model integrating LGE extent, location, and pattern provided strong and independent prognostic value.

Background: Recreational drug use is increasingly associated with adverse outcomes in acute coronary syndrome (ACS) patients, but differences in long-term outcomes between ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction are not well defined. Objective: The authors evaluated the association between recreational drug use and major adverse cardiovascular events (MACE) 1 year after intensive cardiac care unit (ICCU) admission in ACS patients. Methods: The Addiction in Intensive Cardiac Care Units study systematically screened all patients admitted to ICCUs across 39 French centers (April 7-22, 2021) via prospective urinary testing. The primary outcome was MACE, defined as cardiovascular death, nonfatal myocardial infarction, or stroke. One-year follow-up was collected through clinical visits or direct contact between patients and cardiologists, concluding in June 2022. Outcomes were adjudicated by an independent cardiology committee. The prognostic impact of recreational drug use on MACE was assessed using multivariable Cox proportional hazards models, validated by propensity matching. Results: Of 712 ACS patients, 13.5% had recreational drug detection. At 1 year, MACE occurred in 7.0%, with higher rates among drug-positive vs drug-negative patients (12.5% vs 6.2%). Recreational drug use was associated with increased MACE (HR: 2.70; 95% CI: 1.30-5.57; P = 0.013). This association was significant in STEMI (HR: 4.11; 95% CI: 1.60-10.5; P = 0.005) but not in non-ST-elevation myocardial infarction patients. Propensity matching confirmed this in STEMI patients (HR: 3.39; 95% CI: 1.19-9.62; P = 0.022). Conclusions: Recreational drug use was associated with increased 1-year MACE risk in ACS patients, particularly STEMI, supporting routine drug screening.

Late gadolinium enhancement (LGE) assessed by cardiovascular magnetic resonance is the cornerstone in the assessment of myocardial tissue characterization, providing crucial diagnostic and prognostic information across a wide spectrum of cardiac conditions. While LGE is traditionally evaluated for its presence and extent, a comprehensive assessment of its diverse characteristics, called "LGE granularity"-including its location, extent, and pattern-offers deeper insights into myocardial pathophysiology. The clinical significance of LGE is influenced by various factors, ranging from acquisition protocols including choice of contrast-media and post-processing techniques to interpretation by expert readers and, more recently, artificial intelligence (AI)-based analysis. Advances in imaging protocols have refined LGE detection and quantification, improving diagnostic accuracy and reproducibility. Furthermore, AI approaches are revolutionizing LGE assessment by enabling automated segmentation, feature extraction, and risk stratification. Despite the widespread clinical use of LGE, challenges remain in standardizing acquisition parameters and harmonizing interpretation criteria across centers. Additionally, the integration of AI into clinical workflows raises important considerations regarding validation, generalizability, and physician acceptance. However, emerging evidence suggests that AI-based LGE analysis may improve prognostic modeling, facilitate earlier disease detection, and enhance personalized therapeutic decision-making. This review provides a state-of-the-art of LGE's technical, interpretative, and prognostic aspects, highlighting the role of AI in myocardial tissue characterization. By bridging traditional expert analysis with cutting-edge computational techniques, the future of LGE assessment aims to refine cardiac risk stratification and guide precision medicine in cardiology.

AIMS:The aim of this study was to evaluate the prognostic value of stress perfusion cardiovascular magnetic resonance (CMR) in diabetic vs. non-diabetic patients and then in symptomatic vs. asymptomatic diabetics. Diabetic individuals are at increased risk of coronary atherosclerosis. A significant percentage of diabetics fail to perceive the typical symptoms of myocardial ischaemia. Screening methods such as coronary computed tomography angiography (CCTA) or single-photon emission computed tomography (SPECT) have not shown clear benefits in asymptomatic diabetics. The role of stress CMR in this population is not well established. METHODS AND RESULTS:Between 2008 and 2018, all consecutive diabetic and non-diabetic patients without known cardiovascular disease referred for stress perfusion CMR in two tertiary centres were included. Propensity score matching was used to create a cohort of diabetic vs. non-diabetic patients with similar baseline characteristics. All patients were followed for the occurrence of major adverse cardiovascular events (MACE), defined as cardiovascular death or non-fatal myocardial infarction. Diabetic patients were categorized into symptomatic and asymptomatic patients. Out of 3485 eligible patients, 1359 diabetics and 1359 non-diabetics (mean age 69 ± 12 years, 57.4% women) with similar propensity scores were included. Over a median follow-up period of 6.5 (5.9-8.9) years, 386 (14.2%) experienced MACEs. Kaplan-Meier analysis for the occurrence of MACE indicated that the extent of ischaemia or late gadolinium enhancement involving ≥3 segments were independent predictors of the occurrence of MACEs {hazard ratio [HR]: 7.14 [95% confidence interval (CI), 5.01-10.02] and HR: 5.03 [95% CI, 3.47-7.29]; both P < 0.001, respectively}, with no significant differences between diabetics and non-diabetics. Asymptomatic diabetics (n = 255) showed similar event rates as symptomatic patients (P = 0.98). CONCLUSION:Stress CMR provides valuable prognostic information in diabetic patients, irrespective of symptoms. Further assessment is needed to determine whether stress CMR should be a standard screening tool for diabetic patients.

AIMS:Benefits of screening coronary artery disease (CAD) using stress perfusion cardiovascular magnetic resonance (CMR) in patients with hypertension without known CAD is not well established. The aim of our study was to assess the long-term prognostic value of vasodilator stress CMR in patients with hypertension without known CAD. METHODS AND RESULTS:Between December 2008 and January 2022, all consecutive patients with hypertension without known CAD referred for stress CMR were followed up to the occurrence of major cardiovascular events (MACE), defined as cardiovascular mortality or non-fatal myocardial infarction (MI). Cox regressions were performed to determine the prognostic value of each parameter. Among 2019 patients (69 ± 12 years; 45% male) with a median follow up of 6.7 (5.9-8.9) years, 327 had MACE (16%). Patients without ischaemia experienced a lower rate of MACE than those with ischaemia (12% vs. 39%, respectively, P < 0.001). Ischaemia and unrecognized MI were both significantly associated with the occurrence of MACE (respectively, HR: 4.1, 99.5% CI: 3.0-5.7 and HR: 3.6, 99.5% CI: 2.6-5.1, both P < 0.001). After adjustment, both the extent of ischaemia and unrecognized MI were independent predictors of MACE (respectively, HR: 1.2, 99.5% CI: 1.2-1.3, and HR: 1.2, 99.5% CI: 1.1-1.3, both P < 0.001). Adding stress CMR parameters improved model discrimination and reclassification, with greatest improvements in stepwise Model (C-statistic improvement: 0.02; net reclassification improvement: 0.50; integrative discrimination index: 0.02; all P < 0.001). CONCLUSION:In patients with hypertension without known CAD, stress CMR is a long-term predictor for the incidence of MACE and offer an incremental prognostic value over traditional predictors.

BACKGROUND:Although recent work has shown that recent recreational drug use is associated with in-hospital outcomes in patients admitted to the intensive cardiac care unit (ICCU), its cardiovascular consequences after hospitalization for an acute cardiovascular event are not well established. We aimed to evaluate the prognostic impact of recreational drug use at 1-year follow-up on major adverse cardiovascular and cerebrovascular events (MACCE) in patients admitted to the ICCU. METHODS:The ADDICT-ICCU study (Addiction in Intensive Cardiac Care Units) is a prospective multicentric study including all consecutive patients admitted to the ICCU over 2 weeks in April 2021 at 39 French centers. Patients were excluded in cases of scheduled hospitalization, hospitalization within 24 hours before ICCU admission, or in-hospital death. Screening for recreational drug use was performed by a systematic urinary testing upon admission. The primary composite outcome was 1-year MACCE defined as cardiovascular death, nonfatal myocardial infarction, or stroke. Outcomes were adjudicated by 2 senior cardiologists after patient contact and review of anonymized records. A multivariable Cox regression analysis adjusted for traditional prognostic factors was performed to assess the independent association between overall recreational drug use and clinical outcomes. RESULTS:Of the 1392 patients assessed (63±15 years, 69.9% men), 157 (11.3%) had an initial positive test (cannabis or opioids, cocaine, amphetamines, or 3,4-methylenedioxymethamphetamine). After 1-year of follow-up, 94 (6.7%) patients experienced MACCE, with a higher incidence observed among drug users compared with nonusers (12.7% versus 6.0%; risk difference, 6.7% [95% CI, 1.5%-12.2%]). Cannabis or opioid use alone was also associated with MACCE (hazard ratio, 1.77 [95% CI, 1.02-3.08] for cannabis, and hazard ratio, 3.60 [95% CI, 1.57-8.23], for opioids). After adjustment for traditional prognostic factors, recreational drug use remained independently associated with MACCE (hazard ratio, 2.91 [95% CI, 1.68-5.05]). CONCLUSIONS:Recreational drug use markedly increases the risk of 1-year adverse cardiovascular outcomes in ICCU patients highlighting the need for targeted, tailored interventions. REGISTRATION:URL: https://www.clinicaltrials.gov; Unique identifier: NCT05063097.

AIMS:To characterize the cardiovascular magnetic resonance (CMR) phenotype of hypertensive hypertrophic cardiomyopathy (HCM) patients, assess the association between hypertension and all-cause mortality, and compare the long-term prognostic performance of CMR-derived biomarkers between hypertensive versus non-hypertensive HCM patients. METHODS:Between 2008 and 2024, all consecutive HCM patients referred for CMR from the HCM-LGE registry were included. The primary outcome was all-cause mortality. Clinical variables, cardiac volumes, myocardial mass, functions and tissue characterization with late gadolinium enhancement (LGE) were assessed in all patients. The prognostic value of hypertension and CMR-derived biomarkers were assessed using survival analysis. RESULTS:Among 2,873 patients with HCM (52±8 years, 56% males), 796 (28%) had hypertension. Hypertensive HCM patients had greater LGE extent (1.1±1.8 vs 0.9±1.5, p<0.001), more LGE multiple locations (17% vs 11%, p<0.001), more midwall LGE (35% vs 28%, p=0.001). After propensity score matching 1:1, survival curves showed an increased risk of all-cause mortality for hypertensive HCM patients at median 9 year-follow up (HR: 1.32 [95% CI: 1.04-1.69]; log-rank p=0.024). High LGE extent >3 segments and septal location of LGE were independently associated with mortality in hypertensive HCM patients (HR: 3.99 [95%CI: 2.21-7.18]; p=<0.001 and HR: 1.76 [95%CI: 1.03-3.01]; p=0.039, respectively) and non-hypertensive HCM patients (HR: 4.63 [95%CI: 2.15-9.97]; p<0.001 and HR: 1.48 [95%CI: 1.02-2.15]; p=0.040, respectively). CONCLUSION:In a large multicenter cohort of HCM patients, concomitant hypertension was associated with increased long-term mortality and prognostic associations of CMR-LGE biomarkers were found. These findings highlight the importance of comprehensive cardiovascular risk assessment in HCM patients.

Muscular dystrophies encompass a heterogeneous spectrum of inherited myopathies characterized by progressive skeletal muscle degeneration frequently accompanied by life-threatening cardiac involvement. Cardiovascular magnetic resonance (CMR) has become the reference non-invasive imaging modality for the detection, characterization, and longitudinal monitoring of cardiomyopathy involvement across this group of disorders. This state-of-the-art review summarized contemporary evidence on the diagnostic and prognostic value of CMR in the most prevalent muscular dystrophies, including Myotonic dystrophy, Duchenne and Becker muscular dystrophies, Emery-Dreifuss muscular dystrophy, laminopathies, facioscapulohumeral muscular dystrophy, and mitochondrial myopathies. CMR uniquely enables high-resolution assessment of ventricular volumes and function, tissue characterization through late gadolinium enhancement (LGE) and parametric mapping (native T1, T2, extracellular volume fraction), and quantitative strain imaging. These techniques uncover subclinical myocardial involvement years before overt dysfunction occurs, providing a robust substrate for early therapeutic intervention. Disease-specific CMR signatures, such as inferolateral subepicardial fibrosis in dystrophinopathies or mid-wall septal enhancement in laminopathies, allow for refined etiological diagnosis and targeted risk stratification. LGE burden and distribution are independently associated with ventricular arrhythmias and adverse cardiac events, transcending the limitations of traditional criteria based on left ventricular ejection fraction for implantable cardioverter-defibrillator selection. Emerging evidence further supports the integration of CMR biomarkers into genotype-guided management strategies and prospective therapeutic trials.

Abstract Background Late gadolinium enhancement (LGE) extent is the basis for risk stratification of hypertrophic cardiomyopathy (HCM) using cardiac magnetic resonance (CMR). LGE extent has been recently added in the guidelines by the European Society of Cardiology (ESC) and the American College of Cardiology (ACC), setting a threshold at ≥15% of left ventricular mass. However, SCD has become an uncommon event in this population, and mortality is now mostly related to other phenotypes, such as stroke and heart failure. While previous studies have showed the prognostic value of LGE to predict all-cause mortality, the prognostic impact of additional LGE features is not well established. Purpose We aimed to assess the prognostic value of the LGE granularity including extent, location, and pattern in HCM to predict all-cause death. Methods Between 2008 and 2021, all patients referred for HCM assessment using CMR, without history of coronary artery disease (CAD) or clinical history of myocarditis were prospectively recruited in two French centers. The outcome was all-cause death using the French National Registry of Death. The concept of LGE granularity was defined as a model combining LGE extent (by segment), location (septal or others), and pattern (midwall and/or subepicardial). Using nested Cox proportional hazard models, the additional predictive value of LGE granularity was assessed by the C-statistic increment, the continuous net reclassification improvement (NRI), the integrative discrimination index (IDI) and the global Chi-2. Results Among 2,672 patients (52±7 years, 56% males), 862 (32%) had LGE. After a median (IQR) follow-up of 9 (7–11) years, 447 (17%) patients died. Survival curves show an increased risk for LGE presence (log-rank p<0.001, Figure 1A). After adjustment for traditional prognosticators in the overall population (N=2,672), LGE was associated with all-cause death (adjusted hazard ratio (HR) 3.96, 95% CI: 3.26-4.80, p<0.001). Among the LGE subgroup (n=862), survival curves showed that the LGE granularity was associated with a higher risk of all-cause death (all p<0.001, Figure 1B). A nested Cox model adjusted on traditional prognosticators showed that the LGE extent, location and pattern were all independently associated with all-cause death (all p<0.001, Figure 2). The model of LGE granularity combining all independently significative LGE features showed the best improvement in model discrimination and reclassification above traditional prognosticators (C-statistic improvement: 0.90; NRI=41.9%; IDI=13.2%, Chi-2 global=450, all p<0.001; LR-test p<0.001, Figure 2). Conclusion In a large cohort of HCM patients, the LGE granularity model combining the extent, location, and pattern of LGE had an incremental prognostic value over and above traditional prognosticators to predict all-cause death. Prognostic value of LGE in HCM Incremental value of LGE granularity

Background Cardiac amyloidosis (CA) is a severe disease with poor prognosis and increasing incidence. Available scoring systems for prognostic stratification in light chain (AL) and transthyretin (ATTR) amyloidosis are based on limited biological parameters. Allowing process of a greater number and complexity of variables, machine learning (ML) could improve prognostic assessment. Objectives To investigate the feasibility and accuracy of supervised ML algorithms using clinical, biological and imaging features to predict all-cause mortality in CA patients. Methods Data were collected from the French Referral Center for Cardiac Amyloidosis database (Hôpital Henri-Mondor, Créteil), including 1513 patients with wild type ATTR (n=777), hereditary ATTR (n=304) and AL (n=432) CA between 2010 and 2023 (Fig. 1). Based on comprehensive clinical, biological and imaging features, we assessed accuracy of several supervised ML algorithms (Random Forest, Random Forest Ranger, XGBoost and LASSO) to predict all-cause mortality and compared with traditional logistic regression. Results Among 1513 CA included, 636 (42%) died during a median follow-up of 1.5 years (IQR: 0.5–3.1). ML score using XGBoost exhibited a higher area under the curve compared with logistic regression for prediction of all-cause mortality (AUC 0.76 vs 0.67, P<0.001; Fig. 2). In ATTR cohort, ML score using Random Forest ranger evidenced better performance compared with logistic regression (AUC ML score 0.77 vs 0.72 with logistic regression, P=0.008). However, in AL cohort, ML scores were not associated with an incremental prognostic value. Conclusion A ML-model including clinical, biological and imaging parameters showed the best accuracy to predict all-cause mortality in CA patients compared with any traditional methods.

Abstract Background After the REVIVED trial, the utility of cardiovascular magnetic resonance (CMR) assessing the myocardial viability to guide coronary revascularisation remains controversial. Objective To assess the prognostic impact of coronary revascularisation guided by CMR-based myocardial viability to predict death in patients with ischaemic cardiomyopathy (ICM) and reduced left ventricular ejection fraction (LVEF) <50%. Methods From 2008 to 2022, we included all consecutive ICM patients referred for CMR-based myocardial viability assessment in a multicentric study. Eligible patients had ≥70% stenosis in ≥1 epicardial coronary vessel on angiography, a history of myocardial infarction, or prior coronary revascularisation, and LVEF<50%. We collected data on revascularisation within 90 days of the index CMR. The primary endpoint was all-cause mortality. To define myocardial viability, ischaemic-late gadolinium enhancement (LGE) transmurality was assessed for maximal scar depth, considering myocardium with LGE transmurality <50% as viable and ≥50% as non-viable. Results Among 6,082 patients (mean age 65±12 years; 73% male), 3,591 (59%) exhibited ischaemic-LGE. Revascularisation was performed in 2,773 (46%) patients within 90 days of CMR. Revascularisation was more frequent in patients with viable myocardium (88%) than in those with non-viable myocardium (67%, Figure 1). Over a median follow-up of 9 years (interquartile range 7-12 years), 652 patients (11%) died. Using a Cox regression analysis, revascularisation was associated with a reduced risk of mortality (HR: 0.73 95% CI: 0.58-0.91, p<0.001, Figure 2A). Patients with LGE and myocardial viability who underwent revascularization had a similar prognosis to those without LGE (p=0.48). Interestingly, patients who underwent revascularisation with myocardial viability had better outcomes than those who underwent revascularisation without myocardial viability (p<0.001, Figure 2B). Conclusion In a large cohort of ICM patients, coronary revascularisation was beneficial and was associated with improved survival outcomes. The impact of this revascularisation was stronger in patients with myocardial viability assessed by CMR than patients without myocardial viability. Revascularisation in the ICM population Survival curves

Aims Although some scores based on traditional statistical methods are available for risk stratification in patients hospitalized in cardiac intensive care units (CICUs), the interest of machine learning (ML) methods for risk stratification in this field is not well established. We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU. Methods and results In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluated to predict in-hospital MAE. The eight remaining centres were used as an external validation cohort. Among 1499 consecutive patients included (aged 64 +/- 15 years, 70% male), 67 had in-hospital MAE (4.3%). Out of 28 clinical, biological, ECG, and echocardiographic variables, seven were selected to predict MAE in the training set (n = 844). Boosted cost-sensitive C5.0 technique showed the best performance compared with other ML methods [receiver operating characteristic area under the curve (AUROC) = 0.90, precision-recall AUC = 0.57, F1 score = 0.5]. Our ML score showed a better performance than existing scores (AUROC: ML score = 0.90 vs. Thrombolysis In Myocardial Infarction (TIMI) score: 0.56, Global Registry of Acute Coronary Events (GRACE) score: 0.52, Acute Heart Failure (ACUTE-HF) score: 0.65; all P < 0.05). Machine learning score also showed excellent performance in the external cohort (AUROC = 0.88). Conclusion This new ML score is the first to demonstrate improved performance in predicting in-hospital outcomes over existing scores in patients admitted to the intensive care unit based on seven simple and rapid clinical and echocardiographic variables. Trial Registration ClinicalTrials.gov Identifier: NCT05063097.

Background: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this issue, machine learning methods could assist in the detection of recreational drug use.Aims: To investigate the accuracy of a machine learning model using clinical, biological and echocardiographic data for detecting recreational drug use in patients admitted to intensive cardiac care units.Methods: From 07 to 22 April 2021, systematic screening for all traditional recreational drugs (cannabis, opioids, cocaine, amphetamines, 3,4-methylenedioxymethamphetamine) was performed by urinary testing in all consecutive patients admitted to intensive cardiac care units in 39 French centres. The primary outcome was recreational drug detection by urinary testing. The framework involved automated variable selection by eXtreme Gradient Boosting (XGBoost) and model building with multiple algorithms, using 31 centres as the derivation cohort and eight other centres as the validation cohort.Results: Among the 1499 patients undergoing urinary testing for drugs (mean age 63 ± 15 years; 70% male), 161 (11%) tested positive (cannabis: 9.1%; opioids: 2.1%; cocaine: 1.7%; amphetamines: 0.7%; 3,4-methylenedioxymethamphetamine: 0.6%). Of these, only 57% had reported drug use. Using nine variables, the best machine learning model (random forest) showed good performance in the derivation cohort (area under the receiver operating characteristic curve = 0.82) and in the validation cohort (area under the receiver operating characteristic curve = 0.76).Conclusions: In a large intensive cardiac care unit cohort, a comprehensive machine learning model exhibited good performance in detecting recreational drug use, and provided valuable insights into the relationships between clinical variables and drug use through explainable machine learning techniques.