Unrecognized myocardial scar by late-gadolinium-enhancement cardiovascular magnetic resonance: Insights from the population-based Hamburg City Health Study.

BACKGROUND: The presence of myocardial scar is associated with poor prognosis in several underlying diseases. Late-gadolinium-enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging reveals clinically silent "unrecognized myocardial scar" (UMS), but the etiology of UMS often remains unclear. This population-based CMR study evaluated prevalence, localization, patterns, and risk factors of UMS. METHODS: The study population consisted of 1064 consecutive Hamburg City Health Study participants without a history of coronary heart disease or myocarditis. UMS was assessed by standard-phase-sensitive-inversion-recovery LGE CMR. RESULTS: Median age was 66 [quartiles 59, 71] years and 37% (388/1064) were females. UMS was detected in 244 (23%) participants. Twenty-five participants (10%) had ischemic, and 217 participants (89%) had non-ischemic scar patterns, predominantly involving the basal inferolateral left-ventricular (LV) myocardium (75%). Two participants (1%) had coincident ischemic and non-ischemic scar. The presence of any UMS was independently associated with LV ejection fraction (odds ratios (OR) per standard deviation (SD) 0.77 (confidence interval (CI) 0.65-0.90), p = 0.002) and LV mass (OR per SD 1.54 (CI 1.31-1.82), p < 0.001). Ischemic UMS was independently associated with LV ejection fraction (OR per SD 0.58 (CI 0.39-0.86), p = 0.007), LV mass (OR per SD 1.74 (CI 1.25-2.45), p = 0.001), and diabetes (OR 4.91 (CI 1.66-13.03), p = 0.002). Non-ischemic UMS was only independently associated with LV mass (OR per SD 1.44 (CI 1.24-1.69), p < 0.001). CONCLUSION: UMS, in particular with a non-ischemic pattern, is frequent in individuals without known cardiac disease and predominantly involves the basal inferolateral LV myocardium. Presence of UMS is independently associated with a lower LVEF, a higher LV mass, and a history of diabetes.

Personalized diagnosis in suspected myocardial infarction.

BACKGROUND: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. METHODS: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy. CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www. CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www. CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www. CLINICALTRIALS: gov ; NCT01852123), LUND ( www. CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www. CLINICALTRIALS: gov ; NCT03111862), ROMI ( www. CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www. CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www. CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www. CLINICALTRIALS: gov ; NCT02060760).

Impact of Sex and Cardiovascular Risk Factors on Myocardial T1, Extracellular Volume Fraction, and T2 at 3 Tesla: Results From the Population-Based, Hamburg City Health Study.

BACKGROUND: Reliable reference intervals are crucial for clinical application of myocardial T1 and T2 mapping cardiovascular magnetic resonance imaging. This study evaluated the impact of sex and cardiovascular risk factors on myocardial T1, extracellular volume fraction (ECV), and T2 at 3T in the population-based HCHS (Hamburg City Health Study). METHODS: The final study sample consisted of 1576 consecutive HCHS participants between 46 and 78 years without prevalent heart disease, including 1020 (67.3%) participants with hypertension and 110 (7.5%) with diabetes. T1 and T2 mapping were performed on a 3T scanner using 5b(3b)3b modified Look-Locker inversion recovery and T2 prepared, fast-low-angle shot sequence, respectively. Stepwise regression analyses were performed to identify variables with an independent impact on T1, ECV, and T2. Reference intervals were defined as the interval between the 2.5% and 97.5% quantiles. RESULTS: Sex was the major independent influencing factor of myocardial native T1, ECV, and T2. Female patients had significantly higher upper limits of reference intervals for native T1 (1112-1261 versus 1079-1241 ms), ECV (23%-33% versus 22%-32%), and T2 (36-46 versus 35-45 ms) compared with male patients (all P<0.001). Cardiovascular risk factors, such as diabetes and hypertension, did not systematically affect native T1. There was an independent association of T2 by hypertension and, to a lesser degree, by left ventricular mass, heart rate (all P<0.001), and body mass index (P=0.001). CONCLUSIONS: Sex needs to be considered as the major, independent influencing factor for clinical application of myocardial T1, ECV, and T2 measurements. Consequently, sex-specific reference intervals should be used in clinical routine. Our findings suggest that there is no need for specific reference intervals for myocardial T1 and ECV measurements in individuals with cardiovascular risk factors. However, hypertension should be considered as an additional factor for clinical application of T2 measurements. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03934957.