Synthetic multi-contrast late gadolinium enhancement imaging using post-contrast magnetic resonance fingerprinting.

Late gadolinium enhancement (LGE) MRI is the non-invasive reference standard for identifying myocardial scar and fibrosis but has limitations, including difficulty delineating subendocardial scar and operator dependence on image quality. The purpose of this work is to assess the feasibility of generating multi-contrast synthetic LGE images from post-contrast T1 and T2 maps acquired using magnetic resonance fingerprinting (MRF). Fifteen consecutive patients with a history of prior ischemic cardiomyopathy (12 men; mean age 63  ±  13 years) were prospectively scanned at 1.5 T between Oct 2020 and May 2021 using conventional LGE and MRF after injection of gadolinium contrast. Three classes of synthetic LGE images were derived from MRF post-contrast T1 and T2 maps: bright-blood phase-sensitive inversion recovery (PSIR), black- and gray-blood T2 -prepared PSIR (T2 -PSIR), and a novel "tissue-optimized" image to enhance differentiation among scar, viable myocardium, and blood. Image quality was assessed on a 1-5 Likert scale by two cardiologists, and contrast was quantified as the mean absolute difference (MAD) in pixel intensities between two tissues, with different methods compared using Kruskal-Wallis with Bonferroni post hoc tests. Per-patient and per-segment scar detection rates were evaluated using conventional LGE images as reference. Image quality scores were highest for synthetic PSIR (4.0) and reference images (3.8), followed by synthetic tissue-optimized (3.3), gray-blood T2 -PSIR (3.0), and black-blood T2 -PSIR (2.6). Among synthetic images, PSIR yielded the highest myocardium/scar contrast (MAD = 0.42) but the lowest blood/scar contrast (MAD = 0.05), and vice versa for T2 -PSIR, while tissue-optimized images achieved a balance among all tissues (myocardium/scar MAD = 0.16, blood/scar MAD = 0.26, myocardium/blood MAD = 0.10). Based on reference mid-ventricular LGE scans, 13/15 patients had myocardial scar. The per-patient sensitivity/accuracy for synthetic images were the following: PSIR, 85/87%; black-blood T2 -PSIR, 62/53%; gray-blood T2 -PSIR, 100/93%; tissue optimized, 100/93%. Synthetic multi-contrast LGE images can be generated from post-contrast MRF data without additional scan time, with initial feasibility shown in ischemic cardiomyopathy patients.

Permanent Pacemaker Insertion Reduction and Optimized Temporary Pacemaker Management After Contemporary Transcatheter Aortic Valve Implantation With Self-Expanding Valves (from the Pristine TAVI Study).

Permanent pacemaker implantation (PPMI) reduction and optimal management of newly acquired conduction disturbances after transcatheter aortic valve implantation (TAVI) are crucial. We sought to evaluate the relation between transcatheter heart valve (THV) implantation depth and baseline and newly acquired conduction disturbances on PPMI after TAVI. This study included 1,026 consecutive patients with severe symptomatic aortic stenosis (mean age 79.7 ± 8.4 years; 47.4% female) who underwent TAVI with the newer-generation self-expanding THVs Primary outcomes were early and late PPMI defined as the need for PPMI during the index admission and between discharge and 30 days, respectively. Early and late PPMI was required for 115 (11.2%) and 21 patients (2.0%), respectively. Early PPMI rates decreased from 26.7% in 2015 and 2016 to 5.7% in 2021, and so did the mean THV depth from 4.4 ± 2.4 mm to 1.8 ± 1.6 mm. Receiver operator characteristics curve analyses showed THV depth had significant discriminatory value for early and late PPMI with cutoff values of 3.0 and 2.2 mm, respectively. Rates of early and late PPMI were significantly lower for patients with shallower compared with deeper implantations (5.1% vs 22.6% and 0.4% vs 4.1%, p <0.001 for both, respectively). Furthermore, rates of early PPMI were lower with shallower implantations in patients with new left bundle branch block after TAVI (2.4% vs 15.9%; p <0.001) and those with baseline right bundle branch block (7.5% vs 29.6%; p = 0.017). Lower rates of PPMI with shallower THV implantation were consistently observed, including in patients with baseline and newly acquired conduction disturbances. Our findings might help optimize the management of a temporary pacemaker after TAVI.

Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring.

Background: Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD. Methods: We analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (Str) and internal validation (Sv) sets [Str (1): Sv (1): 50:50; Str (2): Sv (2): 60:40; Str (3): Sv (3): 70:30; Str (4): Sv (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from Str to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of Mecg, Mcac, Mecg+cac and Mnom in predicting CV disease-free survival in ASCVD. Findings: Over a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (Str). The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all Sv). The Mecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that Mecg+cac was superior to Mecg (p = 1.8e-10) or Mcac (p < 2.2e-16) alone. The Mnom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, Mnom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables. Conclusion: The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.

Health Needs Assessment of Five Pennsylvania Plain Populations.

We performed a health needs assessment for five Plain communities in Pennsylvania from a random sample of households, comparing them to the general population of Pennsylvania adults. Plain respondents were more likely to drink well water, as likely to eat fruit and vegetables and much more likely to drink raw milk and be exposed to agricultural chemicals. Plain respondents were less likely to receive screening exams compared to the general population and there was variation from settlement to settlement in whether respondents had a regular doctor, whether they received preventive screenings or had their children vaccinated, with Mifflin County Amish generally lowest in these and Plain Mennonites highest. Plain respondents reported good physical and mental health compared to the general population but Groffdale Mennonite respondents had a high proportion of diagnoses of depression and were more likely to be receiving treatment for a mental health condition. Most Plain respondents would want a spouse tested for genetic disease with Mifflin County Amish least in favor of these tests. Despite their geographic and genetic isolation, the health of Plain communities in Pennsylvania is similar to that of other adults in the state.