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Background Women with cardiomyopathy (CM) are often advised against pregnancy due to risk for major adverse cardiovascular events (MACE). However, the impact of CM subtype on maternal MACE is not understood, and so we sought to evaluate the influence of CM phenotype on maternal outcomes, as well as the effect on immediate and late left ventricular function. Methods We evaluated all pregnant women in our high-risk maternal cardiovascular programme (2009–2019). Composite maternal MACE included: death, inotrope use, left ventricular assist device, orthotopic heart transplant and/or escalation in transplant listing status, acute decompensated heart failure and sustained ventricular arrhythmia. Results Among 875 women followed, 32 had CM (29±7 years old, left ventricular ejection fraction (LVEF) 41%±12%): 3 ischaemic CM (ICM), 10 peripartum CM (PPCM) and 19 non-ICM (NICM). MACE events occurred in 6 (18%) women (PPCM: 2 (33%), NICM: 4 (67%)). There was no difference in LVEF at baseline, however, women with MACE had significantly lower LVEF both early (LVEF: 27±5% vs . 41±2%, p<0.05) and late post partum (LVEF: 28±5% vs . 44±2%, p<0.01). Conclusions In this contemporary cohort of women with CM, maternal MACE rates were lower than previously reported, and were less common in PPCM as compared with ICM and NICM. Heart function in women with MACE was negatively impacted immediately after delivery and in late postpartum follow-up, suggesting that pregnancy itself likely has influence on future left ventricular function in women with underlying CM.

AbstractIntroductionThe efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders.MethodsWe retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response.ResultsWe compared the performance of EoE against traditional ML methods and the state‐of‐the‐art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1‐score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results.ConclusionThe proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high‐risk patients.

AbstractObjectiveThis study was undertaken to describe long‐term clinical and developmental outcomes in pediatric refractory status epilepticus (RSE) and identify factors associated with new neurological deficits after RSE.MethodsWe performed retrospective analyses of prospectively collected observational data from June 2011 to March 2020 on pediatric patients with RSE. We analyzed clinical outcomes from at least 30 days after RSE and, in a subanalysis, we assessed developmental outcomes and evaluated risk factors in previously normally developed patients.ResultsFollow‐up data on outcomes were available in 276 patients (56.5% males). The median (interquartile range [IQR]) follow‐up duration was 1.6 (.9–2.7) years. The in‐hospital mortality rate was 4% (16/403 patients), and 15 (5.4%) patients had died after hospital discharge. One hundred sixty‐six (62.9%) patients had subsequent unprovoked seizures, and 44 (16.9%) patients had a repeated RSE episode. Among 116 patients with normal development before RSE, 42 of 107 (39.3%) patients with available data had new neurological deficits (cognitive, behavioral, or motor). Patients with new deficits had longer median (IQR) electroclinical RSE duration than patients without new deficits (10.3 [2.1–134.5] h vs. 4 [1.6–16] h, p = .011, adjusted odds ratio = 1.003, 95% confidence interval = 1.0008–1.0069, p = .027). The proportion of patients with an unfavorable functional outcome (Glasgow Outcome Scale‐Extended score ≥ 4) was 22 of 90 (24.4%), and they were more likely to have received a continuous infusion.SignificanceAbout one third of patients without prior epilepsy developed recurrent unprovoked seizures after the RSE episode. In previously normally developing patients, 39% presented with new deficits during follow‐up, with longer electroclinical RSE duration as a predictor.

Abstract Objective Ultra long‐term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long‐term quality and consistency of the sqEEG signal, which is the objective of this study. Methods The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG ™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long‐term temporal trends in signal impedance and power spectral features were investigated with subject‐specific linear regression models and group‐level linear mixed‐effects models. Results sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8‐13Hz) and nocturnal peaks in the sigma range (12‐16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. Significance The spectral characteristics of minimally invasive, ultra long‐term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain‐computer interfaces.

While published guidelines are useful in the care of patients with long-QT syndrome, it can be difficult to decide how to apply the guidelines to individual patients, particularly those with intermediate risk. We explored the diversity of opinion among 24 clinicians with expertise in long-QT syndrome. Experts from various regions and institutions were presented with 4 challenging clinical scenarios and asked to provide commentary emphasizing why they would make their treatment recommendations. All 24 authors were asked to vote on case-specific questions so as to demonstrate the degree of consensus or divergence of opinion. Of 24 authors, 23 voted and 1 abstained. Details of voting results with commentary are presented. There was consensus on several key points, particularly on the importance of the diagnostic evaluation and of β-blocker use. There was diversity of opinion about the appropriate use of other therapeutic measures in intermediate-risk individuals. Significant gaps in knowledge were identified.

Background We aimed to provide personalized risk estimates for cardiac events (CEs) and life‐threatening events in women with either type 1 or type 2 long QT. Methods and Results The prognostic model was derived from the Rochester Long QT Syndrome Registry, comprising 767 women with type 1 long QT (n=404) and type 2 long QT (n=363) from age 15 through 60 years. The risk prediction model included the following variables: genotype/mutation location, QTc‐specific thresholds, history of syncope, and β‐blocker therapy. A model was developed with the end point of CEs (syncope, aborted cardiac arrest, or long QT syndrome–related sudden cardiac death), and was applied with the end point of life‐threatening events (aborted cardiac arrest, sudden cardiac death, or appropriate defibrillator shocks). External validation was performed with data from the Mayo Clinic Genetic Heart Rhythm Clinic (N=467; type 1 long QT [n=286] and type 2 long QT [n=181]). The cumulative follow‐up duration among the 767 enrolled women was 22 243 patient‐years, during which 323 patients (42%) experienced ≥1 CE. Based on genotype‐phenotype data, we identified 3 risk groups with 10‐year projected rates of CEs ranging from 15%, 29%, to 51%. The corresponding 10‐year projected rates of life‐threatening events were 2%, 5%, and 14%. C statistics for the prediction model for the 2 respective end points were 0.68 (95% CI 0.65–0.71) and 0.71 (95% CI 0.66–0.76). Corresponding C statistics for the model in the external validation Mayo Clinic cohort were 0.65 (95% CI 0.60–0.70) and 0.77 (95% CI 0.70–0.84). Conclusions This is the first risk prediction model that provides absolute risk estimates for CEs and life‐threatening events in women with type 1 or type 2 long QT based on personalized genotype‐phenotype data. The projected risk estimates can be used to guide female‐specific management in long QT syndrome.

Background: Collateral blood flow, modulated by inflammation and arteriogenesis, alters ischemic injury in intracranial atherosclerosis. We investigated the potential link between several inflammatory and arteriogenic biomarkers with angiographic findings at enrollment in BIOSIS/SAMMPRIS. Methods: Baseline angiography and blood biomarkers of BIOSIS/SAMMPRIS, which included subjects with stroke or TIA due to 70-99% intracranial stenosis were analyzed. Collateral blood flow status at angiography was categorized by the combination of antegrade flow (TICI) and corresponding extent of collaterals (ASITN/SIR). Collateral status was analyzed with respect to blood progenitor cell markers (CD34) at baseline and inflammatory biomarkers (Plasminogen Activator Inhibitor-1 (PAI-1), E-selectin, high sensitivity C-reactive protein (hsCRP) and Lipoprotein-associated phospholipase A2 (Lp-PLA 2 ) activity and concentrations) at baseline, 30 days and 4 months. Results: 376 subjects (mean age 60.44 years SD 11; 149/376 (39.6%) women) enrolled in BIOSIS/SAMMPRIS had angiography of collaterals and biomarker data. Collateral perfusion was impaired in 71 (19%) and intermediate in 188 (50%), with robust collaterals in 117 (30%) subjects. Better collateral status was associated with younger age (p=0.001), male sex (p=0.029) and greater baseline physical activity (p=0.007). Baseline blood progenitor cell (CD34+) counts were unrelated to angiographic features at enrollment, whereas inflammatory Lp-PLA 2 activity was linked (p=0.059) with blood flow categories, and lower hsCRP (p=0.025) with better collaterals. Lp-PLA 2 activity was lowest in those with robust collaterals at 30 days (p=0.070) and 4 months (p=0.120). Those with robust collaterals had the greatest decrease in Lp-PLA 2 activity (p=0.021) and concentration (p=0.027) by 30 days, with a continued decline of Lp-PLA 2 activity (p=0.037) to 4 months. Disparate trends in inflammatory markers were seen in the medical and stenting arms. Conclusions: Inflammatory biomarkers are linked with collateral blood flow status in intracranial atherosclerosis. The impact of physical activity, medications and other therapeutic investigations may disclose important mechanisms to avert ischemia.