Better Respiratory Function in Heart Failure Patients With Use of Central-Acting Therapeutics.

Background: Diaphragm atrophy can contribute to dyspnea in patients with heart failure (HF) with its link to central neurohormonal overactivation. HF medications that cross the blood-brain barrier could act centrally and improve respiratory function, potentially alleviating diaphragmatic atrophy. Therefore, we compared the benefit of central- vs peripheral-acting HF drugs on respiratory function, as assessed by a single cardiopulmonary exercise test (CPET) and outcomes in HF patients. Methods: A retrospective study was conducted of 624 ambulatory adult HF patients (80% male) with reduced left ventricular ejection fraction ≤ 40% and a complete CPET, followed at a single institution between 2001 and 2017. CPET parameters, and the outcomes all-cause death, a composite endpoint (all-cause death, need for left ventricular assist device, heart transplantation), and all-cause and/or HF hospitalizations, were compared in patients receiving central-acting (n = 550) vs peripheral-acting (n = 74) drugs. Results: Compared to patients who receive peripheral-acting drugs, patients who receive central-acting drugs had better respiratory function (peak breath-by breath oxygen uptake [VO2], P = 0.020; forced expiratory volume in 1 second [FEV1], P = 0.007), and ventilatory efficiency (minute ventilation / carbon dioxide production [VE/VCO2], P < 0.001; end-tidal carbon dioxide tension [PETCO2], P = 0.015; and trend for forced vital capacity [FVC], P = 0.056). Many of the associations between the CPET parameters and drug type remained significant after multivariate adjustment. Moreover, patients receiving central-acting drugs had fewer composite events (P = 0.023), and HF hospitalizations (P = 0.044), although significance after multivariant correction was not achieved, despite the hazard ratio being 0.664 and 0.757, respectively. Conclusions: Central-acting drugs were associated with better respiratory function as measured by CPET parameters in HF patients. This could extend to clinically meaningful composite outcomes and hospitalizations but required more power to be definitive in linking to drug effect. Central-acting HF drugs show a role in mitigating diaphragm weakness.

Contexte: L'atrophie du diaphragme peut contribuer à la dyspnée chez les personnes atteintes d'insuffisance cardiaque (IC), compte tenu de son lien avec la suractivation neuro-hormonale centrale. Or, les médicaments contre l'IC qui franchissent la barrière hématoencéphalique pourraient exercer une action centrale, améliorer la respiration et ainsi éventuellement atténuer l'atrophie du diaphragme. C'est pourquoi nous avons voulu comparer, au moyen d'une seule épreuve d'effort cardiopulmonaire (EECP), les effets bénéfiques exercés par des médicaments à action périphérique et des médicaments à action centrale sur la fonction respiratoire, de même que l'issue des patients atteints d'IC auxquels ils ont été administrés. Méthodologie: Nous avons réalisé une étude rétrospective auprès de 624 adultes ambulatoires atteints d'IC (80 % d'hommes) dont la fraction d'éjection ventriculaire gauche était réduite (≤ 40 %), qui se sont prêtés à une EECP complète et qui ont été suivis dans le même établissement entre 2001 et 2017. Les paramètres de l'EECP et la mortalité toutes causes confondues, un critère d'évaluation composé (décès toutes causes confondues, nécessité de recourir à un dispositif d'assistance ventriculaire gauche, transplantation cardiaque), et les hospitalisations toutes causes confondues et/ou liées à l'IC ont été comparés entre les patients qui recevaient des médicaments à action centrale (n = 550) et ceux qui recevaient des médicaments à action périphérique (n = 74). Résultats: Comparativement aux patients ayant reçu des médicaments à action périphérique, ceux qui ont reçu des médicaments à action centrale ont bénéficié d'une meilleure fonction respiratoire (consommation maximale d'oxygène [VO2], p = 0,020; volume expiratoire maximal par seconde [VEMS], p = 0,007) et d'une meilleure efficacité ventilatoire (ventilation minute/production de dioxyde de carbone [VE/VCO2], p < 0,001; pression partielle de dioxyde de carbone en fin d'expiration [PETCO2], p = 0,015; et tendance de la capacité vitale forcée [CVF], p = 0,056). De plus, bon nombre des associations entre les paramètres de l'EECP et le type de médicament sont demeurées significatives après ajustement multivarié. Les patients qui ont reçu des médicaments à action centrale ont également présenté moins d'événements faisant partie du critère d'évaluation composé (p = 0,023) et moins d'hospitalisations liées à l'IC (p = 0,044), même si la différence après correction multivariée n'a pas été significative et que les rapports de risques étaient respectivement de 0,664 et de 0,757. Conclusions: Les médicaments à action centrale ont été associés à une meilleure fonction respiratoire, mesurée à l'aide des paramètres d'une EECP, chez les patients atteints d'IC. Ce résultat pourrait également s'appliquer au critère d'évaluation composé et aux hospitalisations, mais une étude plus puissante est nécessaire pour établir un lien cliniquement significatif avec l'effet des médicaments. Les médicaments à action centrale contre l'IC ont donc un rôle à jouer dans la correction de la faiblesse du diaphragme.

Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach.

IMPORTANCE AND OBJECTIVES: The current medical paradigm of evidence-based medicine relies on clinical guidelines derived from randomized clinical trials (RCTs), but these guidelines often overlook individual variations in treatment effects. Approaches have been proposed to develop models predicting the effects of individualized management, such as predictive allocation, individualizing treatment allocation. It is currently unknown whether widespread implementation of predictive allocation could result in better population-level outcomes over guideline-based therapy. We sought to simulate the potential effect of predictive allocation using data from previously conducted RCTs. METHODS AND RESULTS: Data from 3 RCTs (positive trial, negative trial, trial stopped for futility) in pediatric cardiology were used in a computational simulation study to quantify the potential benefits of a personalized approach based on predictive allocation. Outcomes were compared when using a universal approach vs predictive allocation where each patient was allocated to the treatment associated with the lowest predicted probability of negative outcome. Compared to results from RCTs, predictive allocation yielded absolute risk reductions of 13.8% (95% confidence interval [CI] -1.9 to 29.5), 13.9% (95% CI 4.5-23.2), and 15.6% (95% CI 1.5-29.6), respectively, corresponding to a number needed to treat of 7.3, 7.2, and 6.4. The net benefit of predictive allocation was directly proportional to the performance of the prediction models and disappeared as model performance degraded below an area under the curve of 0.55. DISCUSSION: These findings highlight that predictive allocation could result in improved group-level outcomes, particularly when highly predictive models are available. These findings will need to be confirmed in simulations of other trials with varying conditions and eventually in RCTs of predictive vs guideline-based treatment allocation.

Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural network.

Aims: Mathematical models previously developed to predict outcomes in patients with heart failure (HF) generally have limited performance and have yet to integrate complex data derived from cardiopulmonary exercise testing (CPET), including breath-by-breath data. We aimed to develop and validate a time-to-event prediction model using a deep learning framework using the DeepSurv algorithm to predict outcomes of HF. Methods and results: Inception cohort of 2490 adult patients with high-risk cardiac conditions or HF underwent CPET with breath-by-breath measurements. Potential predictive features included known clinical indicators, standard summary statistics from CPETs, and mathematical features extracted from the breath-by-breath time series of 13 measurements. The primary outcome was a composite of death, heart transplant, or mechanical circulatory support treated as a time-to-event outcomes. Predictive features ranked as most important included many of the features engineered from the breath-by-breath data in addition to traditional clinical risk factors. The prediction model showed excellent performance in predicting the composite outcome with an area under the curve of 0.93 in the training and 0.87 in the validation data sets. Both the predicted vs. actual freedom from the composite outcome and the calibration of the prediction model were excellent. Model performance remained stable in multiple subgroups of patients. Conclusion: Using a combined deep learning and survival algorithm, integrating breath-by-breath data from CPETs resulted in improved predictive accuracy for long-term (up to 10 years) outcomes in HF. DeepSurv opens the door for future prediction models that are both highly performing and can more fully use the large and complex quantity of data generated during the care of patients with HF.

Kawasaki Disease in the Time of COVID-19 and MIS-C: The International Kawasaki Disease Registry.

BACKGROUND: Patients with multisystem inflammatory syndrome in children (MIS-C) and Kawasaki disease (KD) have overlapping clinical features. We compared demographics, clinical presentation, management, and outcomes of patients according to evidence of previous SARS-CoV-2 infection. METHODS: The International Kawasaki Disease Registry (IKDR) enrolled KD and MIS-C patients from sites in North, Central, and South America, Europe, Asia, and the Middle East. Evidence of previous infection was defined as: Positive (household contact or positive polymerase chain reaction [PCR]/serology), Possible (suggestive clinical features of MIS-C and/or KD with negative PCR or serology but not both), Negative (negative PCR and serology and no known exposure), and Unknown (incomplete testing and no known exposure). RESULTS: Of 2345 enrolled patients SARS-CoV-2 status was Positive for 1541 (66%) patients, Possible for 89 (4%), Negative for 404 (17%) and Unknown for 311 (13%). Clinical outcomes varied significantly among the groups, with more patients in the Positive/Possible groups presenting with shock, having admission to intensive care, receiving inotropic support, and having longer hospital stays. Regarding cardiac abnormalities, patients in the Positive/Possible groups had a higher prevalence of left ventricular dysfunction, and patients in the Negative and Unknown groups had more severe coronary artery abnormalities. CONCLUSIONS: There appears to be a spectrum of clinical features from MIS-C to KD with a great deal of heterogeneity, and one primary differentiating factor is evidence for previous acute SARS-CoV-2 infection/exposure. SARS-CoV-2 Positive/Possible patients had more severe presentations and required more intensive management, with a greater likelihood of ventricular dysfunction but less severe coronary artery adverse outcomes, in keeping with MIS-C.

Obesity and Outcomes of Kawasaki Disease and COVID-19-Related Multisystem Inflammatory Syndrome in Children.

Importance: Obesity may affect the clinical course of Kawasaki disease (KD) in children and multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19. Objective: To compare the prevalence of obesity and associations with clinical outcomes in patients with KD or MIS-C. Design, Setting, and Participants: In this cohort study, analysis of International Kawasaki Disease Registry (IKDR) data on contemporaneous patients was conducted between January 1, 2020, and July 31, 2022 (42 sites, 8 countries). Patients with MIS-C (defined by Centers for Disease Control and Prevention criteria) and patients with KD (defined by American Heart Association criteria) were included. Patients with KD who had evidence of a recent COVID-19 infection or missing or unknown COVID-19 status were excluded. Main Outcomes and Measures: Patient demographic characteristics, clinical features, disease course, and outcome variables were collected from the IKDR data set. Using body mass index (BMI)/weight z score percentile equivalents, patient weight was categorized as normal weight (BMI <85th percentile), overweight (BMI ≥85th to <95th percentile), and obese (BMI ≥95th percentile). The association between adiposity category and clinical features and outcomes was determined separately for KD and MIS-C patient groups. Results: Of 1767 children, 338 with KD (median age, 2.5 [IQR, 1.2-5.0] years; 60.4% male) and 1429 with MIS-C (median age, 8.7 [IQR, 5.3-12.4] years; 61.4% male) were contemporaneously included in the study. For patients with MIS-C vs KD, the prevalence of overweight (17.1% vs 11.5%) and obesity (23.7% vs 11.5%) was significantly higher (P < .001), with significantly higher adiposity z scores, even after adjustment for age, sex, and race and ethnicity. For patients with KD, apart from intensive care unit admission rate, adiposity category was not associated with laboratory test features or outcomes. For patients with MIS-C, higher adiposity category was associated with worse laboratory test values and outcomes, including a greater likelihood of shock, intensive care unit admission and inotrope requirement, and increased inflammatory markers, creatinine levels, and alanine aminotransferase levels. Adiposity category was not associated with coronary artery abnormalities for either MIS-C or KD. Conclusions and Relevance: In this international cohort study, obesity was more prevalent for patients with MIS-C vs KD, and associated with more severe presentation, laboratory test features, and outcomes. These findings suggest that obesity as a comorbid factor should be considered at the clinical presentation in children with MIS-C.

Cardiac Biomarkers Aid in Differentiation of Kawasaki Disease from Multisystem Inflammatory Syndrome in Children Associated with COVID-19.

Kawasaki disease (KD) and Multisystem Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 show clinical overlap and both lack definitive diagnostic testing, making differentiation challenging. We sought to determine how cardiac biomarkers might differentiate KD from MIS-C. The International Kawasaki Disease Registry enrolled contemporaneous KD and MIS-C pediatric patients from 42 sites from January 2020 through June 2022. The study population included 118 KD patients who met American Heart Association KD criteria and compared them to 946 MIS-C patients who met 2020 Centers for Disease Control and Prevention case definition. All included patients had at least one measurement of amino-terminal prohormone brain natriuretic peptide (NTproBNP) or cardiac troponin I (TnI), and echocardiography. Regression analyses were used to determine associations between cardiac biomarker levels, diagnosis, and cardiac involvement. Higher NTproBNP (≥ 1500 ng/L) and TnI (≥ 20 ng/L) at presentation were associated with MIS-C versus KD with specificity of 77 and 89%, respectively. Higher biomarker levels were associated with shock and intensive care unit admission; higher NTproBNP was associated with longer hospital length of stay. Lower left ventricular ejection fraction, more pronounced for MIS-C, was also associated with higher biomarker levels. Coronary artery involvement was not associated with either biomarker. Higher NTproBNP and TnI levels are suggestive of MIS-C versus KD and may be clinically useful in their differentiation. Consideration might be given to their inclusion in the routine evaluation of both conditions.

Factors Associated With Acute Kidney Injury After Cardiopulmonary Bypass in Children.

Background: Acute kidney injury (AKI) is a common complication after cardiovascular surgery in children, noted in approximately 40% of children undergoing cardiopulmonary bypass (CPB). We sought to determine the risk factors including inflammatory and vascular endothelial markers associated with AKI in children undergoing cardiac surgery. Methods: A secondary analysis of a prospective observational cohort study of paediatric patients with a cardiac defect requiring CPB and a weight of >2.5 kg was performed. AKI was defined as a 1.5 times increase from the preoperative value in serum creatinine or an absolute increase by ≥0.3 mg/dL (≥26.5 µmol/L). Plasma inflammatory markers (interleukin [IL]-1a, IL-1b, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, and tumour necrosis factor α) and vascular endothelial markers (vascular endothelial growth factor, von Willebrand factor, regulated on activation, normal T-cell expressed and secreted, granulocyte macrophage colony-stimulating factor, monocyte chemoattractant protein-1, platelet-derived growth factor, and microparticles) were assessed at 5 perioperative time points. Associations with AKI were found using generalized linear regression models adjusted for repeated measures. Results: A total of 207 patients were assessed, of whom 56% (n = 116) were male. Thirty-three percent (n = 68) developed AKI. In univariable analyses, adverse outcomes significantly related to the presence of AKI included increased intensive care unit stay (3.0 vs 5.6 hours, P < 0.001). In multivariable analysis, independent factors that were significantly associated with AKI included longer duration of CPB (111 vs 154 minutes, P < 0.001) and lower preoperative creatinine. Inflammatory and vascular endothelial biomarkers were not associated with AKI. Conclusions: AKI remains a prevalent problem after cardiac surgery, and renal ischemia related to longer bypass time potentially plays a key role in the etiology. Inflammatory and vascular endothelial biomarkers were not significantly related to AKI.

Contexte: L'insuffisance rénale aiguë (IRA) est une complication fréquente qui survient chez les enfants après une intervention chirurgicale cardiovasculaire. Environ 40 % des enfants chez qui une circulation extracorporelle (CEC) est mise en place durant l'intervention présentent ultérieurement une IRA. Nous avons tenté de définir les facteurs de risque, y compris les marqueurs inflammatoires et endothéliaux vasculaires, qui sont associés à l'IRA chez les enfants qui subissent une intervention chirurgicale cardiaque. Méthodologie: Nous avons réalisé une analyse secondaire d'une étude de cohorte observationnelle prospective menée auprès d'enfants qui étaient atteints d'une anomalie cardiaque nécessitant une CEC et qui pesaient plus de 2,5 kg. L'IRA était définie comme une hausse du taux de créatinine sérique par un facteur de 1,5 par rapport à la valeur préopératoire ou comme une augmentation absolue de ≥ 0,3 mg/dL (≥ 26,5 µmol/l). Les marqueurs inflammatoires plasmatiques (interleukine [IL]-1a, IL-1b, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, facteur de nécrose tumorale alpha) et les marqueurs endothéliaux vasculaires (facteur de croissance de l'endothélium vasculaire, facteur de von Willebrand, chimiokine exprimée et sécrétée après l'activation des lymphocytes T normaux, facteur de stimulation des granulocytes et macrophages, protéine chimiotactique des monocytes-1, facteur de croissance dérivé des plaquettes, microparticules) ont été évalués à 5 moments périopératoires différents. Les associations avec l'IRA ont été établies au moyen de modèles de régression linéaire généraux, qui ont été ajustés pour tenir compte des mesures répétées. Résultats: L'évaluation a porté sur 207 patients, dont 56 % (n = 116) étaient des garçons, et une IRA a été observée chez 33 % (n = 68) d'entre eux. Les résultats d'analyses univariées ont montré que les issues indésirables associées de façon significative à la présence d'une IRA comprenaient un séjour prolongé à l'unité de soins intensifs (3,0 c. 5,6 heures, p < 0,001). Dans les analyses multivariées, les facteurs indépendants associés de façon significative à une IRA comprenaient une CEC prolongée (111 c. 154 minutes, p < 0,001) et un faible taux de créatinine préopératoire. Les biomarqueurs inflammatoires et endothéliaux vasculaires n'ont pas été associés à l'IRA. Conclusions: L'IRA demeure un problème répandu après une intervention chirurgicale cardiaque. L'ischémie rénale associée à une CEC prolongée joue potentiellement un rôle clé dans son étiologie. Par ailleurs, les biomarqueurs inflammatoires et endothéliaux vasculaires n'ont pas été associés de façon significative à l'IRA.

The relationships between acetylcholine-induced chest pain, objective measures of coronary vascular function and symptom status.

Background: Acetylcholine-induced chest pain is routinely measured during the assessment of microvascular function. Aims: The aim was to determine the relationships between acetylcholine-induced chest pain and both symptom burden and objective measures of vascular function. Methods: In patients with angina but no obstructive coronary artery disease, invasive studies determined the presence or absence of chest pain during both acetylcholine and adenosine infusion. Thermodilution-derived coronary blood flow (CBF) and index of microvascular resistance (IMR) was determined at rest and during both acetylcholine and adenosine infusion. Patients with epicardial spasm (>90%) were excluded; vasoconstriction between 20% and 90% was considered endothelial dysfunction. Results: Eighty-seven patients met the inclusion criteria. Of these 52 patients (60%) experienced chest pain during acetylcholine while 35 (40%) did not. Those with acetylcholine-induced chest pain demonstrated: (1) Increased CBF at rest (1.6 ± 0.7 vs. 1.2 ± 0.4, p = 0.004) (2) Decreased IMR with acetylcholine (acetylcholine-IMR = 29.7 ± 16.3 vs. 40.4 ± 17.1, p = 0.004), (3) Equivalent IMR following adenosine (Adenosine-IMR: 21.1 ± 10.7 vs. 21.8 ± 8.2, p = 0.76), (4) Increased adenosine-induced chest pain (40/52 = 77% vs. 7/35 = 20%, p < 0.0001), (5) Increased chest pain during exercise testing (30/46 = 63% vs. 4/29 = 12%, p < 0.00001) with no differences in exercise duration or electrocardiographic changes, and (6) Increased prevalence of epicardial endothelial dysfunction (33/52 = 63% vs. 14/35 = 40%, p = 0.03). Conclusions: After excluding epicardial spasm, acetylcholine-induced chest pain is associated with increased pain during exercise and adenosine infusion, increased coronary blood flow at rest, decreased microvascular resistance in response to acetylcholine and increased prevalence of epicardial endothelial dysfunction. These findings raise questions about the mechanisms underlying acetylcholine-induced chest pain.

FGF21 and GDF15 are elevated in Barth Syndrome and are correlated to important clinical measures.

Barth Syndrome (BTHS) is a rare X-linked disorder that is caused by defects TAFAZZIN, which leads to an abnormal cardiolipin (CL) profile of the inner mitochondrial membrane and clinical features including cardiomyopathy, neutropenia and skeletal myopathy. The ratio of monolysocardiolipin (MLCL, the remodeling intermediate of cardiolipin) to remodeled CL is always abnormal in Barth Syndrome and 3-methylglutaconic acid is often elevated affected patients, however neither of these biomarkers has been shown to temporally correlate to clinical status. In this study, we measured plasma FGF21 and GDF15 levels in 16 individuals with Barth Syndrome and evaluated whether these biomarkers were correlated to the MLCL/CL ratio in patient bloodspots and clinical laboratory parameters indicative of organ involvement in Barth Syndrome including: neutrophil and monocyte counts, liver function, and cardiac function (NT-proBNP). We found that FGF21 and GDF15 were elevated in all 16 patients and that FGF21 was significantly correlated to AST, ALT GGT, percentage of neutrophils comprising total white blood cells, percent monocytes comprising total white blood cells, and NT-proBNP levels. GDF-15 was significantly positively associated with NT-proBNP. We conclude that clinical measurements of FGF21 and GDF-15 may be relevant in the monitoring multi-organ system involvement in Barth Syndrome.

Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation.

BACKGROUND: Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated. OBJECTIVE: The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings. METHODS: Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set. RESULTS: The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE. CONCLUSIONS: In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models.

Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data.

BACKGROUND: Barth syndrome (BTHS) is a rare genetic disease that is characterized by cardiomyopathy, skeletal myopathy, neutropenia, and growth abnormalities and often leads to death in childhood. Recently, elamipretide has been tested as a potential first disease-modifying drug. This study aimed to identify patients with BTHS who may respond to elamipretide, based on continuous physiological measurements acquired through wearable devices. RESULTS: Data from a randomized, double-blind, placebo-controlled crossover trial of 12 patients with BTHS were used, including physiological time series data measured using a wearable device (heart rate, respiratory rate, activity, and posture) and functional scores. The latter included the 6-minute walk test (6MWT), Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue score, SWAY Balance Mobile Application score (SWAY balance score), BTHS Symptom Assessment (BTHS-SA) Total Fatigue score, muscle strength by handheld dynamometry, 5 times sit-and-stand test (5XSST), and monolysocardiolipin to cardiolipin ratio (MLCL:CL). Groups were created through median split of the functional scores into "highest score" and "lowest score", and "best response to elamipretide" and "worst response to elamipretide". Agglomerative hierarchical clustering (AHC) models were implemented to assess whether physiological data could classify patients according to functional status and distinguish non-responders from responders to elamipretide. AHC models clustered patients according to their functional status with accuracies of 60-93%, with the greatest accuracies for 6MWT (93%), PROMIS (87%), and SWAY balance score (80%). Another set of AHC models clustered patients with respect to their response to treatment with elamipretide with perfect accuracy (all 100%). CONCLUSIONS: In this proof-of-concept study, we demonstrated that continuously acquired physiological measurements from wearable devices can be used to predict functional status and response to treatment among patients with BTHS.

COVID-19 Positive Versus Negative Complete Kawasaki Disease: A Study from the International Kawasaki Disease Registry.

To determine clinical differences for children with complete Kawasaki disease (KD) with and without evidence of preceding SARS-CoV-2 infection. From January 2020, contemporaneous patients with complete KD criteria were classified as either SARS-CoV-2 positive (KDCOVID+; confirmed household exposure, positive PCR and/or serology) or SARS-CoV-2 negative (KDCOVID-; negative testing and no exposure) and compared. Of 744 patients in the International Kawasaki Disease Registry, 52 were KDCOVID- and 61 were KDCOVID+. KDCOVID+ patients were older (median 5.5 vs. 3.7 years; p < 0.001), and all additionally met diagnostic criteria for multisystem inflammatory syndrome in children (MIS-C). They were more likely to have abdominal pain (60% vs. 35%; p = 0.008) and headache (38% vs. 10%; p < 0.001) and had significantly higher CRP, troponin, and BUN/creatinine, and lower hemoglobin, platelets, and lymphocytes. KDCOVID+ patients were more likely to have shock (41% vs. 6%; p < 0.001), ICU admission (62% vs. 10%; p < 0.001), lower left ventricular ejection fraction (mean lowest LVEF 53% vs. 60%; p < 0.001), and to have received inotropic support (60% vs. 10%; p < 0.001). Both groups received IVIG (2 doses in 22% vs. 18%; p = 0.63), but KDCOVID+ were more likely to have received steroids (85% vs. 35%; p < 0.001) and anakinra (60% vs. 10%; p = 0.002). KDCOVID- patients were more likely to have medium/large coronary artery aneurysms (CAA, 12% vs. 0%; p = 0.01). KDCOVID+ patients differ from KDCOVID-, have more severe disease, and greater evidence of myocardial involvement and cardiovascular dysfunction rather than CAA. These patients may be a distinct KD phenotype in the presence of a prevalent specific trigger.

Emerging Insights Into the Pathophysiology of Multisystem Inflammatory Syndrome Associated With COVID-19 in Children.

Multisystem inflammatory syndrome in children (MIS-C) has emerged as a rare delayed hyperinflammatory response to SARS-CoV-2 infection and causes severe morbidity in the pediatric age group. Although MIS-C shares many clinical similarities to Kawasaki disease (KD), important differences in epidemiologic, clinical, immunologic, and potentially genetic factors exist and suggest potential differences in pathophysiology and points to be explored and explained. Epidemiologic features include male predominance, peak age of 6 to12 years, and specific racial or ethnicity predilections. MIS-C is characterized by fever, prominent gastrointestinal symptoms, mucocutaneous manifestations, respiratory symptoms, and neurologic complaints, and patients often present with shock. Cardiac complications are frequent and include ventricular dysfunction, valvular regurgitation, pericardial effusion, coronary artery dilation and aneurysms, conduction abnormalities, and arrhythmias. Emerging evidence regarding potential immunologic mechanisms suggest that an exaggerated T-cell response to a superantigen on the SARS-CoV-2 spike glycoprotein-as well as the formation of autoantibodies against cardiovascular, gastrointestinal, and endothelial antigens-are major contributors to the inflammatory milieu of MIS-C. Further studies are needed to determine both shared and distinct immunologic pathway(s) that underlie the pathogenesis of MIS-C vs both acute SARS-CoV-2 infection and KD. There is evidence to suggest that the rare risk of more benign mRNA vaccine-associated myopericarditis is outweighed by a reduced risk of more severe MIS-C. In the current review, we synthesize the published literature to describe associated factors and potential mechanisms regarding an increased risk of MIS-C and cardiac complications, provide insights into the underlying immunologic pathophysiology, and define similarities and differences with KD.

Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review.

The omnipresence and deep impact of artificial intelligence (AI) in today's society are undeniable. While the technology has already established itself as a powerful tool in several industries, more recently it has also started to change the practice of medicine. The aim of this review is to provide healthcare providers working in the field of cardiovascular medicine with an overview of AI and machine learning (ML) algorithms that have passed the initial tests and made it into contemporary clinical practice. The following domains where AI/ML could revolutionize cardiology are covered: (i) signal processing, (ii) image processing, (iii) clinical risk stratification, (iv) natural language processing, and (v) fundamental clinical discoveries.

Association of Childhood Blood Pressure with Left Atrial Size and Function: Study of High Blood Pressure in Pediatrics-Adult Hypertension in Youth (SHIP AHOY).

OBJECTIVE(S): To evaluate the cross-sectional association of cardiovascular disease risk factors with left atrial (LA) size and function among healthy youth, aged 11-18 years, with a wide range of blood pressures (BPs). STUDY DESIGN: Echocardiographic images of youth enrolled in the Study of High Blood Pressure in Pediatrics: Adult Hypertension Onset in Youth study were analyzed for LA measurements. The association of casual BP, ambulatory BP, and other cardiovascular disease risk factors with LA size and function were determined using descriptive statistics and multivariable regression. Regression models adjusting for age, sex, race, and body mass index z score determined the independent association between ambulatory systolic BP indices (mean systolic BP/50th %ile systolic BP) and BP phenotypes with LA outcomes while exploratory analyses investigated for additional predictors of LA outcomes. RESULTS: The study population consisted of 347 youth: median age 15.7 years, 60% male and 40% non-White. Greater-risk casual systolic BP groups had worse cardiometabolic profiles but no differences in LA size and function. Each 0.1 increase in ambulatory systolic BP day or night index was associated with a 9.9 mL/m2 increase in LA volume/body surface area (LAV/BSA; 95th% CI 2.8-17.0, P = .006) and a 6.8 mL/m2 increase in LAV/BSA (95th% CI 0.8-12.8, P = .03), respectively. Ambulatory hypertension was associated with greater odds of abnormal LAV/BSA, defined as >75th %ile (2014 ambulatory BP monitoring criteria: OR 3.2 [95th% CI 1.4-7.2; P = .002]; 2022 ambulatory BP monitoring criteria: OR 2.1 [95th% CI 1.0-4.1; P = .008]). CONCLUSIONS: Increasing ambulatory systolic BP and ambulatory hypertension are independently associated with LAV/BSA.

Association Between the Incidence of Hospitalizations for Acute Cardiovascular Events, Weather, and Air Pollution.

Background: The incidence of hospitalizations for cardiovascular events has been associated with specific weather conditions and air pollution. A comprehensive model including the interactions between various environmental factors remains to be developed. Objectives: The purpose of this study was to develop a comprehensive model of the association between weather patterns and the incidence of cardiovascular events and use this model to forecast near-term spatiotemporal risk. Methods: We present a spatiotemporal analysis of the association between atmospheric data and the incidence rate of hospital admissions related to heart failure (922,132 episodes), myocardial infarction (521,988 episodes), and ischemic stroke (263,529 episodes) in ∼24 million people in Canada between 2007 and 2017. Our hierarchical Bayesian model captured the spatiotemporal distribution of hospitalizations and identified weather and air pollution-related factors that could partially explain fluctuations in incidence. Results: Models that included weather and air pollution variables outperformed models without those covariates for most event types. Our results suggest that environmental factors may interact in complex ways on human physiology. The impact of environmental factors was magnified with increasing age. The weather and air pollution variables included in our models were predictive of the future incidence of heart failure, myocardial infarction, and ischemic strokes. Conclusions: The increasing importance of environmental factors on cardiovascular events with increasing age raises the need for the development of educational materials for older patients to recognize environmental conditions where exacerbations are more likely. This model could be the basis of a forecasting system used for local, short-term clinical resource planning based on the anticipated incidence of events.

The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.

Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues. To address these challenges, the integration of artificial intelligence and machine learning holds great promise as they have the potential to revolutionize patient management and positively impact lifelong outcomes for individuals with TOF. Innovative applications of artificial intelligence and machine learning have spanned across multiple domains of TOF care, including screening and diagnosis, automated image processing and interpretation, clinical risk stratification, and planning and performing cardiac interventions. By embracing these advancements and incorporating them into routine clinical practice, personalized medicine could be delivered, leading to the best possible outcomes for patients. In this review, we provide an overview of these evolving applications and emphasize the challenges, limitations, and future potential for integrating them into clinical care.

De grandes avancées médicales touchant le diagnostic de la tétralogie de Fallot (TF), les techniques chirurgicales, les soins périopératoires ainsi que les soins continus au cours de l'enfance ont transformé le pronostic de cette maladie et prolongé la survie des patients, d'où la nécessité d'adopter une approche thérapeutique à long terme. Compte tenu du nombre croissant de survivants, certains défis prennent une plus grande ampleur et de nouvelles difficultés s'y ajoutent. Il convient donc de réévaluer les soins pour les patients atteints de TF. L'accès limité au diagnostic prénatal, les informations fragmentaires obtenues avec les techniques d'imagerie traditionnelles, les complications médicales inattendues et les débats sur les indications et le moment approprié pour les interventions chirurgicales subséquentes sont de nouveaux enjeux. Pour y faire face, l'intégration des outils d'intelligence artificielle (IA) et d'apprentissage automatique (AA) est prometteuse et pourrait réinventer la prise en charge des patients atteints de TF en plus d'améliorer leurs résultats à long terme. L'utilisation innovante de l'IA et de l'AA touche de nombreux aspects des soins offerts à ces patients, par exemple le dépistage et le diagnostic, l'analyse et l'interprétation automatiques d'images, la stratification du risque clinique de même que la planification et la réalisation d'interventions cardiaques. L'adoption de ces avancées technologiques et leur intégration dans la pratique clinique courante ouvrent la voie à une approche de médecine personnalisée dans l'espoir d'obtenir les meilleurs résultats possibles pour les patients. Notre article de synthèse présente ces applications en pleine évolution et met en évidence leurs perspectives d'intégration aux soins cliniques, mais aussi les défis et les limites qui accompagnent cette approche.

Perioperative Metabolites Are Associated With Adverse Neonatal Congenital Heart Disease Surgical Outcomes.

Background Clinical risk factors in neonatal cardiac surgery do not fully capture discrepancies in outcomes. Targeted metabolomic analysis of plasma from neonates undergoing heart surgery with cardiopulmonary bypass was performed to determine associations with clinical outcomes. Methods and Result Samples and clinical variables from 149 neonates enrolled in the Corticosteroid Therapy in Neonates Undergoing Cardiopulmonary Bypass trial with surgical treatment for congenital heart disease between 2012 and 2016 were included. Blood samples were collected before skin incision, immediately after cardiopulmonary bypass, and 12 hours after surgery. Outcomes include composite morbidity/mortality (death, extracorporeal membrane oxygenation, cardiac arrest, acute kidney injury, and/or hepatic injury) and a cardiac composite (extracorporeal membrane oxygenation, cardiac arrest, or increase in lactate level), hepatic injury, and acute kidney injury. Targeted metabolite levels were determined by high-resolution tandem liquid chromatography and mass spectrometry. Principal component and regression analyses were used to assess associations between metabolic profiles and outcomes, with 2 models created: a base clinical model and a base model+metabolites. Of the 193 metabolites examined, 40 were detected and quantified. The first principal component, principal component 1, was composed mostly of preoperative metabolites and was significantly associated with the composite morbidity/mortality, cardiac composite, and hepatic injury outcomes. In regression models, individual metabolites also improved model performance for the composite morbidity/mortality, cardiac composite, and hepatic injury outcomes. Significant disease pathways included myocardial injury (false discovery rate, 0.00091) and heart failure (false discovery rate, 0.041). Conclusions In neonatal cardiac surgery, perioperative metabolites were associated with postoperative outcomes and improved clinical model outcome associations. Preoperative metabolite levels alone may improve risk models and provide a basis for optimizing perioperative care.