Assessing Bullying Behaviors and the Efficacy of Bullying Prevention in Fourth Grade Classrooms

Introduction: Bullying has recently gained notoriety as a serious concern across all countries. Bullying is generally acknowledged to be a repeated pattern of abuse communicated to a victim by physical, verbal, or written means which results in bodily harm or emotional injury. Victims of bullying have been shown to be at increased risk for suicide, depression, anxiety, headaches, or difficulty sleeping. Puppets in Education (PiE) is a non-profit organization that uses interactive puppet shows and workshops to educate more than 8,000 children per year about disabilities, cultural diversity, and a wide variety of other issues. By performing its shows in classrooms throughout the state, PiE works to model realistic, challenging situations for children and to provide simple and practical strategies for dealing with them. Focusing our attention on the effects of bullying behaviors in schools, our team worked with PiE and several local fourth grade classes to determine the amount of information children retain from the organization’s bullying prevention program, the effectiveness of the program in addressing and preventing bullying behaviors, and the students’ overall perception of the program.https://scholarworks.uvm.edu/comphp_gallery/1074/thumbnail.jp

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data

Introduction Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance. Materials and methods Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid. Results The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2. Conclusion This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions

Multicenter registry and test bed for extended outpatient hemodynamic monitoring: the hemodynamic frontiers in heart failure (HF2) initiative

BackgroundHemodynamic Frontiers in Heart Failure (HF2) is a multicenter academic research consortium comprised of 14 US institutions with mature remote monitoring programs for ambulatory patients with heart failure (HF). The consortium developed a retrospective and prospective registry of patients implanted with a wireless pulmonary artery pressure (PAP) sensor.Goals/aimsHF2 registry collects demographic, clinical, laboratory, echocardiographic (ECHO), and hemodynamic data from patients with PAP sensors. The aims of HF2 are to advance understanding of HF and to accelerate development of novel diagnostic and therapeutic innovations.MethodsHF2 includes adult patients implanted with a PAP sensor as per FDA indications (New York Heart Association (NYHA) Class III HF functional class with a prior hospitalization, or patients with NYHA Class II or brain natriuretic peptide (BNP) elevation without hospitalization) at a HF2 member site between 1/1/19 to present. HF2 registry is maintained at University of Kansas Medical Center (KUMC). The registry was approved by the institutional review board (IRB) at all participating institutions with required data use agreements. Institutions report data into the electronic registry database using REDCap, housed at KUMC.ResultsThis initial data set includes 254 patients implanted from the start of 2019 until May 2023. At time of device implant, the cohort average age is 73 years old, 59.8% are male, 72% have NYHA Class III HF, 40% have left ventricular ejection fraction (LVEF) < 40%, 35% have LVEF > 50%, mean BNP is 560 pg/ml, mean N-Terminal pro-BNP (NTproBNP) is 5,490 pg/ml, mean creatinine is 1.65 mg/dl. Average baseline hemodynamics at device implant are right atrial pressure (RAP) of 11 mmHg, pulmonary artery systolic pressure (PASP) of 47 mmHg, pulmonary artery diastolic pressure (PADP) 21 mmHg, mean pulmonary artery pressure (mPAP) of 20 mmHg, pulmonary capillary wedge pressure (PCWP) of 19 mmHg, cardiac output (CO) of 5.3 L/min, and cardiac index (CI) of 2.5 L/min/m2.ConclusionA real-world registry of patients implanted with a PAP sensor enables long-term evaluation of hemodynamic and clinic outcomes in highly-phenotyped ambulatory HF patients, and creates a unique opportunity to validate and test novel diagnostic and therapeutic approaches to HF

Impact of exercise on pulmonary artery pressure in patients with heart failure using an ambulatory pulmonary artery pressure monitor.

BACKGROUND: In this multicenter prospective study, we explored the relationship between pulmonary artery pressure (PAP) at rest and in response to a 6-min walk test (6MWT) in ambulatory patients with heart failure (HF) with an implantable PAP sensor (CardioMEMS, Abbott). METHODS: Between 5/2019 and 2/2021, HF patients with a CardioMEMS sensor were recruited from seven sites. PAP was recorded in the supine and seated position at rest and in the seated position immediately post-exercise. RESULTS: In our cohort of 66 patients, mean age was 70 ± 12 years, 67% male, left ventricular ejection fraction (LVEF) \u3c 50% in 53%, mean 6MWT distance was 277 ± 95 meters. Resting seated PAPs were 31 ± 15 mmHg (systolic), 13 ± 8 mmHg (diastolic), and 20 ± 11 mmHg (mean). The pressures were lower in the seated rather than the supine position. After 6MWT, the pressures increased to PAP systolic 37 ± 19 mmHg ( CONCLUSION: The measurement of PAP with CardioMEMS is feasible immediately post-exercise. Despite being well-managed, patients had severely limited functional capacity. We observed a significant increase in PAP with ambulation which was greater in patients with higher baseline pressures

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out‐of‐hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out‐of‐hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single‐lead ECGs that comprised the study data set. ECGs of 7‐s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990–1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7‐s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%–98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871–0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT0366280

Impact of exercise on pulmonary artery pressure in patients with heart failure using an ambulatory pulmonary artery pressure monitor.

BACKGROUND: In this multicenter prospective study, we explored the relationship between pulmonary artery pressure (PAP) at rest and in response to a 6-min walk test (6MWT) in ambulatory patients with heart failure (HF) with an implantable PAP sensor (CardioMEMS, Abbott). METHODS: Between 5/2019 and 2/2021, HF patients with a CardioMEMS sensor were recruited from seven sites. PAP was recorded in the supine and seated position at rest and in the seated position immediately post-exercise. RESULTS: In our cohort of 66 patients, mean age was 70 ± 12 years, 67% male, left ventricular ejection fraction (LVEF) \u3c 50% in 53%, mean 6MWT distance was 277 ± 95 meters. Resting seated PAPs were 31 ± 15 mmHg (systolic), 13 ± 8 mmHg (diastolic), and 20 ± 11 mmHg (mean). The pressures were lower in the seated rather than the supine position. After 6MWT, the pressures increased to PAP systolic 37 ± 19 mmHg ( CONCLUSION: The measurement of PAP with CardioMEMS is feasible immediately post-exercise. Despite being well-managed, patients had severely limited functional capacity. We observed a significant increase in PAP with ambulation which was greater in patients with higher baseline pressures