International telemedicine consultations for neurodevelopmental disabilities

Background: A telemedicine program was developed between the Children\u27s National Medical Center (CNMC) in Washington, DC, and the Sheikh Khalifa Bin Zayed Foundation in the United Arab Emirates (UAE). A needs assessment and a curriculum of on-site training conferences were devised preparatory to an ongoing telemedicine consultation program for children with neurodevelopmental disabilities in the underserved eastern region of the UAE. Materials and Methods: Weekly telemedicine consultations are provided by a multidisciplinary faculty. Patients are presented in the UAE with their therapists and families. Real-time (video over Internet protocol; average connection, 768 kilobits/s) telemedicine conferences are held weekly following previews of medical records. A full consultation report follows each telemedicine session. Results: Between February 29, 2012 and June 26, 2013, 48 weekly 1-h live interactive telemedicine consultations were conducted on 48 patients (28 males, 20 females; age range, 8 months–22 years; median age, 5.4 years). The primary diagnoses were cerebral palsy, neurogenetic disorders, autism, neuromuscular disorders, congenital anomalies, global developmental delay, systemic disease, and epilepsy. Common comorbidities were cognitive impairment, communication disorders, and behavioral disorders. Specific recommendations included imaging and DNA studies, antiseizure management, spasticity management including botulinum toxin protocols, and specific therapy modalities including taping techniques, customized body vests, and speech/language and behavioral therapy. Improved outcomes reported were in clinician satisfaction, achievement of therapy goals for patients, and requests for ongoing sessions. Conclusions: Weekly telemedicine sessions coupled with triannual training conferences were successfully implemented in a clinical program dedicated to patients with neurodevelopmental disabilities by the Center for Neuroscience at CNMC and the UAE government. International consultations in neurodevelopmental disabilities utilizing telemedicine services offer a reliable and productive method for joint clinical programs

ST segment depression after Norwood/systemic-pulmonary artery shunt

A three-month-old girl with double inlet left ventricle (S;D;D), hypoplastic outlet ventricle, restrictive bulboventricular foramen, d-transposition of great arteries and interrupted aortic arch underwent Norwood stage 1 operation, systemic to pulmonary artery (PA) shunt and atrial septectomy, and developed ectopic atrial tachycardia that responded to digoxin therapy. She developed fussiness lasting two hours, not associated with cyanosis, and not relieved by feeding. Her pulse oximetry was 88% while breathing room air, and she had no differential blood pressure gradient

Comparison between different strategies of rheumatic heart disease echocardiographic screening in Brazil: Data from the PROVAR (Rheumatic Valve Disease Screening Program) study

Background-—Considering the limited accuracy of clinical examination for early diagnosis of rheumatic heart disease (RHD), echocardiography has emerged as an important epidemiological tool. The ideal setting for screening is yet to be defined. We aimed to evaluate the prevalence and pattern of latent RHD in schoolchildren (aged 5–18 years) and to compare effectiveness of screening between public schools, private schools, and primary care centers in Minas Gerais, Brazil. Methods and Results-—The PROVAR (Rheumatic Valve Disease Screening Program) study uses nonexperts and portable and handheld devices for RHD echocardiographic screening, with remote interpretation by telemedicine, according to the 2012 World Heart Federation criteria. Compliance with study consent and prevalence were compared between different screening settings, and variables associated with RHD were analyzed. In 26 months, 12 048 students were screened in 52 public schools (n=10 901), 2 private schools (n=589), and 3 primary care centers (n=558). Median age was 12.9 years, and 55.4% were girls. Overall RHD prevalence was 4.0% borderline (n=486) and 0.5% definite (n=63), with statistically similar rates between public schools (4.6%), private schools (3.5%), and primary care centers (4.8%) (P=0.24). The percentage of informed consents signed was higher in primary care centers (84.4%) and private schools (66.9%) compared with public schools (38.7%) (P\u3c0.001). Prevalence was higher in children ≥12 years (5.3% versus 3.1%; P\u3c0.001) and girls (4.9% versus 4.0%; P=0.02). Only age (odds ratio, 1.12; 95% confidence interval, 1.09–1.17; P\u3c0.001) was independently associated with RHD. Conclusions-—RHD screening in primary care centers seems to achieve higher coverage rates. Prevalence among schoolchildren is significantly high, with rates higher than expected in private schools of high-income areas. These data are important for the formulation of public policies to confront RHD. (J Am Heart Assoc. 2018;7:e008039. DOI: 10.1161/JAHA.117.008039.

Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation

Background Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. Methods and Results We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9‐feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87. Conclusions Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD