Changes in the 2017 Pediatric Hypertension Clinical Guidelines.

The clinical practice guidelines on diagnosis and management of high blood pressure in children and adolescents have been periodically modified and updated since the original publication in 1977.1 Since the last pediatric blood pressure guideline was published in 2004, known as the Fourth Report,2 the literature on child BP and hypertension has expanded considerably. There has been a recognized need to update the Fourth Report for several years. However, the National Heart, Lung, and Blood Institute (NHLBI) who sponsored previous pediatric BP guidelines announced that NHLBI would no longer sponsor development of new clinical guidelines.3 Subsequently, in 2014 the American Academy of Pediatrics (AAP) agreed to sponsor development of a new pediatric BP clinical practice guideline (CPG). The new CPG for screening and management of high BP in children and adolescents was recently published in Pediatrics.4 This CPG was developed using the rigorous evidence-based approach recommended by the Institute of Medicine in 2011.5 This methodology was consistent with recent NHLBI recommendations on development of CPGs for cardiovascular disease.3 The new pediatric hypertension CPG contains several modifications from the previous guideline to guide clinicians in diagnosis and management of elevated BP and hypertension in children and adolescents. This summary describes those changes made since the 2004 Fourth Report

A comparison of clinical paediatric murmur assessment with echocardiography

Objective: To compare the clinical acumen of paediatric cardiovascular examination between various hospital paediatrician grades. Design: Prospective data collection of clinical and echocardiography findings on paediatric echocardiography referrals. Setting and patients: All paediatric patients (birth - 14 years) referred for echocardiography, in a regional hospital catering for the island population of Malta. Echocardiography was carried out by three paediatricians with tertiary training in this technique. Main outcome measures: Pre-echocardiography clinical diagnoses were compared with echocardiography results according to grade of referring hospital doctor (ranging from houseman to consultant). Both normal and abnormal hearts at echocardiography were included. Results: Echocardiographers had the highest clinical accuracy and the highest attempts at reaching a clinical diagnosis. Accuracy and attempts at diagnosis decreased as doctor's hospital grade decreased, from consultant to houseman. Ventricular septal defect was the most easily diagnosed lesion. Atrial septal defect was often misdiagnosed as pulmonary stenosis.peer-reviewe

Computer-assisted auscultation as a screening tool for cardiovascular disease : a cross-sectional study

Includes synopsis.Includes bibliographical references.Cardiac auscultation is inherently qualitative, highly subjective and requires considerable skill and experience. Computer- assisted auscultation (CAA) is an objective referral-decision support tool that aims to minimise inappropriate referrals. This study evaluated the sensitivity and specificity of 2 CAA systems, Cardioscan® and Sensi®, in detecting echo-confirmed cardiac abnormalities in 79 consecutive patients referred for assessment to a tertiary cardiac clinic. CAA demonstrated suboptimal sensitivity and specificity in detecting cardiac abnormalities in children and adults. As both systems demonstrate 100% sensitivity in detecting acyanotic heart disease, and theoretically carry significant potential in resource-limited settings, further development of current technologies to improve sensitivity and specificity for clinical applications is still warranted

Current trends and perspectives for automated screening of cardiac murmurs

Although in high income countries rheumatic heart disease is now rare, it remains a major burden in low and middle income countries. In these world areas, physicians and expert sonographers are rare, and screening campaigns are usually performed by nomadic caregivers who can only recognise patients in an advanced phase of heart failure with high economic and social costs. Therefore, great interest exists regarding the possibility of developing a simple, low-cost procedure for screening valvular heart disease. With the development of computer science, the cardiac sound signal can be analysed in an automatic way. More precisely, a panel of features characterising the acoustic signal are extracted and sent to a decision-making software able to provide the final diagnosis. Although no system is currently available in the market, the rapid evolution of these technologies recently led to the activation of clinical trials. The aim of this note is to review the state of advancement of this technology (trends in feature selection and automatic diagnostic strategies), data available regarding performance of the technology in the clinical setting and finally what obstacles still need to be overcome before automated systems can be clinically/commercially viable

Narrative review of the role of artificial intelligence to improve aortic valve disease management

Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery

An open access database for the evaluation of heart sound algorithms

This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/0967-3334/37/12/2181In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.This work was supported by the National Institutes of Health (NIH) grant R01-EB001659 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and R01GM104987 from the National Institute of General Medical Sciences.Liu, C.; Springer, DC.; Li, Q.; Moody, B.; Abad Juan, RC.; Li, Q.; Moody, B.... (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement. 37(12):2181-2213. doi:10.1088/0967-3334/37/12/2181S21812213371

Phono-spectrographic analysis of heart murmur in children

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

DIGITAL ANALYSIS OF CARDIAC ACOUSTIC SIGNALS IN CHILDREN

DIGITAL ANALYSIS OF CARDIAC ACOUSTIC SIGNALS IN CHILDREN Milad El-Segaier, MD Division of Paediatric Cardiology, Department of Paediatrics, Lund University Hospital, Lund, Sweden SUMMARY Despite tremendous development in cardiac imaging, use of the stethoscope and cardiac auscultation remains the primary diagnostic tool in evaluation of cardiac pathology. With the advent of miniaturized and powerful technology for data acquisition, display and digital signal processing, the possibilities for detecting cardiac pathology by signal analysis have increased. The objective of this study was to develop a simple, cost-effective diagnostic tool for analysis of cardiac acoustic signals. Heart sounds and murmurs were recorded in 360 children with a single-channel device and in 15 children with a multiple-channel device. Time intervals between acoustic signals were measured. Short-time Fourier transform (STFT) analysis was used to present the acoustic signals to a digital algorithm for detection of heart sounds, define systole and diastole and analyse the spectrum of a cardiac murmur. A statistical model for distinguishing physiological murmurs from pathological findings was developed using logistic regression analysis. The receiver operating characteristic (ROC) curve was used to evaluate the discriminating ability of the developed model. The sensitivities and specificities of the model were calculated at different cut-off points. Signal deconvolution using blind source separation (BSS) analysis was performed for separation of signals from different sources. The first and second heart sounds (S1 and S2) were detected with high accuracy (100% for the S1 and 97% for the S2) independently of heart rates and presence of a murmur. The systole and diastole were defined, but only systolic murmur was analysed in this work. The developed statistical model showed excellent prediction ability (area under the curve, AUC = 0.995) in distinguishing a physiological murmur from a pathological one with high sensitivity and specificity (98%). In further analyses deconvolution of the signals was successfully performed using blind separation analysis. This yielded two spatially independent sources, heart sounds (S1 and S2) in one component, and a murmur in another. The study supports the view that a cost-effective diagnostic device would be useful in primary health care. It would diminish the need for referring children with cardiac murmur to cardiac specialists and the load on the health care system. Likewise, it would help to minimize the psychological stress experienced by the children and their parents at an early stage of the medical care

FROM HEART MURMUR TO ECHOCARDIOGRAPHY CONGENITAL HEART DEFECTS DIAGNOSTICS USING MACHINELEARNING ALGORITHMS

The most common clinical sign in pediatric cardiology is heart murmur, which can often be uncharacteristic. The aim of this research was to present the results of development of a classifier based on machine learning algorithms whose purpose is to classify organic murmur that occur in congenital heart defect (CHD). The study is based on the data collected at Pediatric Clinic, Clinical Center University of Sarajevo during three-year period. Totally, 116 children aged from 1 to 15 years were enrolled in the study. Input parameters for classification are parameters obtained during basic physical examination and assessment of patient. First, analysis of relevance of the feature for classification was done using InfoGain, GainRatio, Relief and Correlation method. In the second step, classifiers based on Naive Bayes, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine were developed and compared by performance. The results of this research suggest that high accuracy (>90%) classifier for detection of CHD based on 16 parameters can be developed. Such classifier with appropriate user interface would be valuable diagnostic aid to doctors and pediatricians at primary healthcare level for diagnostic of heart murmurs

FROM HEART MURMUR TO ECHOCARDIOGRAPHY CONGENITAL HEART DEFECTS DIAGNOSTICS USING MACHINELEARNING ALGORITHMS

The most common clinical sign in pediatric cardiology is heart murmur, which can often be uncharacteristic. The aim of this research was to present the results of development of a classifier based on machine learning algorithms whose purpose is to classify organic murmur that occur in congenital heart defect (CHD). The study is based on the data collected at Pediatric Clinic, Clinical Center University of Sarajevo during three-year period. Totally, 116 children aged from 1 to 15 years were enrolled in the study. Input parameters for classification are parameters obtained during basic physical examination and assessment of patient. First, analysis of relevance of the feature for classification was done using InfoGain, GainRatio, Relief and Correlation method. In the second step, classifiers based on Naive Bayes, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine were developed and compared by performance. The results of this research suggest that high accuracy (>90%) classifier for detection of CHD based on 16 parameters can be developed. Such classifier with appropriate user interface would be valuable diagnostic aid to doctors and pediatricians at primary healthcare level for diagnostic of heart murmurs