Impact of pre-eclampsia on the cardiovascular health of the offspring: a cohort study protocol

Introduction: Pre-eclampsia is a common disorder associated with serious maternal and fetal complications. It is associated with abnormal placentation, which significantly reduces flow, resulting in a relative hypoxic state. These pathophysiological changes lead to subtle macrovascular and cardiac structural and functional changes in the fetus. This can predispose the child with maternal history of pre-eclampsia to risk of premature cardiovascular disease.Methods and analysis: The children will be identified from a cohort of women with pre-eclampsia. The study will be conducted at The Aga Khan University Hospital, Karachi. Inclusion criteria will be children who are between 2 and 5 years of age and have a maternal history of pre-eclampsia. The child’s current weight, height and blood pressure will be recorded. A two-dimensional functional echocardiogram and vascular assessment will be performed to evaluate alterations in cardiac function as well as macrovascular remodelling in these children. Data will be presented as mean±SD, median (IQR) or percentages as appropriate. Independent t-test or Mann-Whitney U test will be used for testing of continuous variables (based on the assumption of normality). A p\u3c0.05will be used to determine statistical significance.Ethics and dissemination: Ethical approval has been obtained from AKUH Ethics Review Committee. Findings will be disseminated through scientific publications and project summaries for the participants

Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: A study protocol

Background: In Pakistan, stillbirth rates and early neonatal mortality rates are amongst the highest in the world. The aim of this study is to provide proof of concept for using a computational model of fetal haemodynamics, combined with machine learning. This model will be based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows with the goal to identify those fetuses at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities.Methods: This will be prospective one group cohort study which will be conducted in Ibrahim Hyderi, a peri-urban settlement in south east of Karachi. The eligibility criteria include pregnant women between 22-34 weeks who reside in the study area. Once enrolled, in addition to the performing fetal ultrasound to obtain Dopplers, data on socio-demographic, maternal anthropometry, haemoglobin and cardiotocography will be obtained on the pregnant women.Discussion: The machine learning approach for predicting adverse perinatal outcomes obtained from the current study will be validated in a larger population at the next stage. The data will allow for early interventions to improve perinatal outcomes

Use of machine learning algorithms for prediction of fetal risk using cardiotocographic data

Background: A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor.Aim: The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses.Methods: CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified.Results: Determined by obstetricians\u27 interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (\u3e96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (\u3e92%).Conclusion: The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health-care workers in low- and middle-income countries can use this model to triage pregnant women in remote areas for early referral and further management

Neurodevelopment assessment of small for gestational age children in a community-based cohort from Pakistan

Background: Children born small for gestational age (SGA) may experience more long-term neurodevelopmental issues than those born appropriate for gestational age (AGA). This study aimed to assess differences in the neurodevelopment of children born SGA or AGA within a periurban community in Pakistan.Methods: This was a prospective cohort study in which study participants were followed from the pilot Doppler cohort study conducted in 2018. This pilot study aimed to develop a pregnancy risk stratification model using machine learning on fetal Dopplers. This project identified 119 newborns who were born SGA (2.4±0.4 kg) based on International Fetal and Newborn Growth Consortium standards. We assessed 180 children (90 SGA and 90 AGA) between 2 and 4 years of age (76% of follow-up rate) using the Malawi Developmental Assessment Tool (MDAT).Findings: Multivariable linear regression analysis comparing the absolute scores of MDAT showed significantly lower fine motor scores (β: -0.98; 95% CI -1.90 to -0.06) among SGAs, whereas comparing the z-scores using multivariable logistic regression, SGA children had three times higher odds of overall z-scores ≤-2 (OR: 3.78; 95% CI 1.20 to 11.89) as compared with AGA children.Interpretation: SGA exposure is associated with poor performance on overall MDAT, mainly due to changes in the fine motor domain in young children. The scores on the other domains (gross motor, language and social) were also lower among SGAs; however, none of these reached statistical significance. There is a need to design follow-up studies to assess the impact of SGA on child\u27s neurodevelopmental trajectory and school performance

Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: A study protocol

Introduction: Rheumatic heart diseases (RHDs) contribute significant morbidity and mortality globally. To reduce the burden of RHD, timely initiation of secondary prophylaxis is important. The objectives of this study are to determine the frequency of subclinical RHD and to train a deep learning (DL) algorithm using waveform data from the digital auscultatory stethoscope (DAS) in predicting subclinical RHD.Methods and analysis: We aim to recruit 1700 children from a group of schools serving the underprivileged over a 12-month period in Karachi (Pakistan). All consenting students within the age of 5-15 years with no underlying congenital heart disease will be eligible for the study. We will gather information regarding sociodemographics, anthropometric data, history of symptoms or diagnosis of rheumatic fever, phonocardiogram (PCG) and electrocardiography (ECG) data obtained from DAS. Handheld echocardiogram will be performed on each study participant to assess the presence of a mitral regurgitation (MR) jet (\u3e1.5 cm), or the presence of aortic regurgitation (AR) in any view. If any of these findings are present, a confirmatory standard echocardiogram using the World Heart Federation (WHF) will be performed to confirm the diagnosis of subclinical RHD. The auscultatory data from digital stethoscope will be used to train the deep neural network for the automatic identification of patients with subclinical RHD. The proposed neural network will be trained in a supervised manner using labels from standard echocardiogram of the participants. Once trained, the neural network will be able to automatically classify the DAS data in one of the three major categories-patient with definite RHD, patient with borderline RHD and normal subject. The significance of the results will be confirmed by standard statistical methods for hypothesis testing.Ethics and dissemination: Ethics approval has been taken from the Aga Khan University, Pakistan. Findings will be disseminated through scientific publications and to collaborators.Article focus: This study focuses on determining the frequency of subclinical RHD in school-going children in Karachi, Pakistan and developing a DL algorithm to screen for this condition using a digital stethoscope

Trends in Gaps of Care for Patients With Congenital Heart Disease: Implications for Social Determinants of Health and Child Opportunity Index

BACKGROUND: Lifelong continuity of care is essential for patients with congenital heart disease (CHD) to maximize health outcomes; unfortunately, gaps in care (GIC) are common. Trends in GIC and of social determinants of health factors contributing to GIC are poorly understood. METHODS AND RESULTS: This retrospective cohort study included patients with CHD, aged 0 to 34 years, who underwent surgery between January 2003 and May 2020, followed up at a pediatric subspeciality hospital. Patients were categorized as having simple, moderate, and complex CHD based on 2018 American Heart Association and American College of Cardiology guidelines. Social determinants of health, such as race, ethnicity, language, insurance status, and Child Opportunity Index, based on home address zip code, were analyzed. Of 2012 patients with CHD, a GIC of ≥3 years was identified in 56% (n=1119). The proportion of patients with GIC per year increased by 0.51% (P\u3c0.001). Multivariable longitudinal models showed that the odds of GIC were higher for patients who were ≥10.5 years old, had simple CHD, lived out of state, lived farther from care site, received public insurance, had less protection with additional insurance plans, and with low Child Opportunity Index. A separate model for patients with only moderate/complex CHD showed similar findings. Race and ethnicity were not associated with the odds of experiencing GIC over time. CONCLUSIONS: GIC have increased over time for patients with CHD. Social determinants of health, like insurance, access, and neighborhood opportunity, are key risk factors for increasing GIC. Efforts to reduce GIC in patients with CHD should focus on addressing the impact of specific social determinants of health

Optimizing patient care and outcomes through the congenital heart center of the 21st century

Pediatric cardiovascular services are responding to the dynamic changes in the medical environment, including the business of medicine. The opportunity to advance our pediatric cardiology field through collaboration is now realized, permitting us to define meaningful quality metrics and establish national benchmarks through multicenter efforts. In March 2016, the American College of Cardiology hosted the first Adult Congenital/Pediatric Cardiology Section Congenital Heart Community Day. This was an open participation meeting for clinicians, administrators, patients/parents to propose metrics that optimize patient care and outcomes for a stateâ ofâ theâ art congenital heart center of the 21st century. Care center collaboration helps overcome the barrier of relative small volumes at any given program. Patients and families have become active collaborative partners with care centers in the definition of acute and longitudinal outcomes and our quality metrics. Understanding programmatic metrics that create an environment to provide outstanding congenital heart care will allow centers to improve their structure, processes and ultimately outcomes, leading to an increasing number of centers that provide excellent care. This manuscript provides background, as well listing of proposed specialty domain quality metrics for centers, and thus serves as an updated baseline for the ongoing dynamic process of optimizing care and realizing patient value.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143653/1/chd12575_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143653/2/chd12575.pd

mRNA Coronavirus Disease 2019 Vaccine-Associated Myopericarditis in Adolescents: A Survey Study

In this survey study of institutions across the US, marked variability in evaluation, treatment, and follow-up of adolescents 12 through 18 years of age with mRNA coronavirus disease 2019 (COVID-19) vaccine-associated myopericarditis was noted. Only one adolescent with life-threatening complications was reported, with no deaths at any of the participating institutions

International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors

BACKGROUND: Sinonasal neoplasms, whether benign and malignant, pose a significant challenge to clinicians and represent a model area for multidisciplinary collaboration in order to optimize patient care. The International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors (ICSNT) aims to summarize the best available evidence and presents 48 thematic and histopathology-based topics spanning the field. METHODS: In accordance with prior International Consensus Statement on Allergy and Rhinology documents, ICSNT assigned each topic as an Evidence-Based Review with Recommendations, Evidence-Based Review, and Literature Review based on the level of evidence. An international group of multidisciplinary author teams were assembled for the topic reviews using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses format, and completed sections underwent a thorough and iterative consensus-building process. The final document underwent rigorous synthesis and review prior to publication. RESULTS: The ICSNT document consists of four major sections: general principles, benign neoplasms and lesions, malignant neoplasms, and quality of life and surveillance. It covers 48 conceptual and/or histopathology-based topics relevant to sinonasal neoplasms and masses. Topics with a high level of evidence provided specific recommendations, while other areas summarized the current state of evidence. A final section highlights research opportunities and future directions, contributing to advancing knowledge and community intervention. CONCLUSION: As an embodiment of the multidisciplinary and collaborative model of care in sinonasal neoplasms and masses, ICSNT was designed as a comprehensive, international, and multidisciplinary collaborative endeavor. Its primary objective is to summarize the existing evidence in the field of sinonasal neoplasms and masses

Advocacy at the Eighth World Congress of Pediatric Cardiology and Cardiac Surgery

The Eighth World Congress of Pediatric Cardiology and Cardiac Surgery (WCPCCS) will be held in Washington DC, USA, from Saturday, 26 August, 2023 to Friday, 1 September, 2023, inclusive. The Eighth World Congress of Pediatric Cardiology and Cardiac Surgery will be the largest and most comprehensive scientific meeting dedicated to paediatric and congenital cardiac care ever held. At the time of the writing of this manuscript, The Eighth World Congress of Pediatric Cardiology and Cardiac Surgery has 5,037 registered attendees (and rising) from 117 countries, a truly diverse and international faculty of over 925 individuals from 89 countries, over 2,000 individual abstracts and poster presenters from 101 countries, and a Best Abstract Competition featuring 153 oral abstracts from 34 countries. For information about the Eighth World Congress of Pediatric Cardiology and Cardiac Surgery, please visit the following website: [www.WCPCCS2023.org]. The purpose of this manuscript is to review the activities related to global health and advocacy that will occur at the Eighth World Congress of Pediatric Cardiology and Cardiac Surgery. Acknowledging the need for urgent change, we wanted to take the opportunity to bring a common voice to the global community and issue the Washington DC WCPCCS Call to Action on Addressing the Global Burden of Pediatric and Congenital Heart Diseases. A copy of this Washington DC WCPCCS Call to Action is provided in the Appendix of this manuscript. This Washington DC WCPCCS Call to Action is an initiative aimed at increasing awareness of the global burden, promoting the development of sustainable care systems, and improving access to high quality and equitable healthcare for children with heart disease as well as adults with congenital heart disease worldwide