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

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

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

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. 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 sociodemographic, maternal anthropometry, haemoglobin and cardiotocography will be obtained on the pregnant women. 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