Pregnancy outcomes of patients with ultrasound-indicated and history-indicated mcdonald cervical cerclage

Objective: To assess pregnancy outcomes of patients with ultrasound-indicated and history-indicated McDonald cervical cerclage. Materials and methods: This was a cross-sectional study of pregnant women who had cervical cerclage performed at Aga Khan University Hospital, Karimabad campus. We obtained Institutional Review Board approval and reviewed the medical records of patients. A purposive continuous sampling technique was used. A total of 88 patients were included. There were no exclusion criteria. Outcome data were collected from the medical record of patients from January 2010 to December 2016. Results: Analysis reported a statistically significant lower gravidity and parity in the scan-indicated group as compared with other groups (p = 0.000 and p = 0.001, respectively). Previous history of cervical cerclage, history of mid-trimester miscarriage, and preterm labor were significantly associated with indication for cerclage (p = 0.001, 0.046, and 0.001, respectively). Cervical length was also significantly associated with the indication for cerclage (p p = 0.003 and p = 0.04 and 0.004, respectively). Conclusion: Our study showed that patients with a history suggestive of cervical incompetence or short cervix on ultrasound should be offered cerclage to prevent preterm birth and to improve neonatal outcomes. Ultrasound-indicated cerclage after the first trimester indicates that universal cervical-length screening by transvaginal ultrasound in the mid-trimester can pick silent cases, and inserting cerclage can prolong pregnancy to term. Clinical significance: Screening cervical length will help clinicians to reduce preterm birth rate, especially in resource-limited underdeveloped countries

Uterine arteriovenous malformations after suction evacuation of missed miscarriage

Uterine arteriovenous malformation (AVM) is an uncommon but life-threating source of bleeding. AVM is an abnormal connection between uterine arteries and veins. Patients typically present with vaginal bleeding following miscarriage (medical/surgical) or cesarean section. The treatment of choice depends on the symptoms, age, desire of fertility, localization and size of the lesion. Uterine artery embolization is the first choice in symptomatic patients of reproductive age group. We report a case of AVM presenting after dilation and evacuation with extensive lesion, which was successfully treated with bilateral uterine artery embolization

Household chores as the main source of physical activity: Perspectives of pregnant Pakistani women

Objective: To understand the level of physical activity in pregnant women and to identify perceived facilitators and barriers faced by them. Methods: This cross-sectional study was conducted from January to June 2016 at Aga Khan Maternity and Child Care Centre, Hyderabad, Pakistan, and comprised pregnant women attending the antenatal clinics. They were administered the pregnancy physical activity questionnaire while additional questions were asked to assess perceived barriers and facilitators in pregnancy. SPSS 19 was used for data analysis.Results: Of the 455 subjects, 179(36%) were physically active. Their median metabolic equivalent of task hours per week was 14.65 (interquartile range=0-105.8). The overall mean age of subjects was 26±4.47 years, while the mean gestational age was 24±10 weeks. Household activity had a strong positive correlation with total activity (p\u3c0.05). Reported barriers included lack of energy and lack of information regarding benefits of physical activity, and facilitators included support from family and affordable facilities in the area of residence.Conclusion: Majority of the pregnant women failed to meet the daily recommendations for physical activity

Comparison of neonatal outcomes between category-1 and non-category-1 primary emergency cesarean section: A retrospective record review in a tertiary care hospital

Objective: To compare neonatal outcomes between Category-1 and Non-Category-1 Primary Emergency Cesarean Section.Methods: This was a retrospective analysis, conducted at Aga Khan University Hospital Karachi from January 1st 2016 till December 31st 2016. Non-probability purposive sampling technique was used. A sample size of 375 patients who had primary Emergency Caesarean Section (Em-CS) was identified by keeping CS rate of 41.5% and 5% bond on error. Data was collected from labor ward, operating theatre and neonatal ward records by using structured questionnaire.Results: In the current study, out of 375 participants who underwent primary Em-CS; majority (89.3%) were booked cases. Two-hundred-eighty-two (75.2%) were primiparous women. Two hundred and thirty (61.3%) were at term and 145(38.7%) were preterm. The main indication among Category-1 CS was fetal distress (15.7%). For Non-Category-1 CS, non-progress of labour (45.1%) was the leading cause of abdominal delivery. Except for APGAR score at one minute (p value = 0.048), no other variables were statistically significant when neonatal outcomes were compared among Category 1 and Non-Category-1 CS.Conclusion: In this study, fetal distress and non-progress of labor were the main indications for Category-1 and Non-Category-1 CS respectively. We did not find statistically significant association between indications of Em CS and neonatal outcomes. However further prospective studies are required to confirm this association

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

Episiotomy and its complications: A cross sectional study in secondary care hospital

Episiotomy is a commonly performed procedure at the time of vaginal delivery to prevent perineal lacerations. A study was conducted to evaluate the complications of episiotomy. A sample size of 235 patients was taken. The complications were divided in two groups depending on the time of occurrence after delivery. Out of 235 patients, immediate complications were reported in 10(4.3%) patients. These included perineal tears, postpartum haemorrhage, extended episiotomy, perineal pain, inability to pass urine or stool and vaginal haematoma. Early complications including wound infection, gaping wound and resuturing of wound were reported in 21(8.9%) patients. On multivariate analysis, it was seen that age (19-29 years) was significantly associated with complications. Mediolateral episiotomy is a safe obstetrical surgical procedure in order to prevent third and fourth degree perineal tears and is not associated with increased incidence of complications

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

Deep learning applications in neuro-oncology

Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic abilities of DL and physicians severely limit the ability of this technology to be widely adopted. One of the medical tenets is judgment, a facet of medical decision making in DL that is often missing because of its inherent nature as a black box. A natural distrust for newer technology, combined with a lack of autonomy that is normally expected in our current medical practices, is just one of several important limitations in implementation. In our review, we will first define and outline the different types of artificial intelligence (AI) as well as the role of AI in the current advances of clinical medicine. We briefly highlight several of the salient studies using different methods of DL in the realm of neuroradiology and summarize the key findings and challenges faced when using this nascent technology, particularly ethical challenges that could be faced by users of DL

Referral pattern of emergencies in obstetrics: implications for defining scope of services and policy

Objective: To analyse referral pattern of high-risk obstetric cases from secondary to tertiary care hospitals and to assess their maternal and neonatal outcomes. Methods: This retrospective study was conducted at the Aga Khan University Hospital, Karachi, and comprised all referred obstetric cases from secondary-level hospitals to tertiary-level care within and outside the Hospital between January 2011 and December 2014. Day and time of referral, reason for referral as well as maternal and neonatal outcomes were collected. SPSS 19 was used for data analysis. Results: Of the 634 obstetric referrals, 279(44%) patients were referred to the study site, while 355(56%) sought care in other hospitals. Of those patients who were referred to the AKUH, medical records of 195(69.9%) were available for review. The mean age of the participants was 28±4.7 years. Obstetric complications led to 122(61%) referrals. The top three reasons among these were pregnancy-induced hypertension, preterm labour and foetal causes. Medical causes such as viral infections were the cause of 50(27%) referrals. Moreover, 177(91%) patients were pregnant at the time of referral and the remaining 18(9%) were referred after delivery. Of the pregnant women, 133(75%) delivered at the study site. Caesarean section was the mode in 92(69%) deliveries. There was 1(0.75%) maternal death due to puerperal sepsis while 9(7%) neonatal deaths were recorded. Conclusion: The most common reason for referrals was obstetric indications. Moreover, a quarter of referrals were initiated due to medical conditions, most of which were due to infections

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