Utility of Epilepsy Surgery in Survivors of Childhood Cancer.

Resection of an epileptogenic focus improves seizure control in patients with drug-resistant epilepsy. There is little data available on usefulness of epilepsy surgery in childhood cancer survivors with drug-resistant epilepsy. To learn about seizure outcome after epilepsy surgery in childhood cancer survivors, we retrospectively reviewed charts of 42 children who were referred to an epilepsy center for surgical evaluation. Sixteen children (38%) were offered epilepsy surgery and 10 consented. Seizure outcome was classified based on International League Against Epilepsy outcome scale. All 10 children were having multiple seizures a month on therapeutic doses of three antiepilepsy drugs (AEDs). At a median follow-up of 5.6 years after epilepsy surgery, three children had class 1 outcome (no seizures), four had class 3 outcome (1-3 seizure days/year), and three had class 4 outcome (≥ 50% reduction in seizure frequency). One child was off AEDs, seven were on a single AED, and two were on three AEDs at their last follow-up. Epilepsy surgery had low morbidity and improved seizure control in childhood cancer survivors with drug-resistant epilepsy. Childhood cancer survivors with drug-resistant epilepsy should be referred to an epilepsy center for a higher level of care.

Assessing Health-Related Quality of Life, Morbidity, and Survival Status for Individuals With Down Syndrome in Pakistan (DS-Pak): Protocol for a Web-Based Collaborative Registry.

BACKGROUND: Down syndrome is the most common chromosomal disorder, with a global incidence of 1 in 700 live births. However, the true prevalence, associated morbidities, and health-related quality of life (HRQOL) of these individuals and their families are not well documented, especially in low- and middle-income countries such as Pakistan. Disease-specific documentation in the form of a collaborative registry is required to better understand this condition and the associated health outcomes. This protocol paper describes the aims and processes for developing the first comprehensive, web-based collaborative registry for Down syndrome in a Pakistani cohort. OBJECTIVE: This study aims to assess the HRQOL, long-term survival, and morbidity of individuals with Down syndrome by using a web-based collaborative registry. METHODS: The registry data collection will be conducted at the Aga Khan University Hospital and at the Karachi Down Syndrome Program. Data will be collected by in-person interviews or virtually via telephone or video interviews. Participants of any age and sex with Down syndrome (trisomy 21) will be recruited. After receiving informed consent and assent, a series of tablet-based questionnaires will be administered. The questionnaires aim to assess the sociodemographic background, clinical status, and HRQOL of the participants and their families. Data will be uploaded to a secure cloud server to allow for real-time access to participant responses by the clinicians to plan prompt interventions. Patient safety and confidentiality will be maintained by using multilayer encryption and unique coded patient identifiers. The collected data will be analyzed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corporation), with the mean and SD of continuous variables being reported. Categorical variables will be analyzed with their percentages being reported and with a P value cutoff of .05. Multivariate regression analysis will be conducted to identify predictors related to the HRQOL in patients with Down syndrome. Survival analysis will be reported using the Kaplan-Meier survival curves. RESULTS: The web-based questionnaire is currently being finalized before the commencement of pilot testing. This project has not received funding at the moment (ethical review committee approval reference ID: 2020-3582-11145). CONCLUSIONS: This registry will allow for a comprehensive understanding of Down syndrome in low- and middle-income countries. This can provide the opportunity for data-informed interventions, which are tailored to the specific needs of this patient population and their families. Although this web-based registry is a proof of concept, it has the potential to be expanded to national, regional, and international levels. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/24901.

Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan.

Universal primary education is critical for individual academic growth and overall adult productivity of nations. Estimates indicate that 25% of 59 million primary age out of school children drop out and early grade failure is one of the factors. An objective and feasible screening measure to identify at-risk children in the early grades can help to design appropriate interventions. The objective of this study was to use a Machine Learning algorithm to evaluate the power of Electroencephalogram (EEG) data collected at age 4 in predicting academic achievement at age 8 among rural children in Pakistan. Demographic and EEG data from 96 children of a cohort along with their academic achievement in grade 1-2 measured using an academic achievement test of Math and language at the age of 7-8 years was used to develop the machine learning algorithm. K- Nearest Neighbor (KNN) classifier was used on different model combinations of EEG, sociodemographic and home environment variables. KNN model was evaluated using 5 Stratified Folds based on the sensitivity and specificity. In the current dataset, 55% and 74% failed in the mathematics and language test respectively. On testing data across each fold, the mean sensitivity and specificity was calculated. Sensitivity was similar when EEG variables were combined with sociodemographic, and home environment (Math = 58.7%, Language = 66.3%) variables but specificity improved (Math = 43.4% to 50.6% and Language = 32% to 60%). The model requires further validation for EEG to be used as a screening measure with adequate sensitivity and specificity to identify children in their preschool age who may be at high risk of failure in early grades.