Poisoning emergency visits among children: a 3-year retrospective study in Qatar

Background Poisoning in toddlers and infants is almost always unintentional due to their exploratory behavior, which is different from adults. The prevalence and background of childhood poisoning in Qatar is still unknown. The aim of this study is to explore the extent of childhood poisoning in Qatar and, specifically, to describe the frequency of poisoning as a cause of Accident & Emergency (A&E) admission, the demographic profile of affected patients, the circumstances leading to exposure, and the specific agents involved in poisoning among children under age 14 in our setting. Methods This study was a cross-sectional survey of children up to 14 years old utilizing retrospective data between October 2009 and October 2012. The data were collected from the childhood poisoning case registry and patient medical records at the Accident and Emergency (A&E) Unit of all the Hamad Medical Corporation hospitals. Pharmacists reviewed all the handwritten medical records. Data written on the data collection form were transferred into excel and later into SPSS version 21. The data were analyzed using frequencies and percentages, and a chi-square test was used for categorical variables. Results Out of 1179 registered poisoning cases listed in the registry, only 794 cases (67.3 %) were usable and included in the final analysis. A&E admissions for unintentional poisoning for children accounted for 0.22 % of all A&E admissions from 2009 to 12. The majority of poisoning cases happened among children between 1 and 5 years old (n = 704, 59.7 %). Cases were more frequent among non-Qatari than Qatari children (39.4 % vs. 28.5 %). Most cases occurred in the living room (28.2 %) and typically took place in the afternoon (29.2 %). Analgesic and antipyretic medicines were the most common agents ingested by children (n = 194, 36.9 %), specifically paracetamol (n = 140, 26.6 %). Conclusions Cases of unintentional poisoning are higher among children aged 1 to 5 years, males and non-Qatari. Most cases occurred in the living room and typically took place in the afternoon. The most common type of poison ingested by children was medicines, i.e., analgesics and antipyretics, specifically paracetamol

Vulnerable newborn types: Analysis of population-based registries for 165 million births in 23 countries, 2000-2021.

OBJECTIVE: To examine the prevalence of novel newborn types among 165 million live births in 23 countries from 2000 to 2021. DESIGN: Population-based, multi-country analysis. SETTING: National data systems in 23 middle- and high-income countries. POPULATION: Liveborn infants. METHODS: Country teams with high-quality data were invited to be part of the Vulnerable Newborn Measurement Collaboration. We classified live births by six newborn types based on gestational age information (preterm 90th centile) for gestational age, according to INTERGROWTH-21st standards. We considered small newborn types of any combination of preterm or SGA, and term + LGA was considered large. Time trends were analysed using 3-year moving averages for small and large types. MAIN OUTCOME MEASURES: Prevalence of six newborn types. RESULTS: We analysed 165 017 419 live births and the median prevalence of small types was 11.7% - highest in Malaysia (26%) and Qatar (15.7%). Overall, 18.1% of newborns were large (term + LGA) and was highest in Estonia 28.8% and Denmark 25.9%. Time trends of small and large infants were relatively stable in most countries. CONCLUSIONS: The distribution of newborn types varies across the 23 middle- and high-income countries. Small newborn types were highest in west Asian countries and large types were highest in Europe. To better understand the global patterns of these novel newborn types, more information is needed, especially from low- and middle-income countries

A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices

Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems. 2022 Elsevier LtdThis work was supported by the Qatar National Research Grant: UREP28-144-3-046 . The statements made herein are solely the responsibility of the authors.Scopu

Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction

Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images. We present a full end-to-end framework for segmenting, measuring, and estimating fetal gestational age and weight based on two-dimensional ultrasound images of the fetal head. Our segmentation framework is based on the following components: (i) eight segmentation architectures (UNet, UNet Plus, Attention UNet, UNet 3+, TransUNet, FPN, LinkNet, and Deeplabv3) were fine-tuned using lightweight network EffientNetB0, and (ii) a weighted voting method for building an optimized ensemble transfer learning model (ETLM). On top of that, ETLM was used to segment the fetal head and to perform analytic and accurate measurements of circumference and seven other values of the fetal head, which we incorporated into a multiple regression model for predicting the week of gestational age and the estimated fetal weight (EFW). We finally validated the regression model by comparing our result with expert physician and longitudinal references. We evaluated the performance of our framework on the public domain dataset HC18: we obtained 98.53% mean intersection over union (mIoU) as the segmentation accuracy, overcoming the state-of-the-art methods; as measurement accuracy, we obtained a 1.87 mm mean absolute difference (MAD). Finally we obtained a 0.03% mean square error (MSE) in predicting the week of gestational age and 0.05% MSE in predicting EFW

Pediatric Traumatic Brain Injury: a 5-year descriptive study from the National Trauma Center in Qatar

Abstract Background The epidemiologic characteristics and outcomes of pediatric traumatic brain injury (pTBI) have not been adequately documented from the rapidly developing countries in the Arab Middle East. We aimed to describe the hospital-based epidemiologic characteristics, injury mechanisms, clinical presentation, and outcomes of pTBI and analyze key characteristics and determinant of pTBI that could help to make recommendations for policies to improve their care. Methods We conducted a retrospective observational study in a level 1 trauma center (2010–2014) for all pTBI patients. Data were analyzed and compared according to different patient age groups. Results Out of 945 traumatic brain injury patients, 167 (17.7%) were ≤ 18 years old with a mean age of 10.6 ± 5.9 and 81% were males. The rate of pTBI varied from 5 to 14 cases per 100,000 children per year. The most affected group was teenagers (15–18 years; 40%) followed by infants/toddlers (≤ 4 years; 23%). Motor vehicle crash (MVC; 47.3%) was the most frequent mechanism of injury followed by falls (21.6%). MVC accounted for a high proportion of pTBI among teenagers (77.3%) and adolescents (10–14 years; 48.3%). Fall was a common cause of pTBI for infants/toddlers (51.3%) and 5–9 years old group (30.3%). The proportion of brain contusion was significantly higher in adolescents (61.5%) and teenagers (58.6%). Teenagers had higher mean Injury Severity Scoring of 24.2 ± 9.8 and lower median (range) Glasgow Coma Scale of 3 (3–15) (P = 0.001 for all). The median ventilatory days and intensive care unit and hospital length of stay were significantly prolonged in the teenage group. Also, pTBI in teenage group showed higher association with pneumonia (46.4%) and sepsis (17.3%) than other age groups (P = 0.01). The overall mortality rate was 13% (n = 22); 11 died within the first 24 h, 7 died between the second and seventh day and 4 died one week post-admission. Among MVC victims, a decreasing trend of case fatality rate (CFR) was observed with age; teenagers had the highest CFR (85.7) followed by adolescents (75.0), young children (33.3), and infants/toddlers (12.5). Conclusions This local experience to describe the burden of pTBI could be a basis to adopt and form an efficient, tailored strategy for safety in the pediatric population

PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem. 2022 Elsevier LtdThis work was supported by the Qatar National Research Grant: UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.Scopu

Satisfaction with a 2-day communication skills course culturally tailored for medical specialists in Qatar

OBJECTIVE: Health-care communication skills training may be particularly needed in the Arabian Gulf countries because of the variety of cultures within the physician and patient populations. This study describes the implementation and results of a communication skills training program for physicians in Qatar that assessed previous training, and effect of previous training on participants' course evaluations. MATERIALS AND METHODS: We conducted a 2-day communication skills training course covering seven culturally adapted modules. Educational strategies included large and small group work with the standardized patient, demonstration videos, and lectures. At the end, participants completed a course evaluation survey. Data analysis performed with SPSS; frequencies and percentages were calculated, and Chi-square test applied to evaluate statistical significance. RESULTS: A total of 410 physicians in Qatar have participated in the course over a period of 2 years. Evaluation ratings of the course were high. Participants rated the module on Breaking Bad News as the most useful, and the small group role-play as the most helpful course component. One-third of participants had previously participated in experiential communication skills training. There was no association between previous experience and evaluation of the course. CONCLUSION: Physicians in Qatar positively evaluated a 2-day communication skills course, though the majority of participants did not have any previous exposure to experiential communication skills training