ABSTRACT
INTRODUCTION: Lack of infrastructure drives a large unmet need in children's surgery in low- and middle-income countries (LMIC). This study examines the impact of upgrading existing pediatric operating rooms (ORs) on surgical volume in a hospital in Ouagadougou, Burkina Faso. METHOD(S): A nongovernmental organization upgraded 3 ORs in September 2019. Surgical volume before and after the upgrade were compared from January 2019 to January 2022 using interrupted time series analysis of a prospective data collection tool. Data from April to May 2020 were omitted owing to COVID-related lockdowns. RESULT(S): After the upgrade, patients were younger (5 vs 3.8 years, p < 0.001) and had lower rate of postoperative sepsis (2.4% vs 0.5%, p < 0.001) and surgical site infection (4.3% vs 2.2%;p = 0.005). Volume increased by 36 cases per month (p = 0.005) from a baseline of 161 cases per month. There was a positive trend of 1 case per month after upgrade and a significant difference between pre- and post- trends in volume (pre-upgrade -8 cases per month vs after upgrade of +1 case per month;p = 0.002). The mortality rate fell from 20% to 4% (p = 0.003) in the month after the upgrade, with a significant difference between pre- and postupgrade mortality rate trends (6.2% vs -1%, p < 0.001). CONCLUSION(S): There was significant improvement in volume and surgical outcomes after the OR upgrade This study supports the investment in surgical infrastructure to strengthen capacity in LMIC. Future work should include risk-adjusted mortality.
ABSTRACT
Heart rhythm problems, more commonly known as heart arrhythmias, is the phenomenon in which heartbeats don't work properly due to electrical impulses. This causes the heart to irregularly, sometimes too slow, or too fast, depending on the condition. Fluttering and racing heart are the most common arrhythmia symptoms, which is most of the time harmless. Sometimes heart arrhythmias can even be life-threatening and may manifest several alarming signs and symptoms. This paper is about the acquisition and analysis of heart activity. Using the AD8232 module, the heart's electrical activity is captured using the principles of Electrocardiography (ECG). For the acoustic activity of the heart, a stethoscope and an electret microphone are used to convert the acoustic energy to electrical energy. The signal from each practice is passed through an ADC to translate the signal to a digital signal to allow further operation. Upon acquiring the data, it is then analyzed whether the subject has arrhythmia, murmur, or is normal using a Deep Learning algorithm. The said algorithm is provided using a Convolutional Neural Network (ConvNet/CNN). Remote communities where medical assistance is scarce will benefit from this research as it can be operated with no medical experience. The study was able to successfully acquire ECGs and PCGs and analyze the heart condition of the data source. The researchers successfully integrated preprocessing techniques to better analyze the gathered data from human subjects. Lastly, the tuned CNN model correctly classified human subjects based on 4 classes which are normal, abnormal, others, and noisy. The classification accuracy shows that 80% of the 20 subjects were correctly classified based on their current medical condition. The performance sensitivity of the study is 100%, while performance specificity is 77.78%. The detection of error rate is at 20%. All values for conformance testing were deemed acceptable considering the testing restrictions due to the ongoing COVID-19 pandemic. © 2021 IEEE.