Background: Seizure control among children with epilepsy (CWE) receiving anti-seizure medications (ASMs) remains a challenge in low-resource settings. Uncontrolled seizures are significantly associated with increased morbidity and mortality among CWE. This negatively impacts their quality of life and increases stigma.Aim: This study determined seizure control status and described the factors associated among CWE receiving ASMs at Mbarara Regional Referral Hospital (MRRH). Methods: In a retrospective chart review study, socio-demographic and clinical data were obtained from 112 medical records. CWE receiving ASMs for at least six months and regularly attending the clinic were included in the study. Physical or telephone interviews were conducted with the immediate caregivers of the CWE to establish the current seizure control status of the participants. Results: A total of 112 participants were enrolled. Of these, three-quarters had generalized onset seizures, 23% had focal onset seizures, while 2% had unknown onset motor seizures. About 60.4% of the study participants had poor seizure control. Having a comorbidity (p-value 0.048, AOR 3.2 (95% CI 1.0-9.9)), history suggestive of birth asphyxia (p-value 0.014, AOR 17.8 (95% CI 1.8-176.8)), and being an adolescent (p-value 0.006, AOR 6.8 (95% CI 1.8-26.6)) were significantly associated with poor seizure control. Conclusion: Seizure control among CWE receiving ASMs at MRRH remains poor. Efforts geared to addressing seizure control and optimizing drugs are needed, especially among children with comorbidities, those with history of birth asphyxia, and adolescents.

Background Leptospirosis is an emerging neglected zoonotic disease that presents with nonspecific signs/symptoms and it can be mistaken for other diseases. Owing to limited diagnostic capacity and unawareness, the data on human leptospirosis particularly in neonates are scarce in many sub-Saharan countries. It has been underreported hindering preventive and control measures in place. The study aimed at determining prevalence of leptospirosis as a cause of febrile illness in neonates using IgM ELISA and a quantitative real-time PCR (qPCR). Methods This was a descriptive cross-sectional study that included 103 neonatal sepsis cases whose parents/legal guardians gave informed consent. The data on demographic and clinical characteristics were collected using structured data collection form. EDTA whole blood sample was collected from the neonates by trained study nurses. From the samples, IgM ELISA was done using automated analyzers, DNA extracted and qPCR was performed using primers for LipL32, specific for the pathogenic leptospires. Results The prevalence of anti-leptospiral IgM among the neonates as determined by ELISA was 4.3%, where all of them presented with lethargy and poor feeding. No pathogenic Leptospira species DNA was amplified by qPCR. Conclusions Evidence of leptospirosis was demonstrated in neonatal sepsis cases in this study. The findings suggest considerations of leptospirosis in the differential diagnosis of neonates with sepsis. More data are needed on the real epidemiology, clinical features, and burden of leptospirosis in neonates. There is need to include intermediate pathogenic species of Leptospira in the diagnostic qPCR assays.

BackgroundNeonatal sepsis is a significant cause of neonatal death and has been a major challenge worldwide. The difficulty in early diagnosis of neonatal sepsis leads to delay in treatment. The early diagnosis of neonatal sepsis has been predicted to improve neonatal outcomes. The use of machine learning techniques with the relevant screening parameters provides new ways of understanding neonatal sepsis and having possible solutions to tackle the challenges it presents. This work proposes an algorithm for predicting neonatal sepsis using electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) that can improve the early recognition and treatment of sepsis in neonates.Methods A retrospective analysis was performed on datasets composed of de-identified electronic medical records collected between 2015 to 2019. The dataset contains records of 482 neonates hospitalized in Mbarara Regional Referral Hospital, Uganda. The proposed algorithm implements Support Vector Machine (SVM), Logistic regression (LR), K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision tree (DT) algorithms, which were trained, tested, and compared based on the acquired data. The performance of the proposed algorithm was evaluated by comparing it with the physician's diagnosis. The experiment used a Stratified K-fold cross-validation technique to evaluate the performance of the models. Statistical significance of the experimental results was carried out using the Wilcoxon Signed-Rank Test. ResultsThe results of this study show that the proposed algorithm (with the lowest Sensitivity of 95%, lowest Specificity of 95%) outperformed the physician diagnosis (Sensitivity = 89%, Specificity = 11%). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 98%) performed better than the other models in predicting neonatal sepsis as their results were statistically significant.ConclusionsThe study provides evidence that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests effectively diagnose neonatal sepsis. Based on the study result, the proposed algorithm can help identify neonatal sepsis cases as it exceeded clinicians' sensitivity and specificity. A prospective study is warranted to test the algorithm's clinical utility, which could provide a decision support aid to clinicians.

About 2.9 million neonates die every year worldwide, and most of these deaths occur in low-resource settings. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, a timely diagnosis is critical. The standard gold test for diagnosing neonatal sepsis is blood culture, which takes at least 72 hours. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality. The matching articles were identified by searching the PubMed, IEEE, and Cochrane bibliography databases. For the inclusion of articles, the abstract and titles were first screened based on some predetermined criteria and then, the full-text articles were screened. Thirty-one studies met the full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests. The main limitation is the variation in the performance measures used in the studies, which made it difficult to perform a quantitative assessment. A combination of predictor variables has been shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.

About 2.9 million neonates die every year worldwide, and most of these deaths occur in low-resource settings. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, a timely diagnosis is critical. The standard gold test for diagnosing neonatal sepsis is blood culture, which takes at least 72 hours. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality. The matching articles were identified by searching the PubMed, IEEE, and Cochrane bibliography databases. For the inclusion of articles, the abstract and titles were first screened based on some predetermined criteria and then, the full-text articles were screened. Thirty-one studies met the full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests. The main limitation is the variation in the performance measures used in the studies, which made it difficult to perform a quantitative assessment. A combination of predictor variables has been shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.