MATERIALS AND METHODS: This retrospective cross-sectional study involved children aged 6 months to 6 years who were hospitalised for febrile seizures in Hospital Tuanku Ja'afar Seremban (HTJS) from January 2021 to June 2022. Descriptive statistics were used to summarise the differences in demographics and clinical presentations. Logistic regression analyses were performed to identify factors associated with COVID-19 and non-COVID-19 febrile seizures.
RESULTS: Of the 345 patients (median age 22 months, IQR 15- 32; 59.7% were males) included in the study, 130 (37.7%) tested positive for COVID-19, while 215 (62.3%) tested negative. There were no significant differences between both groups based on age, comorbidities, history of febrile seizures, seizure types, temperature on arrival, cough and rhinorrhoea. Multivariate analysis revealed that a family history of febrile seizures and leucocytosis were associated with increased odds of non-COVID-19 febrile seizures. In contrast, lymphopenia was associated with decreased odds.
CONCLUSION: The clinical presentation of COVID-19 and non- COVID-19 febrile seizures are remarkably similar, highlighting the importance of including COVID-19 screening in febrile seizures workup. Full blood count readings may be potentially useful for differentiating between these conditions.
METHODS: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively.
CONCLUSION: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.