Paediatric new onset type 1 diabetes and diabetic ketoacidosis during the COVID-19 pandemic in Malaysia.

AIMS: Despite emerging evidence of increased paediatric diabetes mellitus (DM) and diabetic ketoacidosis (DKA) worldwide following the COVID-19 pandemic, studies in Asia are lacking. We aimed to determine the frequency, demographics, and clinical characteristics of new onset type 1 DM (T1DM) during the pandemic in Malaysia. METHODS: This is a retrospective multicenter study involving new onset T1DM paediatric patients in Klang Valley, Malaysia during two time periods ie 18th September 2017-17th March 2020 (pre-pandemic) and 18th March 2020-17th September 2022 (pandemic). RESULTS: There was a total of 180 patients with new onset T1DM during the 5-year study period (71 pre-pandemic, 109 pandemic). An increase in frequency of T1DM was observed during the pandemic (52 in 2021, 38 in 2020, 27 in 2019 and 30 in 2018). A significantly greater proportion of patients presented with DKA (79.8 % vs 64.8 %), especially severe DKA (46.8 % vs 28.2 %) during the pandemic. Serum glucose was significantly higher (28.2 mmol vs 25.9 mmol/L) with lower venous pH (7.10 vs 7.16), but HbA1c was unchanged. CONCLUSIONS: New onset T1DM increased during the pandemic, with a greater proportion having severe DKA. Further studies are required to evaluate the mechanism leading to this rise to guide intervention measures.

Risk factors for disease severity among children with Covid-19: a clinical prediction model.

BACKGROUND: Children account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19. 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.