Incidence of diabetic ketoacidosis during COVID-19 pandemic: a meta-analysis of 124,597 children with diabetes.

BACKGROUND: Diabetic ketoacidosis (DKA) is a potentially life-threatening complication of type 1 diabetes mellitus (T1DM) that has increased during the COVID-19 pandemic. This study will not only shed light on such life-threatening complications but also be a step to increase the awareness of healthcare providers about such complications in the upcoming pandemic waves and increased dependence on telemedicine. Thus, we aimed to further investigate the increase of DKA in pediatrics. METHODS: PubMed, Web of Science, and Scopus were broadly searched for studies assessing the incidence of DKA in pediatrics during the COVID-19 pandemic. RESULTS: Our study included 24 papers with a total of 124,597 children with diabetes. A statistically significant increase occurred in the risk of DKA among newly diagnosed T1DM patients during the pandemic (RR 1.41; 95% CI 1.19, 1.67; p < 0.01; I2 = 86%), especially in the severe form of DKA (RR 1.66: 95% CI 1.3, 2.11) when compared to before. CONCLUSION: DKA in newly diagnosed children with T1DM has increased during the pandemic and presented with a severe form. This may reflect that COVID-19 may have contributed not only to the development but also the severity of DKA. IMPACT: Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 diabetes mellitus (T1DM) that has increased during the COVID-19 pandemic. Our study included 25 papers with a total of 124,597 children with diabetes. A statistically significant increase occurred in the risk of DKA among newly diagnosed T1DM patients during the pandemic. Our findings reflect that COVID-19 may have an altered presentation in T1DM and can be related to DKA severity.

Development of a Discharge Criteria Checklist for COVID-19 Patients From the Intensive Care Unit.

This study aims to develop and validate a checklist of discharge readiness criteria for COVID-19 patients from the intensive care unit (ICU). We conducted a Delphi design study. The degree of agreement among 7 experts had been evaluated using the content validity index (CVI) through a 4-point Likert scale. The instrument was validated with 17 items. All the experts rated all items as very relevant which scored the item-CVI 1, which validates all checklist items. Using the mean of all items, the scale-CVI was calculated, and it was 1. This meant validation of the checklist as a whole. With regard to the overall checklist evaluation, the mean expert proportion of the instrument was 1, and the S-CVI/UA was 1. This discharge criteria checklist improves transition of care for COVID-19 patients and can help nurses, doctors, and academics to discharge COVID-19 patients from the ICU safely.

Development and Validation of a Red Flag Prediction Model for Admission of COVID-19 Patients to the Intensive Care Unit.

We aimed to develop and validate a model for the criteria for admission of COVID-19 patients to the intensive care unit (ICU). A Delphi design study was conducted. The content validity index (CVI) was used to determine the degree of agreement among the experts to validate the content of the admission criteria tool. Eleven experts determined the validity. The evaluation was conducted using a 4-point rating scale. The accepted CVI value was 0.50 and more. The model was validated with 31 items in the 5 dimensions, with the item-CVI of 1, a face validity index of 1, and a scale-level content validity index (S-CVI) value of 1. We have developed and validated a red flag prediction model for ICU admission of COVID-19 patients. The accurate implementation of this model could improve the outcomes of those patients and possibly decrease mortality.