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Objectives Diabetes mellitus intensify the risks and complications related to COVID-19 infection. A major effect of the pandemic has been a drastic reduction of in-person visits. The aim of this study was to evaluate the impact of the COVID-19 pandemic on HbA1c management and results among pediatric and adult outpatients with diabetes, considering the laboratory and point-of-care testing (POCT) HbA1c measurements. Methods Observational retrospective study including patients from pediatric and adult diabetes units was conducted. HbA1c results obtained in the laboratory and POCT over 3 years (2019–2021) were collected from the laboratory information system. Results After the lockdown, the number of HbA1c plummeted. Children returned soon to routine clinical practice. The number of HbA1c increased gradually in adults, especially in POCT. Globally, HbA1c results were lower in children compared with adults (p<0.001). HbA1c values in children (p<0.001) and adults (p=0.002) decreased between pre-pandemic and post-pandemic periods, though lower than the HbA1c reference change value. The percentage of HbA1c results above 8% remained stable during the study period. Conclusions Continuous glucose monitoring and a telemedicine have been crucial, even allowing for improvements in HbA1c results. During the lockdown, patients with better metabolic control were managed in the laboratory whereas patients with poorer control or a severe clinical situation were attended in diabetes units by POCT. Adults returned to pre-pandemic management slowly because they were more susceptible to morbidity and mortality due to COVID-19. Coordination among all health professionals has been essential to offering the best management, especially in difficult scenarios such as the COVID-19 pandemic.

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Purpose To propose adaptive setup protocols using Bayesian statistics that facilitate, based on a prediction of coverage probability, making a decision on which patients should follow daily imaging prior to treatment delivery. Materials and Methods The suitability of the treatment margins was assessed combining interfraction variability measurements of the first days of treatment with previous data gathered from our patient population. From this information, we decided if a patient needs an online imaging protocol to perform daily isocenter correction before each treatment fraction. We applied our method to five different datasets. Protocol parameters were selected from each dataset based on coverage probability, the expected imaging workload of the treatment unit, and the accuracy of patient classification. Time trends were assessed and included in the proposed protocols. To validate the accuracy of the protocols, they were applied to a validation dataset of prostate cancer patients. Results Adaptive setup protocols lead expected population coverage >97% in all datasets analyzed when time trends were considered. The reduction in imaging workload ranged from 40% in lung treatments to 28.5% in prostate treatments. Results of the protocol on the validation dataset were very similar to those previously predicted. Conclusions The adaptive setup protocols based on Bayesian statistics presented in this study enable the optimization of imaging workload in the treatment unit ensuring that appropriate dose coverage remains unchanged.