ABSTRACT
Objective/Background: Hyperglycemia in hospitalized patients with COVID-19 is linked to increased morbidity and mortality. With increasing use of glucocorticoid (GC) therapy for COVID-19 hypoxemia, clinicians are challenged with an increased prevalence of hyperglycemia. Methods: We developed an insulin protocol to aid front line providers in the management of GC-induced hyperglycemia for hospitalized adults with COVID-19. The protocol was based on expert opinion and was available from March 2021 onward. Glycemic and clinical outcomes were obtained for patients treated with GC and retrospectively compared to historical controls treated with GC admitted up to 6 months prior to protocol implementation. Glycemic parameters for finger stick glucose values (FSG) were defined as hypoglycemia <70 mg/dL, euglycemia 70-180 mg/dL, mild hyperglycemia 180-250 mg/dL and severe hyperglycemia >250 mg/dL. To account for differences in the quantity of total FSG between groups, we adjusted for length of stay and calculated the proportion of FSG per patient. Results: 130 patients with COVID-19 and GC induced hyperglycemia from before (n=65) and after (n=65) protocol implementation were matched 1: 1 based on age, weight, and sex. There were no significant differences in other baseline patient characteristics. Significantly more patients in the protocol group had mean FSG in the euglycemic range (16.9% vs 38.5%, p=0. 006), and there was a decrease in patients with mean glucose in the mild hyperglycemia range that trended toward significance (50.8% vs 35.4%, p=0. 076). The protocol group had a significantly lower proportion of hypoglycemic FSG per patient (0. 003 vs 0. 008, p=0. 044). Patients in the protocol group had more euglycemic FSG per patient, but this did not reach significance (0.413 vs 0.350, p=0.239). Similarly, the proportion of mild hyperglycemic FSG per patient trended lower in the protocol group (0.315 vs 0.272, p=0.291). There were no significant differences in severe hyperglycemia per patient (0.311 vs 0.328, p=0.828). The protocol group had a significantly higher utilization of basal-bolus insulin regimen (n=43 vs n=63, p<0. 0001) with an increase percentage of bolus insulin utilization in peak total daily dose (70.2% vs. 62.4%, p=0. 08). Total peak day insulin dose trended lower in the protocol group (0.25 vs 0.32 U/kg, p=0.36). Conclusion: A novel insulin protocol for the management of GC-induced hyperglycemia in hospitalized COVID-19 patients is an effective tool to achieve reductions in hypoglycemic events and higher utilization of standard of care basal-bolus insulin regimens without increased hyperglycemia or total daily dose of insulin.Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.
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Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States. Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models. Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P< .001), severe hypoglycemia (4% vs 1%, P= .04), and hospitalization (52% vs 22%, P< .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P< .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P< .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P< .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01;95% CI, 2.11-12.63). Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.
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Older adults with type 1 diabetes (OAwT1D) face challenges such as hypoglycemia, cognitive dysfunction, and physical limitations. Limited data on their technology use is available. We evaluated technology preferences in OAwT1D stratified by tech-savviness. Adults ≥ 65 years old (n=26) were enrolled at two sites during the COVID-19 pandemic. Subjects were stratified as tech-savvy or non-tech savvy based on the Pew Technology Survey. Health-related quality of life was assessed by the SF-36 Survey. Semi-structured telephone interviews were conducted (n=23) and analyzed for thematic content using ATLAS.ti v8.0. Individuals were all well-educated;those in the tech-savvy group were younger, more likely to be employed, and reported better physical functioning (Table 1). Per interview data, all subjects used a computer, smartphone, or the internet;the non-savvy group relied more on others to help download information or navigate content. Subjects preferred in-person training, menu systems with data access in 3 steps, flexibility of data access and entry, and/or a help menu. For device management, 6/23 subjects downloaded their data to share with care teams and 14/23 subjects cited healthcare providers as their primary tech support. OAwT1D have unique preferences for technology use and training. Customized training for device use and patient support should be considered to maximize device benefits in this high-risk group.
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Introduction: Obesity is a major public health problem worldwide and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposures at the individual, community, and societal levels. However, monitoring and evaluating such changes is challenging. Development in the fields of behaviour change science, public health, clinical paediatrics, technology, citizen science and Big Data analytics can be harnessed to implement multidisciplinary research addressing the prevention and treatment of child and adolescent obesity at a population level. The H2020 project “BigO: Big Data Against Childhood Obesity” (http://bigoprogram.eu) is one example of such research efforts. Following the emergence of the COVID-19 pandemic, the BigO research team in Ireland adapted study procedures to ensure collection of Big Data could continue with modified procedures. Aim: To present the approach used for BigO data collection in Ireland during the COVID-19 pandemic and to explore changes in data collection over time. Methods: Step 1 reviewed and sought approval for ethical and regulatory procedures relevant to the collection, monitoring, and storage of personal data collected during the COVID-19 pandemic in children from the general population and those attending a multidisciplinary clinical service for severe obesity. Step 2 explored recruitment strategies and the informed consent and assent process. Step 3 explored the collection of anonymized data including: photographs of meals, beverages and advertisements, physical activity metrics, and masked GPS data using geo-hash representing geographical area rather than detailed coordinates. Following aggregation, analysis, and visualization of collected data descriptive statistics were used to explore patterns of behaviour in the population over time in order to better understand whether the system could be used to monitor behaviours through a period of significant societal change. Results: New ethical approval was granted for the updated methods. From 88 secondary schools approached to participate in the study, five agreed to commence an online consent process with parents and students. In the school setting, 700 children were eligible for study inclusion and 178 consented to participate, respectively. For the clinical study, images of outdoor advertisements collected from participants in Ireland changed during lock-down periods as children had less access to outdoor space and use of study smartwatches was discontinued to adhere to local infection control policies. Please see Table 1 for further details on results of the clinical study. Conclusions: Real-time collection of Big Data was possible through a period of societal upheaval though the expected volume of data was reduced. Such data may prove an important tool for monitoring interventions at the level of the individual child or at the population level for this vulnerable group.
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Introduction: COVID-19 has brought to the fore an urgent need for secure information and communication technology (ICT) supported healthcare delivery, as the pertinence of infection control and social distancing continues. Telehealth for pediatric care incorporates additional layers of complexity compared with adult services for a variety of reasons including logistical, privacy, parental consent, child assent, child welfare, and quality concerns. There is no systematic evidence synthesis available that outlines the implementation issues for incorporating telehealth to pediatric services generally, or how users perceive these issues. Methods: We conducted a rapid mixed-methods evidence synthesis to identify barriers, facilitators, and documented stakeholder experiences of implementing pediatric telehealth, to inform the pandemic response. A systematic search was undertaken by a research librarian in MEDLINE for relevant studies. All identified records were blind double-screened by two reviewers. Implementation-related data were extracted, and studies quality appraised using the Mixed-Methods Appraisal Tool. Qualitative findings were analyzed thematically and then mapped to the Consolidated Framework for Implementation Research. Quantitative findings about barriers and facilitators for implementation were narratively synthesized. Results: We identified 27 eligible studies (19 quantitative;5 mixed-methods, 3 qualitative). Important challenges highlighted from the perspective of the healthcare providers included issues with ICT proficiency, lack of confidence in the quality/reliability of the technology, connectivity issues, concerns around legal issues, increased administrative burden and/ or fear of inability to conduct thorough examinations with reliance on subjective descriptions. Facilitators included clear dissemination of the aims of ICT services, involvement of staff throughout planning and implementation, sufficient training, and cultivation of telehealth champions. Families often expressed preference for in-person visits but those who had tried tele-consultations, lived far from clinics, or perceived increased convenience with technology considered telemedicine more favorably. Concerns from parents included the responsibility of describing their child's condition in the absence of an examination. Conclusion: Healthcare providers and families who have experienced tele-consultations generally report high satisfaction and usability for such services. The use of ICT to facilitate pediatric healthcare consultations is feasible for certain clinical encounters and can work well with appropriate planning and quality facilities in place.
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INTRODUCTION: In early March 2020, COVID-19 spread in New York City. This paper shows trends of rising cases of DKA associated with a worsening COVID-19 pandemic in New York City. With the potential for such a large number of DKA patients, our institution found a need for revised glycemic management protocol. We discuss how a multidisciplinary team designed a protocol to care for patients with COVID-19 infection and DKA. METHODS: Five of The Mount Sinai Health System's EDs are on a shared electronic health record system (Epic Systems, Verona, WI). Deidentified visit data extracted for routine quality review was made available for analysis. We looked at total visits and select visit diagnoses related to DKA through March, April and May 2019 and compared those counts to the same period in 2020. Our protocol was developed by stakeholders in a multidisciplinary hospital team. We focused on the basic tenets of DKA management: insulin therapy, fluid resuscitation, and electrolyte repletion. RESULTS: A total of 93,218 visits were recorded across the five EDs from March 1-May 31, 2019. During that period there were 106 diagnoses of DKA made in the EDs (0.114% of visits). Across the same period in 2020 there were 59,009 visits, and 214 diagnoses of DKA (0.363% of visits). This coincides with the height of the Sars-CoV-2 pandemic in New York City. To address this surge, our protocol decreased the frequency of fingerstick monitoring, with changes to insulin dosing allowing admission to non-ICU beds. We transitioned from a provider-driven protocol to a nurse-driven protocol to avoid treatment delays due to order placement. Insulin infusion rate charts were created to provide nurses guidance on dosing modifications. To minimize ARDS risk, our protocol's fluid replacement recommendations lowered resuscitation and replacement rate volumes, adjusting based on provider reassessment. Lab values were monitored every 3-4 hours. CONCLUSIONS: There is a correlation between the rise of the Sars-CoV-2 pandemic in New York City and a net rise in patients diagnosed with diabetic ketoacidosis. We believe our DKA protocol will facilitate safe and effective management of patients with COVID-19 and DKA, reducing the healthcare burden associated with protocols that necessitate frequent treatment modifications and ICU-level care.