Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring

Shen, Yun; Fan, Xiaohong; Zhang, Lei; Wang, Yaxin; Li, Cheng; Lu, Jingyi; Zha, Bingbing; Wu, Yueyue; Chen, Xiaohua; Zhou, Jian; Jia, Weiping

doi:10.2337/dc20-1448

Cited by 38 publications

(35 citation statements)

References 26 publications

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“…Glucose telemonitoring is emphasised for patients admitted to hospital with COVID, both those with diabetes and those without diabetes but high admission glucose. Using CGM in 35 patients with COVID and diabetes, Shen et al [ 44 ] showed that %TAR > 160 mg/dL (> 8.9 mmol/L) or %TBR < 70 mg/dL (< 3.9 mmol/L) is associated with up to 2.45-fold increased odds ratio of ICU admission, mechanical ventilation or hypotension/multiple organ dysfunction. Length of hospital stay was also associated with %TAR and %TBR.…”

Section: Hyperglycaemia Is a Significant Risk Factor For Covid Infection Hospitalization And Deathmentioning

confidence: 99%

Telemonitoring, Telemedicine and Time in Range During the Pandemic: Paradigm Change for Diabetes Risk Management in the Post-COVID Future

et al. 2021

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People with diabetes are at greater risk for negative outcomes from COVID-19. Though this risk is multifactorial, poor glycaemic control before and during admission to hospital for COVID-19 is likely to contribute to the increased risk. The COVID-19 pandemic and restrictions on mobility and interaction can also be expected to impact on daily glucose management of people with diabetes. Telemonitoring of glucose metrics has been widely used during the pandemic in people with diabetes, including adults and children with T1D, allowing an exploration of the impact of COVID-19 inside and outside the hospital setting on glycaemic control. To date, 27 studies including 69,294 individuals with T1D have reported the effect of glycaemic control during the COVID-19 pandemic. Despite restricted access to diabetes clinics, glycaemic control has not deteriorated for 25/27 cohorts and improved in 23/27 study groups. Significantly, time in range (TIR) 70–180 mg/dL (3.9–10 mmol/L) increased across 19/27 cohorts with a median 3.3% (− 6.0% to 11.2%) change. Thirty per cent of the cohorts with TIR data reported an average clinically significant TIR improvement of 5% or more, possibly as a consequence of more accurate glucose monitoring and improved connectivity through telemedicine. Periodic consultations using telemedicine enables care of people with diabetes while limiting the need for in-person attendance at diabetes clinics. Reports that sustained hyperglycaemia and early-stage diabetic ketoacidosis may go untreated because of the lockdown and concerns about potential exposure to the risk of infection argue for wider access to glucose telemonitoring. Therefore, in this paper we have critically reviewed reports concerning use of telemonitoring in the acute hospitalized setting as well as during daily diabetes management. Furthermore, we discuss the indications and implications of adopting telemonitoring and telemedicine in the present challenging time, as well as their potential for the future.

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Section: Hyperglycaemia Is a Significant Risk Factor For Covid Infection Hospitalization And Deathmentioning

confidence: 99%

Telemonitoring, Telemedicine and Time in Range During the Pandemic: Paradigm Change for Diabetes Risk Management in the Post-COVID Future

et al. 2021

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“…45 Most importantly, a spline analysis of glucose levels in patients with DM with continuous glucose monitoring showed a non-linear relationship between time spent above range and glycaemic variability with the increased likelihood of composite adverse COVID-19 outcomes (need for ICU admission, mechanical ventilation or critical illness). 46…”

Section: Open Accessmentioning

confidence: 99%

“…This also prevented the analysis of time in range or BG variability, both being linked to increased severity, case fatality and poor COVID-19 outcomes. [42][43][44][45][46] Lastly, the study period coincides with the height of the first pandemic wave in Spain, when there was a shortage of ventilators and intensive care beds. At this point, age was the deciding factor for whether or not someone received potentially lifesaving ICU care.…”

Section: Limitations Of This Studymentioning

confidence: 99%

Risk factors for severe outcomes in people with diabetes hospitalised for COVID-19: a cross-sectional database study

et al. 2021

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AimThis study’s objective was to assess the risk of severe in-hospital complications of patients admitted for COVID-19 and diabetes mellitus (DM).DesignThis was a cross-sectional study.SettingsWe used pseudonymised medical record data provided by six general hospitals from the HM Hospitales group in Spain.Outcome measuresMultiple logistic regression analyses were used to identify variables associated with mortality and the composite of mortality or invasive mechanical ventilation (IMV) in the overall population, and stratified for the presence or absence of DM. Spline analysis was conducted on the entire population to investigate the relationship between glucose levels at admission and outcomes.ResultsOverall, 1621 individuals without DM and 448 with DM were identified in the database. Patients with DM were on average 5.1 years older than those without. The overall in-hospital mortality was 18.6% (N=301), and was higher among patients with DM than those without (26.3% vs 11.3%; p<0.001). DM was independently associated with death, and death or IMV (OR=2.33, 95% CI: 1.7 to 3.1 and OR=2.11, 95% CI: 1.6 to 2.8, respectively; p<0.001). In subjects with DM, the only variables independently associated with both outcomes were age >65 years, male sex and pre-existing chronic kidney disease. We observed a non-linear relationship between blood glucose levels at admission and risk of in-hospital mortality and death or IMV. The highest probability for each outcome (around 50%) was at random glucose of around 550 mg/dL (30.6 mmol/L), and the risks flattened above this value.ConclusionThe results confirm the high burden associated with DM in patients hospitalised with COVID-19 infection, particularly among men, the elderly and those with impaired kidney function. Moreover, hyperglycaemia on admission was strongly associated with poor outcomes, suggesting that personalised optimisation could help to improve outcome during the hospital stay.

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“…This work highlights the potential for data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19. Unlike previous studies characterizing glucose in COVID-19, 3 , 9 , 20 , 60 this work utilizes electronic medical records from a large cohort of patients across the United States. Presentation of a low- and high-complexity model emphasizes the heterogeneity in glucose curves of patients with COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering

2021

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Objective Hyperglycemia has emerged as an important clinical manifestation of coronavirus disease 2019 (COVID-19) in diabetic and non-diabetic patients. Whether these glycemic changes are specific to a subgroup of patients and persist following COVID-19 resolution remains to be elucidated. This work aimed to characterize longitudinal random blood glucose in a large cohort of non-diabetic patients diagnosed with COVID-19. Materials and Methods De-identified electronic medical records of 7,502 patients diagnosed with COVID-19 without prior diagnosis of diabetes between January 1st and November 18th, 2020 were accessed through the TriNetX Research Network. Glucose measurements, diagnostic codes, medication codes, laboratory values, vital signs, and demographics were extracted before, during, and after COVID-19 diagnosis. Unsupervised time-series clustering algorithms were trained to identify distinct clusters of glucose trajectories. Cluster associations were tested for demographic variables, COVID-19 severity, glucose-altering medications, glucose values, and new-onset diabetes diagnoses. Results Time-series clustering identified a low-complexity model with 3 clusters and a high-complexity model with 19 clusters as the best-performing models. In both models, cluster membership differed significantly by death status, COVID-19 severity, and glucose levels. Clusters membership in the 19 cluster model also differed significantly by age, sex, and new-onset diabetes mellitus. Discussion and Conclusion This work identified distinct longitudinal blood glucose changes associated with subclinical glucose dysfunction in the low-complexity model and increased new-onset-diabetes incidence in the high complexity model. Together, these findings highlight the utility of data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19 and provide clinical evidence for further evaluation of the role of COVID-19 in diabetes pathogenesis. Lay Summary Hyperglycemia is defined as elevated blood glucose measurements and are common in diabetic patients. Recent findings suggest that patients diagnosed with COVID-19 may experience elevated blood glucose levels during COVID-19 infection. Whether blood glucose levels remain elevated after COVID-19 infection remains poorly understood. This study aimed to identify how patterns of blood glucose levels before, during, and after COVID-19 change. This work analyzed blood glucose levels from 2,059 patients diagnosed with COVID-19 and used machine learning to identify different patterns of blood glucose changes. We found patterns demonstrating an overall increase, overall decrease, temporal increase, and temporary decrease in blood glucose levels. Some of these patterns were associated with COVID-19 severity and proportion of patients with new-onset diabetes. These findings demonstrate the usefulness of machine learning in understanding glucose changes following COVID-19 diagnosis and indicate that more research is needed to understand if blood glucose monitoring in COVID-19 patients should be routinely performed.

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Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring

Cited by 38 publications

References 26 publications

Telemonitoring, Telemedicine and Time in Range During the Pandemic: Paradigm Change for Diabetes Risk Management in the Post-COVID Future

Telemonitoring, Telemedicine and Time in Range During the Pandemic: Paradigm Change for Diabetes Risk Management in the Post-COVID Future

Risk factors for severe outcomes in people with diabetes hospitalised for COVID-19: a cross-sectional database study

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering

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