Glycemic Gap Predicts Mortality in a Large Multicenter Cohort Hospitalized with COVID-19.

PURPOSE: Diabetes or hyperglycemia at admission are established risk factors for adverse outcomes during hospitalization for COVID-19, but the impact of prior glycemic control is not clear. METHODS: We examined the relationship between clinical predictors including acute and chronic glycemia and clinical outcomes including ICU admission, mechanical ventilation (MV), and mortality among 1,786 individuals with diabetes or hyperglycemia (glucose > 10 mmol/l twice in 24 hrs.) admitted from March 2020 through February 2021 with COVID-19 infection at 5 university hospitals in the eastern U.S. RESULTS: The cohort was 51.3% male, 53.3% White, 18.8% Black, 29.0% Hispanic, with age = 65.6 ± 14.4 yrs., BMI = 31.5 ± 7.9 kg/m2, glucose = 12.0 ± 7.5 mmol/l [216 ± 135 mg/dl], and HbA1c = 8.07 ± 2.25%. During hospitalization, 38.9% were admitted to the ICU, 22.9% received MV, and 10.6% died. Age (p < 0.001) and admission glucose (p = 0.014) but not HbA1c were associated with increased risk of mortality. Glycemic gap, defined as admission glucose minus estimated average glucose based on HbA1c, was a stronger predictor of mortality than either admission glucose or HbA1c alone (OR = 1.040 [95% CI: 1.019, 1.061] per mmol/l, p < 0.001). In an adjusted multivariable model, glycemic gap, age, BMI, and diabetic ketoacidosis on admission were associated with increased mortality, while higher eGFR and use of any diabetes medication were associated with lower mortality (p < 0.001). CONCLUSIONS: Relative hyperglycemia, as measured by the admission glycemic gap, is an important marker of mortality risk in COVID-19.

Three Year Outcomes after Metabolic Surgery or Medical/Lifestyle Intervention: The ARMMS-T2D Trial

There is limited Level-1 evidence from well-powered randomized controlled trials (RCTs) examining improved glycemic control after metabolic surgery in patients with type 2 diabetes (T2D) and obesity. ARMMS-T2D is a multi-center consortium conducting a follow-up study of 4 merged RCTs in 256 patients with baseline Class 1-3 obesity and T2D, randomly assigned to metabolic surgery (MS;Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric banding) or intensive Medical/Lifestyle Intervention (MLI). Three years after randomization, T2D remission rates were higher after MS than MLI (37.5%, 60/160 vs. 2.6%, 2/76, respectively, P<0.001). Adjusting for treatment allocation, baseline HbA1c, and T2D duration, the probability of remission with MS was 41.6% (95% CI, 29.6-58.3%) compared to 1% (95% CI, 0.2-4.0%) with MLI (P<0.001). MS patients experienced greater reductions than MLI in HbA1c (-1.9±2.0 vs. -0.1±2.0%, P<0.001) and fasting plasma glucose (-52 [-105, 5] vs. -12 [-48, 26] mg/dL, P<0.001). Compared to MLI, MS patients had greater reductions in BMI (-22.0±9.4 vs. -4.8±7.9 kg/m2, P<0.001) and waist circumference (-17.5 ±10.2 vs. -2.1±9.6 cm, P<0.001), greater increases in HDL-C (35.5± 27.6 vs. 9.1±24.1, P<0.001), greater reductions in triglycerides (-33[-52, -2] vs. -10 [-36, 14] P<0.001), and similar changes in LDL-C (9.5±41.5 vs. 4.2±31.6 mg/dL). MS and MLI rendered similar reductions in albumin/creatinine ratio (-2 [-13, 1] vs. 0 [-4, 4]) and eGFR (-3.1±16.7 vs. -4.6±19.5 mL/min/1.73 m2). Also, MS patients required fewer medications for diabetes, hypertension, and dyslipidemia compared to MLI (P<0.001). In summary, this 3-year follow-up of the largest cohort of patients randomized to metabolic surgery vs. non-surgical treatment demonstrates that surgery is more effective than intensive medical/lifestyle therapy in achieving extended diabetes remission, BMI reduction, and improved metabolic disease biomarkers while reducing medication requirements. Disclosure: J. P. Kirwan: None. J. M. Jakicic: Advisory Panel;Self;Naturally Slim, Spark360, Weight Watchers International, Inc. M. Patti: Consultant;Self;Cello Health, Fractyl Laboratories, Inc., MBX, Poxel SA, WGBH, Other Relationship;Self;Xeris Pharmaceuticals, Inc., Research Support;Self;Dexcom, Inc. K. Wolski: None. P. Schauer: Advisory Panel;Self;GI Dynamics, Keyron, Mediflix, Persona, Consultant;Self;Ethicon, Inc., Medtronic, Research Support;Self;Ethicon, Inc., Medtronic, Pacira. A. Courcoulas: None. D. E. Cummings: Advisory Panel;Self;DyaMx, GI Dynamics. A. Goldfine: Employee;Self;Novartis AG. S. Kashyap: Advisory Panel;Self;Fractyl Laboratories, Inc., GI Dynamics. D. C. Simonson: Stock/Shareholder;Spouse/Partner;Phase V Technologies, Inc. D. Arterburn: None. W. F. Gourash: None. A. H. Vernon: None. Funding: National Institutes of Health (DK114156);Ethicon Endo-Surgery;Covidien [ABSTRACT FROM AUTHOR] Copyright of Diabetes is the property of American Diabetes Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Outreach Method Predicts Patient Re-engagement in Diabetes Care During Sustained Care Disruption.

OBJECTIVE: During the COVID-19 pandemic, visits for diabetes care were abruptly canceled without predefined procedures to re-engage patients. This study was designed to determine how outreach influences patients to maintain diabetes care and identify factors that might impact the intervention's efficacy. METHODS: A diabetes nursing team attempted outreach for patients who had a canceled appointment for diabetes between March 16, 2020, and June 19, 2020. Outreach status was defined as reached, message left, or no contact. Outcomes were defined as follows: (1) booking and (2) keeping a follow-up appointment. RESULTS: Seven hundred eighty-seven patients were included (384 [49%] were reached, 152 (19%) were left a message, and 251 (32%) had no contact). Reached patients were more likely to book [odds ratio (OR) = 2.43, P < .001] and keep an appointment (OR = 2.39, P < .001) than no-contact patients. Leaving a message did not increase the odds of booking (OR = 1.05, P = .84) or keeping (OR = 1.17, P = .568) an appointment compared with no contact. Older age was a significant predictor of booking an appointment (OR = 1.014 for each year of age, P = .037). Patients on insulin were more likely to keep their appointment (OR = 1.70, P = .008). Patients with a higher hemoglobin A1C level were less likely to keep their appointment (OR = 0.87 for each 1.0% increase in the hemoglobin A1C level, P = .011). CONCLUSION: These findings suggest that to optimize re-engagement during care disruption, 1-way communication is no better than no contact and that 2-way communication increases the likelihood that patients will maintain access to care. In addition, although higher-risk patients (eg, patients with older age or those on insulin) may be more incentivized to stay engaged, targeted outreach is needed for those with chronically poor glycemic control.

Outreach Method Predicts Patient Engagement during Sustained Care Disruption

The COVID-19 pandemic has caused sustained disruptions in access to usual diabetes care. In response to the high number of cancelations, an academic, urban diabetes program transitioned to virtual visits and launched an initiative to provide outreach to patients who canceled an appointment between Mar 16 and Jun 19, 2020. Members of the diabetes team used a standardized approach to prevent delayed care or disengagement. Method of contact was defined as Reached (two-way communication between clinician and patient by phone or EHR portal message), Message Left (e.g., voicemail) and No Contact. Engagement was defined as booking a follow up appointment and keeping the appointment. A total of 787 patients were determined to have canceled visits due to the pandemic. Mean (SD) age was 61.7 yrs (14.2), 53.7% female, 67.3% white, baseline A1c 7.96% (1.81). Of the 648 visits booked, 519 (80%) were kept. Patients who were reached were more likely to book (OR = 2.43, p<0.001) and keep an appointment (OR = 2.39, p<0.001) compared to no contact or message left. Older age was a significant predictor of booking (OR = 1.014 for each year older, p=0.037). Patients on insulin were more likely to keep their appointment (OR = 1.70, p=0.008), while patients with higher HbA1c were less likely to (OR = 0.87 for each 1.0% increase in HbA1c, p=0.011). These findings suggest that for systems designed to optimize engagement during care disruption, one-way communication is no better than no contact, and two-way communication increases the likelihood that patients will maintain access to care. In addition, while higher risk patients (e.g., older age or on insulin) may be more incentivized to stay engaged, targeted outreach may be needed for those with chronic poor glycemic control.

The Impact of COVID-19 Infection on Subsequent Glycemic Control

Recent studies suggest COVID-19 infection has a detrimental effect on glycemic control in patients with diabetes in the acute care setting. However, there is limited information about the impact of COVID-19 on glycemic control in the months following infection. In this retrospective observational study, we examined the correlation between COVID-19 infection and glycemic control in adult patients with elevated A1c in a single health system (n=3,295). Patients were selected from a cohort of those age 18 years or older who were tested for COVID-19 between 3/1/2020 and 11/1/2020. COVID positive patients were identified through positive SARS-CoV-2 PCR or a COVID flag in the EMR. COVID negative patients were randomly selected in a 3:1 ratio from all patients who had a negative COVID test in the same week as each positive patient. Patients from this cohort were included if they had a baseline A1c ≥ 5.7% measured in the preceding six months and at least two weeks prior to their COVID positive (n=670) or COVID negative (n=2,625) test date, as well as an endpoint A1c ≥ 5.7% at least three months after this date. The study population was 54% female, 60% White, 20% Hispanic, and 13% Black with a mean BMI 32 ± 7.3 kg/m2, age 64.1 ± 13.6 yrs, and baseline A1c of 7.7± 1.6 %;81% of patients had a diagnosis of type 2 diabetes. In a linear regression model, COVID-19 infection was significantly associated with increased endpoint A1c (coefficient = 0.14, 95% CI [0.03 - 0.25], p = 0.01). Baseline A1c, male sex, and Hispanic ethnicity were also significantly associated with increased A1c, while age and median income based on zip code were associated with decreased A1c. In conclusion, prior COVID-19 infection appears to be associated with worse glycemic control at least 3 months post-infection compared to patients who did not contract COVID-19. Additional research is needed to determine if this association is present in other patient populations as well as the physiologic and socio-demographic causes for this finding.

Stress Hyperglycemia Predicts Mechanical Ventilation and Death in COVID-19 Infected Adults

People with diabetes (DM) hospitalized with COVID-19 infection have a 2-3 fold higher risk of death compared with those without DM, and mechanical ventilation (MV) has been identified as a major risk factor for death. While stress hyperglycemia (StH) has been established as a risk factor for death in some critically ill cohorts, it is not a well-established risk factor for MV in COVID-19. The COVIDEastDM consortium pooled data from 5 academic hospitals on the East Coast of the US to study the relationship between hyperglycemia and COVID-19 outcomes. Data were obtained retrospectively from electronic records of adults with COVID-19 and either DM or StH (defined in this cohort as day-1 admission blood glucose >180 mg/dl and A1c <6.5%). This analysis included 3,435 individuals, of which 1,001 (29.1%) required MV and 748 (21.8%) died. The mean age was 67 ± 15 yrs., BMI 30.4 ± 7.7 kg/m2, A1c 7.98 ± 2.21 % and glucose 184 ± 104 mg/dl. Additionally, 57% were male, 4 % Asian,19% Black, 52% White, and 28% Hispanic. In a univariate analysis, risk factors for MV included younger age (OR 0.98 [95% CI 0.97, 0.99] per year older), male sex (OR 1.4 [1.2, 1.7]), BMI (OR 1.013 [1.003, 1.023] per kg/m2), Hispanic ethnicity (OR 1.16 [1.07, 1.28]) and the presence of diabetic ketoacidosis (n=38) at admission (OR 3.9 [2.0, 7.5]). Patients with StH were more likely to require MV (OR 1.93, [1.10, 3.39]). In a multivariate analysis, this relationship was continuous, with both lower A1c and higher glucose increasing risk of MV (p< 0.01 for higher glucose, p<0.001 for lower A1c). Patients who required MV were more likely to die than those who did not require MV (OR 5.1, [4.3, 6.1]) and StH predicted mortality in the multivariate analysis. Thus, in patients hospitalized with COVID19 infection, StH is a strong predictor of both MV and death in COVID-19 infected adults. While it is unclear if StH is a cause or effect of severe COVID-19, its presence early in the hospital course identifies higher risk patients and could potentially impact management.