key: cord-0895377-wcqr1cxm
authors: Plummer, Mark P.; Rait, Louise; Finnis, Mark E.; French, Craig J.; Bates CCRN, Samantha; Douglas, James; Bhurani, Mansi; Broadley, Tessa; Trapani, Tony; Deane, Adam M.; Udy, Andrew A.; Burrell, Aidan JC.
title: Diabetes mellitus, glycaemic control and severe COVID-19 in the Australian Critical Care Setting – a nested cohort study
date: 2022-05-23
journal: Aust Crit Care
DOI: 10.1016/j.aucc.2022.05.002
sha: f800be569a2dd5cc1060c14039fea301e9894b7a
doc_id: 895377
cord_uid: wcqr1cxm

BACKGROUND: Internationally, diabetes mellitus is recognized as a risk factor for severe COVID-19. The relationship between diabetes mellitus and severe COVID-19 has not been reported in the Australian population. OBJECTIVES: To determine the prevalence of, and outcomes for patients with diabetes admitted to Australian intensive care units (ICUs) with COVID-19. METHODS: A nested cohort study of four ICUs in Melbourne participating in the the Short PeRiod IncideNce sTudy of Severe Acute Respiratory Infection (SPRINT-SARI) Australia project. All adult patients admitted to ICU with COVID-19 from 20 February 2020 to 27 February 2021 were included. Blood glucose and glycated haemoglobin (HbA1c) data were retrospectively collected. Diabetes was diagnosed from medical history or a HbA1c ≥6.5% (48 mmol/mol). Hospital mortality was assessed using logistic regression. RESULTS: There were 136 patients with median age 58 years [48-68] and median APACHE II score of 14 [11-19]. 58 patients had diabetes (43%), 46 patients had stress induced hyperglycaemia (34%) and 32 patients had normoglycaemia (23%). Patients with diabetes were older, with higher APACHE II scores, had greater glycaemic variability than patients with normoglycaemia and longer hospital length of stay. Overall hospital mortality was 16% (22/136), including nine patients with diabetes, nine patients with stress induced hyperglycaemia and two patients with normoglycaemia. CONCLUSION: Diabetes is prevalent in patients admitted to Australian ICUs with severe COVID-19 highlighting the need for prevention strategies in this vulnerable population.

During previous outbreaks of highly transmissible respiratory viral infections, including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and the H1N1 influenza virus, a greater risk of infection was observed in people with diabetes (1). This is not the case for coronavirus disease 2019 (COVID- 19) , with similar rates of diabetes in patients with COVID-19 compared to the general population (2) . Despite not increasing the risk of infection, diabetes is strongly associated with progression to severe COVID-19 and death. Worldwide, approximately one quarter of adults with severe COVID-19 have pre-existing diabetes (3) , substantially greater than the global prevalence of <10% (4). Moreover, pre-existing diabetes is associated with nearly double the risk of death from severe COVID-19 in international meta-analyses (5) .

Hyperglycaemia in severe COVID-19 also occurs frequently in patients with previously normal glucose tolerance -so-called stress induced hyperglycaemia (1) . In addition to the known metabolic effects of critical illness, COVID-19 is thought to be particularly deleterious for glycaemic control, due to direct effects of the virus increasing insulin resistance and impairing insulin secretion (5, 6) . This is further exacerbated by corticosteroid treatment which has become a standard of care for critically ill patients requiring supplemental oxygen and/or mechanical ventilation (7) . The distinction between stress induced hyperglycaemia and the chronic hyperglycaemia attributed to diabetes is likely important, as the presence of chronic hyperglycaemia is known to attenuate the association between acute hyperglycaemia and mortality in a general critically ill population (8) .

There is a paucity of data assessing the interaction between acute and chronic hyperglycaemia and its impact on mortality in severe COVID-19. Moreover, previous meta-analyses demonstrating the association between diabetes and a worse prognosis have failed to include Australian data (2, 9) . This is important as the 13% mortality rate for severe COVID-19 in Australia (10) is substantially lower than that reported globally; pooled estimate 28.1% (95% CI, 23.4 -33.0) (3).

The Short Period Incidence Study of Severe Acute Respiratory Infections (SPRINT-SARI) Australia study (10) has been prospectively collecting comprehensive data on critically ill patients with COVID-19 admitted to Australian intensive care units (ICU) from February 2020. Within the SPRINT-SARI case report form, diabetes status is recorded as well as peak daily blood glucose. We used a nested cohort within the SPRINT-SARI Australia database to determine the clinical characteristics and outcomes for patients with diabetes and stress induced hyperglycaemia. Our primary hypothesis was that clinical outcomes would be inferior in those patients with diabetes, independent of age and admission illness severity.

We conducted a nested cohort study within a multi-centre national registry following the recommendations of the STROBE Statement (11) . Ethics approval with full consent waiver was granted under the National Mutual Acceptance scheme by the Alfred Health Human Research Ethics Committee (HREC/16/Alfred/59) or by specific applications at individual sites.

The methodology for SPRINT-SARI Australia has been described in detail elsewhere (10) . In brief, the SPRINT-SARI Australia study prospectively collected data on all suspected and confirmed COVID-19 admissions to participating ICUs, adult and paediatric. Patients included in this nested cohort study 

Diabetes mellitus was defined as any patient recorded as having diabetes and/or a HbA1c ≥6.5% (48 mmol/mol) (12) . Patients with stress hyperglycaemia were patients without diabetes and a random plasma glucose ≥11.1 mmol/l. While the threshold blood glucose to define stress hyperglycaemia remains contentious (13) , the American Diabetes Association (ADA) Diabetes in Hospitals Writing Committee Guidelines suggest random plasma glucose ≥ 11.1 mmol/l is appropriate for use in hospitalised patients (12) . Given the majority of critically ill patients receive continuous enteral nutrition (14) we chose to use this threshold prior to reviewing available data. Normoglycaemic patients were those without stress induced hyperglycaemia or diabetes. Glycaemic variability was analysed as the standard deviation and the coefficient of variation (SD/mean x100%) of blood glucose values (15) . Moderate hypoglycaemia was defined as any blood glucose ≤ 4.0 mmol/L and severe hypoglycaemia as a subset with any blood glucose ≤ 2.2 mmol/L (12).

Data are presented as frequencies and proportions for categorical variables and mean (standard deviation) or median [interquartile range] for continuous variables. Proportions were compared using the χ 2 or Fisher's exact test. Between group comparisons were performed by t-test, Wilcoxon ranksum or Kruskal-Wallis test as indicated. Univariate logistic regression was used to assess factors associated with the primary outcome, hospital mortality. The relationship between glycaemia status and hospital outcome was assessed with univariate and multivariable logistic regression, with adjustment for age and severity of illness (as the Acute Physiology and Chronic Health Evaluation II score, APACHE II) planned a priori. Analysis of glycaemic variability by time period was designated a priori as pre-and post-the online pre-publication of the RECOVERY trial data on the 16 th of June 2020 (7) . Continuous coefficients of glucose variation were analysed using linear regression with the post-RECOVERY time period as a binary indicator variable. Binary glycaemic outcomes were analysed by logistic regression and are presented as the odds ratio, with 95% confidence interval and corresponding P-value. ICU and hospital lengths of stay were analysed by competing risks regression as per Fine and Gray (16) , with death as the competing event and estimates presented as the respective sub-hazard ratio (SHR) and corresponding 95% CI. All regression analyses were performed employing robust standard errors to allow for within ICU correlation. Analyses were performed using Stata, version 16.1 (StataCorp). Table 1 .

In total, 58 (43%) had diabetes, 46 (34%) had stress-induced hyperglycaemia and 32 (23%) patients were normoglycaemic. Six (11%) patients were defined as having diabetes by HbA1c criteria alone.

There were 11,375 blood glucose measurements recorded with a median of 42 measurements per patient. Patients with diabetes had greater mean and maximum glucose concentrations than patients with stress induced hyperglycaemia or normoglycaemia ( Figure 1 , and Table 2 ). Hypoglycaemia occurred infrequently, with 11 (8.1%) patients recording one or more episodes of moderate hypoglycaemia ( 4.0 mmol/L) and 2 (1.5%) patients with diabetes recording episodes of severe hypoglycaemia ( 2.2 mmol/L) ( Table 2) . Glycaemic variability was greater among patients with diabetes than in patients with stress induced hyperglycaemia and normoglycaemia (Table 2) .

Two of 32 (6.3%) patients with normoglycaemia, 10 of 46 (22%) patients with stress induced hyperglycaemia and 10 of58 (17%) patients with diabetes died in hospital. Normoglycaemic patients were younger and had lower APACHE II scores than patients with stress induced hyperglycaemia or diabetes (Table 1) . After adjusting for the covariates of age and severity of illness (APACHE II) there was no association between acute or chronic hyperglycaemia and hospital mortality; stress induced hyperglycaemia OR 1.61 (95%CI 0.51 -5.13); P = 0.42, diabetes OR 1.37 (95% CI 0.52 -3.61) P = 0.53 ( Figure 2 ). ICU length of stay differed markedly across glycaemia groups (Table 2) . Under a competing risks regression model, adjusted for age and APACHE2 score, the sub-hazard ratios for discharge alive from ICU compared with the normoglycaemia group were: stress induced hyperglycaemia 0.32 (0.20, 

Dysglycaemia was prevalent in patients with severe COVID-19 admitted to Australian adult ICUs.

Nearly half of patients had pre-existing diabetes and another third had stress induced hyperglycaemia.

Routine use of corticosteroids resulted in greater perturbations in glycaemic control with higher mean and peak blood glucose levels, and increased glycaemic variability, suggesting that pre-existing insulin protocols were inadequate for these patients. Patients with dysglycaemia had markedly longer ICU and hospital admissions.

The prevalence of diabetes reported in this nested cohort of severe COVID-19 is approximately five times higher than the prevalence in the Australian population (7.4%) (17) . Moreover, it is nearly double the 22% prevalence of recognised diabetes reported in the Australian critically ill population in the pre-COVID-19 era (8) . With this high prevalence of patients with chronic hyperglycaemia, the prevalence of stress hyperglycaemia in our cohort is comparatively lower than the 50% reported in the pre-COVID-19 era (8) . The prevalence of hypoglycaemia is similar to historical rates (18) . It is likely that there are epidemiological and pathophysiological changes driving worse outcomes for patients with diabetes and COVID-19. It is well recognised that age is strongly associated with worse outcomes in COVID-19 and the prevalence of diabetes increases with age in both the general population and in patients with COVID-19 (1). Due to the syndromic nature of the disease, diabetes is associated with J o u r n a l P r e -p r o o f hypertension, obesity and cardiovascular disease. Moreover, older patients with diabetes are more likely to have had a longer exposure to dysglycaemia with a greater prevalence of microvascular and macrovascular complications (19) . Several observational studies have reported a greater prevalence of hypertension, cardiovascular, cerebrovascular, and chronic kidney disease in COVID-19 patients with diabetes; co-morbidities independently associated with worse outcomes (20) . In addition to these epidemiological hallmarks of severe disease, it is likely that diabetes is an independent determinant of severe COVID-19. Putative mechanisms include diabetes induced dysregulated immune responses, altered expression of Renin-Angiotensin-Aldosterone-System effectors and hyperglycaemia induced endothelial dysfunction (5 are wide the point estimate is consistent with the recognised association between stress hyperglycaemia and mortality in a non-COVID critically ill population (8) , and severe COVID-19 (24) (25).

In our population, the mortality rate for patients with diabetes was 17% (10/58), markedly lower than reported in Italy 40% (328/814), the USA 37% (509/1370) and the UK 35% (1223/3524) (21) (22) (23) . The lower mortality for patients with diabetes parallels the higher survivorship of severe COVID-19 in J o u r n a l P r e -p r o o f Australia overall (10) . Together with the lower sample size, this contributed to our study being under powered to detect a mortality difference.

Our results have important implications for the management of patients with diabetes in Australia during the current COVID-19 pandemic. First, since diabetes is overrepresented among patients with severe COVID-19 requiring ICU admission, strategies to prevent COVID-19 in patients with diabetes remain important. Second, even in a non-overwhelmed critical care setting with the ability to provide vigilant glucose monitoring with high nurse to patient ratios, the routine use of dexamethasone worsens glycaemic control and requires vigilant monitoring. Whether this has implications for dexamethasone use in critically ill COVID-19 patients, and/or their prognosis, requires further study.

Strengths of our study include that it was performed using data from a national database in which data collection was performed by experienced research staff using a standardised case report form.

The follow-up rate was high with complete data for the primary outcome of hospital mortality. This yielded novel data on metrics of dysglycaemia including glycaemic variability and hypoglycaemia.

There are, however, important limitations. Firstly, as outlined above, the small sample size and low mortality rate resulted in the study being an inadequate sample to detect a mortality difference between groups if one truly existed. Secondly, the nested cohort of four hospitals in Melbourne captured 27% of Australian ICU admissions and may not be representative of the national experience with COVID-19. Thirdly, a recent glycated haemoglobin was only available in 42% of the cohort, resulting in an additional 11% being classified as having unrecognised diabetes. There may be additional cases with diabetes misclassified as stress induced hyperglycaemia and the true prevalence of diabetes is likely higher. Observational data in the pre-COVID era would suggest the prevalence of unrecognised diabetes in an adult critically ill population is at least 5% (8) . Fourthly, covid variant was not reliably analysed and provided from local pathology databases; thus we are unable to comment J o u r n a l P r e -p r o o f on variant specific differences. Fifthly, while vaccination status was not reported, by virtue of the timing of the capture period, all of the patients were unvaccinated and further studies are indicated to assess how interactions between diabetes status and outcome are modified in the setting of widespread vaccination. Finally, clinical information on diabetes sub-type, duration of diabetes and the presence of complications was not available and we lacked data to assess the impact of diabetes specific risk-factors including pre-morbid glycaemic control and glucose lowering medications (26) .

Given the heterogeneity of diabetes, characterising which risk factors are most useful to identify progression to severe disease is an important area for future research (20, 23) .

In summary, in a nested cohort of four adult ICUs in Australia, diabetes complicated nearly half of COVID-19 admissions and was associated with longer ICU and hospital length of stay. The high prevalence of this condition in patients with severe COVID-19 suggests this group is highly vulnerable.

Data sharing statement: Data sharing requests will be considered on an individual basis by the SPRINT-SARI Australia management committee. Requests for de-identified data are to be sent to MNHS-Sprint.Sari@monash.edu. Any such requests will require a formal data sharing agreement, and must be consistent with the Australian and New Zealand Intensive Care -Research Centre (ANZIC-RC) Data Sharing Policy, available at: https://www.monash.edu/__data/assets/pdf_file/0010/2153899/2020-03-19-ANZIC-RC-Terms-of-Reference.pdf 

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