key: cord-0933144-ocufdto4
authors: Sourij, Harald; Aziz, Faisal; Bräuer, Alexander; Ciardi, Christian; Clodi, Martin; Fasching, Peter; Karolyi, Mario; Kautzky‐Willer, Alexandra; Klammer, Carmen; Malle, Oliver; Oulhaj, Abderrahim; Pawelka, Erich; Peric, Slobodan; Ress, Claudia; Sourij, Caren; Stechemesser, Lars; Stingl, Harald; Stulnig, Thomas; Tripolt, Norbert; Wagner, Michael; Wolf, Peter; Zitterl, Andreas; Kaser, Susanne
title: COVID‐19 fatality prediction in people with diabetes and prediabetes using a simple score upon hospital admission
date: 2020-12-04
journal: Diabetes Obes Metab
DOI: 10.1111/dom.14256
sha: b5f9a89357cb0019403defc55b27e7ed5b0e1aff
doc_id: 933144
cord_uid: ocufdto4

AIM: To assess predictors of in‐hospital mortality in people with prediabetes and diabetes hospitalized for COVID‐19 infection and to develop a risk score for identifying those at the greatest risk of a fatal outcome. MATERIALS AND METHODS: A combined prospective and retrospective, multicentre, cohort study was conducted at 10 sites in Austria in 247 people with diabetes or newly diagnosed prediabetes who were hospitalized with COVID‐19. The primary outcome was in‐hospital mortality and the predictor variables upon admission included clinical data, co‐morbidities of diabetes or laboratory data. Logistic regression analyses were performed to identify significant predictors and to develop a risk score for in‐hospital mortality. RESULTS: The mean age of people hospitalized (n = 238) for COVID‐19 was 71.1 ± 12.9 years, 63.6% were males, 75.6% had type 2 diabetes, 4.6% had type 1 diabetes and 19.8% had prediabetes. The mean duration of hospital stay was 18 ± 16 days, 23.9% required ventilation therapy and 24.4% died in the hospital. The mortality rate in people with diabetes was numerically higher (26.7%) compared with those with prediabetes (14.9%) but without statistical significance (P = .128). A score including age, arterial occlusive disease, C‐reactive protein, estimated glomerular filtration rate and aspartate aminotransferase levels at admission predicted in‐hospital mortality with a C‐statistic of 0.889 (95% CI: 0.837‐0.941) and calibration of 1.000 (P = .909). CONCLUSIONS: The in‐hospital mortality for COVID‐19 was high in people with diabetes but not significantly different to the risk in people with prediabetes. A risk score using five routinely available patient variables showed excellent predictive performance for assessing in‐hospital mortality.

Following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the end of 2019 in Wuhan, China, COVID-19 1 disease has rapidly spread across the world, achieving pandemic status.

Initial reports from China, 2,3 followed by the United States 4 and Europe, 5 showed that the prevalence of diabetes was as high as 20% in people hospitalized with COVID-19. Moreover, the epidemiological data also suggested that diabetes is more frequent in people experiencing adverse clinical outcomes. 6 The prevalence of diabetes was high in people experiencing severe disease, and other studies showed higher mortality rates in people with diabetes compared with non-diabetic cohorts. 7 In addition, another study also highlighted the high prevalence of prediabetes in people experiencing severe COVID-19 disease. 8 Previous research has also shown that people with diabetes face an increased risk of infections, which can potentially be explained by impaired phagocytosis via neutrophils, macrophages and monocytes, impaired neutrophil chemotaxis and bactericidal activity, as well as impaired innate cellmediated immunity. 9, 10 Although some observational studies suggest that good glycaemic control may accompany a reduced risk of infectious disease, there is still debate concerning this matter in the literature. 9 During the COVID-19 lockdown phases across various countries, the question arose regarding those population groups that are at a particularly high risk of severe COVID-19 episodes or death because they require special protection; once affected by the disease, rapid risk stratification in people with disturbed glucose metabolism is critical for planning further therapy, as well as studies investigating novel treatment approaches.

Given the high prevalence of diabetes in COVID-19, everyone with diabetes was initially considered to be part of a high-risk population. However, while further research showed an independent impact of diabetes on outcomes in people with SARS-CoV2 infection, 7 it became evident that age and co-morbidities play major roles in unfavourable outcomes. 11 To untangle the contribution of diabetes by itself from associated co-morbidities, the Austrian Diabetes Association initiated a COVID-19 registry in people with diabetes or prediabetes with the aim of identifying those individuals at the greatest risk of lethal disease outcomes when hospitalized with SARS-CoV-2 infection. The objective of the current study is to analyse fatality rates in people with diabetes or prediabetes hospitalized for COVID-19 in Austria and to develop an easily applicable score with which to identify those people at the highest risk of a fatal outcome within this patient population. hospitalized with COVID-19. Diabetes was diagnosed according to the Austrian Diabetes Association. 12 Prediabetes was defined as an HbA1c of 5.7%-6.4% (39-46 mmol/mol). 12 HbA1c was measured in case increased glucose values were evident in people without known diabetes. No specific exclusion criteria were defined.

Data collection was performed by clinical physicians and study coordinators at 10 participating centres across Austria. Data were collected from their medical files and the clinical laboratory.

This study captured and processed data using an electronic case report form developed with HybridForms, as part of a validated electronic data capture system with an audit trail and controlled levels of access (Kapsch BusinessCom and Icomedias, Graz, Austria).

The primary outcome of this study was in-hospital mortality in patients with diabetes and confirmed diagnosis of COVID-19.

Demographic information, clinical characteristics and laboratory findings were collected from the medical record systems of the participating centres. Demographic data consisted of information regarding age and gender. Clinical characteristics included the classification of diabetes, duration of diabetes, microvascular (diabetic retinopathy and diabetic kidney disease) and macrovascular disease (stroke, myocardial infarction, chronic heart disease, arterial occlusive disease [i.e. cerebrovascular or peripheral artery disease]), as well as other co-morbidities of interest (autoimmune disease, cancer, respiratory disease, liver disease, transplantation) and vital signs. Furthermore, current therapy to regulate blood pressure, blood sugar, blood lipids, immunity and pain were recorded. Laboratory data, available from the local laboratory at the clinical site, included HbA1c, fasting glucose, leucocytes, haemoglobin, estimated glomerular filtration rate (eGFR; the Modification of Diet in Renal Disease equation was used at three sites, and at the other sites the Chronic Kidney Disease Epidemiology Collaboration formula was used), high sensitive C-reactive protein (CRP), inflammatory markers, liver function tests, lipid status, procalcitonin, ferritin, interleukine-6, n-terminal pro brain natriuretic peptide (NT-proBNP) and troponin T. The variables recorded in the registry are those taken upon hospital admission.

All statistical analyses were performed in Stata 16.1 (StataCorp, TX, USA). Qualitative variables are presented as frequency and percentage (%) and quantitative variables as mean ± standard deviation (SD) or median and interquartile range (IQR) as appropriate. Chisquare or Fischer exact tests were performed to compare qualitative variables and unpaired t-tests or Mann-Whitney U tests to compare normal and non-normal quantitative variables. A P-value of less than .05 was considered statistically significant. 

The predictive performance of the risk model was assessed in terms of discrimination and calibration. Discrimination was assessed by calculating the C statistics and calibration was assessed by performing the Hosmer-Lameshaw goodness-of-fit test and fitting calibration plots of observed versus expected probability of the in-hospital mortality. 

After generating and validating the risk model, a nomogram was generated from the multivariable logistic regression model using the Stata nomolog package.

T A B L E 1 Comparison and unadjusted odds ratios (95% confidence interval) of characteristics, anthropometric indices, co-morbidities, medications and laboratory variables with in-hospital mortality in patients hospitalized with COVID-19 Abbreviations: ACE, angiotensin-converting enzyme; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AR, angiotensin receptor; BP, blood pressure; CI, confidence interval; CHD, coronary heart disease; CRP, C-reactive protein; HDL, high density lipoprotein; DPP-4, dipeptidyl peptidase-4; eGFR, estimated glomerular filtration rate; GLP-1, glucagon-like peptide-1; IL6, interleukin 6; LDL, low density lipoprotein; NT-proBNP, n-terminal pro brain natriuretic peptide; OR, odds ratio; SGLT-2, sodium-dependent glucose co-transporter-2. were discharged alive. People who died had greater than 4-fold known co-morbidities upon admission (48.3%) compared with those who survived (12.2%; P < .001).

With regard to medication use, no difference was observed for angiotensin-converting enzyme inhibitors, angiotensin receptor blockers or any other glucose-lowering medication between people who survived or died. Loop diuretics and mineralocorticoid receptor blockers were used more frequently in those who died in the hospital.

eGFR was significantly lower in people who died in the hospital (45.0 ± 21.2 vs. 73.1 ± 25.0; P < .001). Moreover, CRP levels were significantly higher in those people admitted to the ICU who died, as were procalcitonin, interleukin-6 levels and NT-proBNP levels. Figure S1 , Figure S2 ).

T A B L E 2 Comparison of characteristics, anthropometric indices, co-morbidities and laboratory variables by diabetes and prediabetes in patients hospitalized with COVID-19 .128

Fasting plasma glucosemg/dL, median (IQR) 187 107 (93.0) 149 (91.0) <.001

HbA1c -%, median (IQR) 174 5.9 (0.3) 6.7 (1.9) <.001

Abbreviations: BP, blood pressure; CHD, coronary heart disease; ICU, intensive care unit; IQR, interquartile range.

In our cohort of people with established diabetes and prediabetes hospitalized with COVID-19 in Austria, in-hospital mortality was as high as 24.4% (Table 1) . We did not observe a statistically significant difference for mortality between people with type 1 diabetes and type 2 diabetes, although the number of people with type 1 diabetes was only 11. Interestingly, the mortality rate in those with prediabetes was numerically lower (14.9%), albeit this was not statistically significant in comparison with people with type 2 diabetes. With the identified predictors for in-hospital mortality, namely, age, the presence of arterial occlusive disease, AST, eGFR and CRP levels upon admission,

we developed a simple clinical score to identify those people at the highest risk of a fatal outcome.

Earlier this year, data on the high prevalence of diabetes in people hospitalized with COVID-19, and in particular severe episodes of the disease, emerged worldwide. [2] [3] [4] [5] Later, more thorough analyses adjusting the results for covariates still identified diabetes as a significant risk factor for fatal outcomes. 7 The CORONADO study included the first large dataset investigating people with diabetes hospitalized for COVID-19 in France. 11 Similar to our study, the authors showed that HbA1c upon admission was not a significant predictor of outcomes in this patient cohort.

Meanwhile, a UK analysis reported a higher mortality rate in people with higher HbA1c levels, both in type 1 and type 2 diabetes. 13 A recent Italian study showed glucose upon admission as a predictor of disease severity and prognosis; however, admission glucose mainly appears to reflect the inflammatory response, rather than the quality of pre-COVID-19 glycaemic control. 14 While other studies have examined recommendations for glucose lowering at home, in the hospital setting or around surgery, our data do not cover this important aspect. 15, 16 In contrast to our data, body mass index was a predictor of mor- the issue of sample size in our study. In addition, the French cohort identified age, obstructive sleep apnoea syndrome, and microvascular and macrovascular complications, as predictors of adverse outcomes.

In terms of the laboratory variables, similar to our findings, AST and CRP were directly, and eGFR inversely, related to mortality. In addition, a recent Chinese study reported CRP as a major predictor of mortality in people with diabetes. 17 In line with the CORONADO data, we also did not find a difference in mortality between people with type 1 and type 2 diabetes who were hospitalized. In a UK NHS dataset, the adjusted odds ratios (for age, sex, deprivation, ethnicity and geographical region) for in-hospital-related COVID-19 mortality were higher in people with type 1 diabetes than in people with type 2 diabetes. 18 Also, in our database, no specific glucose-lowering drug was associated with an increased or reduced risk of in-hospital death. For a clinician, simple and easily applicable risk stratification for patients admitted to the emergency room is helpful for triage and planning further care. Moreover, this risk stratification is also an important tool with which to design clinical trials for therapeutic agents, as it is probable that these will have different effects across different at-risk groups. Therefore, we propose a simple risk score based on age, the presence of arterial occlusive disease, as well as CRP, AST and eGFR levels. With an AUC of more than 0.8, this score looks promising; however, we were only able to validate it internally by using the bootstrapping technique, and it clearly needs external validation before clinical applications can be considered.

One limitation of our study is the sample size of 238 subjects.

However, given that the total population of Austria is less than 9 million people, and the importance of making in-hospital mortality data for people with diabetes accessible to as many countries as possible, these findings are of value. Another limitation is the lack of comparison data for people without diabetes in Austria who were hospitalized with COVID-19. In addition, because of the pragmatic design, we do not have a full dataset consisting of all laboratory variables of interest available in this registry. Hence, we decided to only use those laboratory variables in the risk score model that were available for more than 80% of participants. Sensitivity analyses including further laboratory variables (even where the frequency was <80%) did not substantially change the predictive performance of the score. Given that HbA1c is not routinely measured in all people admitted to hospital, prediabetes was probably underdiagnosed in the overall cohort of people with COVID-19, a matter which requires further investigation.

The strengths of the current study are the data on people with prediabetes and COVID-19, and the idea of summarizing the risk variables into a simple clinical score; however, a limitation related to this is the lack of external validation concerning this score, which is key regarding its potential utility in routine care.

Our data show high in-hospital mortality in people with diabetes and prediabetes in Austria. A simple five-variable risk score could help to identify patients at the greatest risk of fatal outcomes, but this needs further validation in other cohorts.

whole-population study. Lancet Diabetes Endocrinol. 2020;8: 813-822.

Additional supporting information may be found online in the Supporting Information section at the end of this article. 

Sourij and FA are the guarantors of the data

Endocrinology, Diabetology and Metabolic Diseases St. Vinzenz Hospital Zams

Clinical Division for Endocrinology and Diabetology and Metabolic Diseases

Clinic Hietzing, Vienna Health Care Group, Austria: Thomas M. Stulnig, Slobodan Peric, Andreas Zitterl

Diagnosis and treatment of coronavirus disease 2019 (COVID-19): Laboratory, PCR, and chest CT imaging findings

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Clinical characteristics of coronavirus disease 2019 in China

Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area

Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region

Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention

Clinical characteristics and risk factors for mortality of COVID-19 patients with diabetes in Wuhan, China: a two-center, retrospective study

Unrecognized diabetes in critically ill COVID-19 patients

Diabetes and infection: assessing the association with glycaemic control in population-based studies

Infections in patients with diabetes mellitus

Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORO-NADO study

Diabetes mellitus -definition, classification, diagnosis, screening and prevention (update 2019)

Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: a population-based cohort study

Hyperglycemia at hospital admission is associated with severity of the prognosis in patients hospitalized for COVID-19: The Pisa COVID-19 Study

Importance of hyperglycemia in preoperative, intraoperative and postoperative periods in COVID-19 patients

Prevention and management of COVID-19 among patients with diabetes: an appraisal of the literature

Clinical characteristics and outcomes of patients with diabetes and COVID-19 in association with glucose-lowering medication

Associations of type 1 and type 2 diabetes with COVID-19-related mortality

We would like to thank Kapsch Austria, Icomedias, and Microsoft Austria for programming the electronic case report form and the provision of secure data storage space. We thank Andrew Spencer for proofreading the manuscript. The study was supported by unrestricted research grants to the Austrian Diabetes Association from NovoNordisk, Novartis, Sanofi, AstraZeneca and Boehringer Ingelheim. The study funder was not involved in the design of the study; the collection, analysis and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.

This study will be published on https://www.medrxiv.org as a preprint.

The peer review history for this article is available at https://publons. com/publon/10.1111/dom.14256.

The Austrian Diabetes Association has full access to the dataset and access can be granted upon request.

https://orcid.org/0000-0003-3510-9594Alexandra Kautzky-Willer https://orcid.org/0000-0002-3520-4105Norbert Tripolt https://orcid.org/0000-0002-7566-2047