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Details

Autor(en) / Beteiligte
Titel
Haemoglobin A1c is a predictor of COVID‐19 severity in patients with diabetes
Ist Teil von
  • Diabetes/metabolism research and reviews, 2021-07, Vol.37 (5), p.e3398-n/a
Ort / Verlag
Bognor Regis: Wiley Subscription Services, Inc
Erscheinungsjahr
2021
Link zum Volltext
Quelle
Wiley Online Library - AutoHoldings Journals
Beschreibungen/Notizen
  • Aim Poor outcomes of coronavirus disease 2019 (COVID‐19) have been linked to diabetes, but its relation to pre‐infection glycaemic control is still unclear. Materials and Methods To address this question, we report here the association between pre‐infection Haemoglobin A1c (HbA1c) levels and COVID‐19 severity as assessed by need for hospitalization in a cohort of 2068 patients with diabetes tested for COVID‐19 in Leumit Health Services (LHSs), Israel, between 1 February and 30 April 2020. Using the LHS‐integrated electronic medical records system, we were able to collect a large amount of clinical information including age, sex, socio‐economic status, weight, height, body mass index, HbA1c, prior diagnosis of ischaemic heart disease, depression/anxiety, schizophrenia, dementia, hypertension, cerebrovascular accident, congestive heart failure, smoking, and chronic lung disease. Results Of the patients included in the cohort, 183 (8.85%) were diagnosed with COVID‐19 and 46 were admitted to hospital. More hospitalized patients were female, came from higher socio‐economic background and had a higher baseline HbA1c. A prior diagnosis of cerebrovascular accident and chronic lung disease conferred an increased risk of hospitalization but not obesity or smoking status. In a multivariate analysis, controlling for multiple prior clinical conditions, the only parameter associated with a significantly increased risk for hospitalization was HbA1c ≥ 9%. Conclusion Using pre‐infection glycaemic control data, we identify HbA1c as a clear predictor of COVID‐19 severity. Pre‐infection risk stratification is crucial to successfully manage this disease, efficiently allocate resources, and minimize the economic and social burden associated with an undiscriminating approach.

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