Clinical outcomes and predictive modeling in COVID-19 patients with type 2 diabetes mellitus: a multicenter retrospective cohort study.

This study aimed to assess the influence of type 2 diabetes mellitus (T2DM) on clinical features and adverse outcomes in COVID-19 patients and to develop a predictive model for adverse outcomes in this population.

A retrospective analysis was conducted from December 2022 to February 2023, involving 1058 COVID-19 inpatients at two hospitals. Patients were divided into T2DM (n = 363) and non-T2DM (n = 695) groups. Demographic and laboratory data were collected, and univariate analyses were performed. Logistic regression analysis was employed to identify risk factors associated with ICU admission, and a predictive model was constructed and validated using ROC curves.

T2DM patients exhibited higher levels of certain inflammatory and biochemical markers and a greater incidence of ICU admission compared to non-T2DM patients. Neutrophil count and lactate dehydrogenase were identified as independent risk factors for ICU admission.

T2DM is associated with increased levels of inflammatory and biochemical markers and a higher risk of ICU admission in COVID-19 patients. The predictive model, incorporating neutrophil count and lactate dehydrogenase, offers clinical utility. The study's findings can inform clinical strategies for managing COVID-19 patients with T2DM, particularly in predicting and mitigating adverse outcomes.
Diabetes
Chronic respiratory disease
Diabetes type 2
Access
Care/Management
Advocacy

Authors

Guo Guo, Zhi Zhi, Zhou Zhou, Huang Huang, Lin Lin, Pang Pang, Xiao Xiao, Sun Sun, Zeng Zeng
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