Dataset Information

Risk Factors in Patients with Diabetes Hospitalized for COVID-19: Findings from a Multicenter Retrospective Study.

ABSTRACT:

Methods

In this multicenter retrospective study, patients with COVID-19 in China were included and classified into two groups according to whether they were complicated with diabetes or not. Demographic symptoms and laboratory data were extracted from medical records. Univariable and multivariable logistic regression methods were used to explore the risk factors.

Results

538 COVID-19 patients were finally included in this study, of whom 492 were nondiabetes and 46 were diabetes. The median age was 47 years (IQR 35.0-56.0). And the elderly patients with diabetes were more likely to have dry cough, and the alanine aminotransferase, lactate dehydrogenase, Ca, and mean hemoglobin recovery rate were higher than the other groups. Furthermore, we also found the liver and kidney function of male patients was worse than that of female patients, while female cases should be paid more attention to the occurrence of bleeding and electrolyte disorders. Moreover, advance age, blood glucose, gender, prothrombin time, and total cholesterol could be considered as risk factors for COVID-19 patients with diabetes through the multivariable logistic regression model in our study.

Conclusion

The potential risk factors found in our study showed a major piece of the complex puzzle linking diabetes and COVID-19 infection. Meanwhile, focusing on gender and age factors in COVID-19 patients with or without diabetes, specific clinical characteristics, and risk factors should be paid more attention by clinicians to figure out a targeted intervention to improve clinical efficacy worldwide.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7847355 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Risk Factors in Patients with Diabetes Hospitalized for COVID-19: Findings from a Multicenter Retrospective Study.

Zhang Yili Y Wang Juan J Tan Nannan N Du KangJia K Gao Kuo K Zuo Jiacheng J Lu Xiaoguang X Ma Yan Y Hou Yong Y Li Quntang Q Xu Hongming H Huang Jin J Huang Qiuhua Q Na Hui H Wang Jingwei J Wang Xiaoyan X Xiao Yanhua Y Zhu Junteng J Chen Hong H Liu Zhang Z Wang Mingxuan M Zhang Linsong L Guo Shuzhen S Wang Wei W

Journal of diabetes research 20210115

<h4>Methods</h4>In this multicenter retrospective study, patients with COVID-19 in China were included and classified into two groups according to whether they were complicated with diabetes or not. Demographic symptoms and laboratory data were extracted from medical records. Univariable and multivariable logistic regression methods were used to explore the risk factors.<h4>Results</h4>538 COVID-19 patients were finally included in this study, of whom 492 were nondiabetes and 46 were diabetes. T ...[more]

PMID: 33553435

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Project description:BackgroundType 2 diabetes (T2D) as a worldwide chronic disease combined with the COVID-19 pandemic prompts the need for improving the management of hospitalized COVID-19 patients with preexisting T2D to reduce complications and the risk of death. This study aimed to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D patients and build an individualized risk prediction nomogram for risk stratification and early clinical intervention to reduce mortality.MethodsIn this retrospective study, the clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, China, were collected and analyzed. Univariate and multivariate Cox regression models were performed to identify specific clinical factors associated with mortality of COVID-19 patients with T2D. An individualized risk prediction nomogram was developed and evaluated by discrimination and calibration.ResultsNearly 15% (16/108) of hospitalized COVID-19 patients with T2D died. Twelve risk factors predictive of mortality were identified. Older age (HR = 1.076, 95% CI = 1.014-1.143, p=0.016), elevated glucose level (HR = 1.153, 95% CI = 1.038-1.28, p=0.0079), increased serum amyloid A (SAA) (HR = 1.007, 95% CI = 1.001-1.014, p=0.022), diabetes treatment with only oral diabetes medication (HR = 0.152, 95%CI = 0.032-0.73, p=0.0036), and oral medication plus insulin (HR = 0.095, 95%CI = 0.019-0.462, p=0.019) were independent prognostic factors. A nomogram based on these prognostic factors was built for early prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index (C-index) was achieved, and the calibration curves showed the model had good prediction ability within three weeks of COVID-19 onset.ConclusionsBy incorporating specific prognostic factors, this study provided a user-friendly graphical risk prediction tool for clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.

Dataset Information

Risk Factors in Patients with Diabetes Hospitalized for COVID-19: Findings from a Multicenter Retrospective Study.

Methods

Results

Conclusion

Publications

Risk Factors in Patients with Diabetes Hospitalized for COVID-19: Findings from a Multicenter Retrospective Study.

Similar Datasets

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