Ontology highlight
ABSTRACT: Objective
Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear.Methods
A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( OR) and 95% confidence interval (95% CI) of the associations between comorbidities (cardiometabolic or non-cardiometabolic diseases), clinical severity, and treatment outcomes of COVID-19.Results
Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks.Conclusion
Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.
SUBMITTER: Ma Y
PROVIDER: S-EPMC7817475 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Ma Yan Y Zhu Dong Shan DS Chen Ren Bo RB Shi Nan Nan NN Liu Si Hong SH Fan Yi Pin YP Wu Gui Hui GH Yang Pu Ye PY Bai Jiang Feng JF Chen Hong H Chen Li Ying LY Feng Qiao Q Guo Tuan Mao TM Hou Yong Y Hu Gui Fen GF Hu Xiao Mei XM Hu Yun Hong YH Huang Jin J Huang Qiu Hua QH Huang Shao Zhen SZ Ji Liang L Jin Hai Hao HH Lei Xiao X Li Chun Yan CY Li Min Qing MQ Li Qun Tang QT Li Xian Yong XY Liu Hong De H Liu Jin Ping JP Liu Zhang Z Ma Yu Ting YT Mao Ya Y Mo Liu Fen LF Na Hui H Wang Jing Wei JW Song Fang Li FL Sun Sheng S Wang Dong Ting DT Wang Ming Xuan MX Wang Xiao Yan XY Wang Yin Zhen YZ Wang Yu Dong YD Wu Wei W Wu Lan Ping LP Xiao Yan Hua YH Xie Hai Jun HJ Xu Hong Ming HM Xu Shou Fang SF Xue Rui Xia RX Yang Chun C Yang Kai Jun KJ Yuan Sheng Li SL Zhang Gong Qi GQ Zhang Jin Bo JB Zhang Lin Song LS Zhao Shu Sen SS Zhao Wan Ying WY Zheng Kai K Zhou Ying Chun YC Zhu Jun Teng JT Zhu Tian Qing TQ Zhang Hua Min HM Wang Yan Ping YP Wang Yong Yan YY
Biomedical and environmental sciences : BES 20201201 12
<h4>Objective</h4>Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear.<h4>Methods</h4>A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logist ...[more]