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. 2020 Dec 20;33(12):893-905.
doi: 10.3967/bes2020.123.

Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China

Yan Ma  1 , Dong Shan Zhu  2 , Ren Bo Chen  1 , Nan Nan Shi  1 , Si Hong Liu  3 , Yi Pin Fan  1 , Gui Hui Wu  4 , Pu Ye Yang  5 , Jiang Feng Bai  6 , Hong Chen  7 , Li Ying Chen  8 , Qiao Feng  9 , Tuan Mao Guo  10 , Yong Hou  11 , Gui Fen Hu  12 , Xiao Mei Hu  13 , Yun Hong Hu  14 , Jin Huang  15 , Qiu Hua Huang  15 , Shao Zhen Huang  16 , Liang Ji  17 , Hai Hao Jin  18 , Xiao Lei  19 , Chun Yan Li  20 , Min Qing Li  21 , Qun Tang Li  22 , Xian Yong Li  23 , Hong De Liu  24 , Jin Ping Liu  25 , Zhang Liu  26 , Yu Ting Ma  27 , Ya Mao  28 , Liu Fen Mo  29 , Hui Na  30 , Jing Wei Wang  30 , Fang Li Song  31 , Sheng Sun  32 , Dong Ting Wang  33 , Ming Xuan Wang  34 , Xiao Yan Wang  35 , Yin Zhen Wang  36 , Yu Dong Wang  37 , Wei Wu  38 , Lan Ping Wu  39 , Yan Hua Xiao  40 , Hai Jun Xie  41 , Hong Ming Xu  42 , Shou Fang Xu  43 , Rui Xia Xue  44 , Chun Yang  45 , Kai Jun Yang  46 , Sheng Li Yuan  47 , Gong Qi Zhang  48 , Jin Bo Zhang  49 , Lin Song Zhang  50 , Shu Sen Zhao  51 , Wan Ying Zhao  52 , Kai Zheng  53 , Ying Chun Zhou  54 , Jun Teng Zhu  55 , Tian Qing Zhu  56 , Hua Min Zhang  57 , Yan Ping Wang  1 , Yong Yan Wang  1 ; 'lung cleansing & detoxifying decoction' emergency project expert group for covid-19
Affiliations

Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China

Yan Ma et al. Biomed Environ Sci. .

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.

Keywords: Age; COVID-19; Comorbidities; Gender; Severity; Treatment outcome.

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