| 1 | Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China显示文摘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(T2 DM)(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. T2 DM(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. | MA Yan ZHU Dong Shan CHEN Ren Bo SHI Nan Nan LIU Si Hong FAN Yi Pin WU Gui Hui YANG Pu Ye BAI Jiang Feng CHEN Hong CHEN Li Ying FENG Qiao GUO Tuan Mao HOU Yong HU Gui Fen HU Xiao Mei HU Yun Hong HUANG Jin HUANG Qiu Hua HUANG Shao Zhen JI Liang JIN Hai Hao LEI Xiao LI Chun Yan LI Min Qing LI Qun Tang LI Xian Yong LIU Hong De LIU Jin Ping LIU Zhang MA Yu Ting MAO Ya MO Liu Fen NA Hui WANG Jing Wei SONG Fang Li SUN Sheng WANG Dong Ting WANG Ming Xuan WANG Xiao Yan WANG Yin Zhen WANG Yu Dong WU Wei WU Lan Ping XIAO Yan Hua XIE Hai Jun XU Hong Ming XU Shou Fang XUE Rui Xia YANG Chun YANG Kai Jun YUAN Sheng Li ZHANG Gong Qi ZHANG Jin Bo ZHANG Lin Song ZHAO Shu Sen ZHAO Wan Ying ZHENG Kai ZHOU Ying Chun ZHU Jun Teng ZHU Tian Qing ZHANG Hua Min WANG Yan Ping WANG Yong Yan 无 | 2020 | Biomedical and Environmental Sciences2020，33，12： | 8 |
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