Acta medica Iranica · 2012
Cerebral sinus thrombosis in scleroderma: a case report.
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Li Zhi, Liang Jiamin, Lu Katie, Tang Shuyu, Huang Jinyi, Zhang Jinrong, Zou Jianjun, Huang Dongsheng, Xie Chenli, Zeng Linglong, Wang Zhiwei, Deng Yibin, Lu Jiachun
Аннотация
BACKGROUND: In 2020, COVID-19 posed a major threat to global public health in a remarkably short period. Although the WHO declared an end to the emergency phase in May 2023, a considerable proportion of recovered cases experience medium- and long-term effects, which pose ongoing health challenges to society. Therefore, it remains necessary to conduct relevant research in the post-epidemic era to explore the risk factors for death in COVID-19 inpatients.
METHODS: We determined the mortality of COVID-19 inpatients with pneumonia manifestations through one-year follow-up, utilizing real-world data from three medical centers. Clinical characteristics associated with mortality risk were analyzed by logistic regression. Then, the dataset was randomly partitioned into three sets at a ratio of 4:2:4. Three machine learning algorithms were employed to develop and validate a mortality risk predictive model for COVID-19 inpatients, and a web-based visualization tool was created.
RESULTS: There were 100 fatalities among the 1,693 samples included in this study. Meanwhile, we identified 37 factors correlated with increased mortality risk in COVID-19 inpatients with pneumonia manifestations. Ultimately, we developed a mortality risk predictive model using the random forest algorithm, which demonstrated superior predictive performance (AUC=0.907, 95% CI=0.849-0.957).
CONCLUSIONS: This study reports a mortality rate of 5.9% for COVID-19 inpatients with pneumonia manifestations. The high-performance mortality risk prediction model obtained in this study provides important practical guidance for monitoring the mortality risks of COVID-19 inpatients with pneumonia manifestations.
METHODS: We determined the mortality of COVID-19 inpatients with pneumonia manifestations through one-year follow-up, utilizing real-world data from three medical centers. Clinical characteristics associated with mortality risk were analyzed by logistic regression. Then, the dataset was randomly partitioned into three sets at a ratio of 4:2:4. Three machine learning algorithms were employed to develop and validate a mortality risk predictive model for COVID-19 inpatients, and a web-based visualization tool was created.
RESULTS: There were 100 fatalities among the 1,693 samples included in this study. Meanwhile, we identified 37 factors correlated with increased mortality risk in COVID-19 inpatients with pneumonia manifestations. Ultimately, we developed a mortality risk predictive model using the random forest algorithm, which demonstrated superior predictive performance (AUC=0.907, 95% CI=0.849-0.957).
CONCLUSIONS: This study reports a mortality rate of 5.9% for COVID-19 inpatients with pneumonia manifestations. The high-performance mortality risk prediction model obtained in this study provides important practical guidance for monitoring the mortality risks of COVID-19 inpatients with pneumonia manifestations.