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Published on: September 24, 2020
Risk factors for mortality due to COVID-19 in intensive care units: a single-center study
Yu Chen1, Zhengyin Liu2, Xiaogang Li3
1Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
Insights
Identifying risk factors for coronavirus disease 2019 (COVID-19) death in critically ill patients is crucial. A predictive model combining Interleukin-6 (IL-6) and D-dimer shows excellent performance for forecasting COVID-19 mortality.
Area of Science:
- Critical care medicine
- Infectious diseases
- Biomarker research
Background:
- Prognostic risk factors for coronavirus disease 2019 (COVID-19) are known, but factors predicting death in critically ill patients require further elucidation.
- This study aimed to identify predictors of mortality in intensive care unit (ICU) patients with severe COVID-19.
Purpose of the Study:
- To identify clinical and laboratory risk factors associated with mortality in critically ill COVID-19 patients.
- To develop and evaluate a predictive model for COVID-19 death using ICU patient data.
Main Methods:
- Retrospective analysis of clinical data and laboratory tests from 92 ICU patients with COVID-19.
- Utilized a random forest classifier and Receiver Operating Characteristic (ROC) curve analysis to develop a predictive model.
- Assessed the predictive performance of individual biomarkers (IL-6, D-dimer, lymphocytes, albumin) and their combinations.
Main Results:
- Non-survivors frequently presented with dyspnea (73.8%).
- Biomarkers IL-6, D-dimer, lymphocytes, and albumin demonstrated significant predictive value for mortality (AUCs ranging from 0.8994 to 0.9476).
- A combined model of IL-6 and D-dimer achieved excellent predictive performance (AUC = 0.997).
Conclusions:
- Mortality is a significant concern in critically ill COVID-19 patients.
- A predictive model integrating IL-6 and D-dimer effectively forecasts COVID-19 mortality.
- Further optimization and multicenter validation are recommended for the predictive model.
Background:
Many studies have revealed several risk factors associated with the prognosis of patients with coronavirus disease 2019 (COVID-19), but the risk factors associated with death in critically ill COVID-19 patients still needs to be fully elucidated. Therefore, we analyzed clinical characteristics and laboratory data of ICU patients to identify risk factors associated with COVID-19 death.
Methods:
Patients with COVID-19 from the ICU in the Sino-French New City Branch of Tongji Hospital Wuhan, China, between February 4 and February 29, 2020, were enrolled in this study. The final date of follow-up was April 4, 2020. Clinical manifestations, laboratory tests, treatment, and outcome of participants before and during the ICU stay were retrospectively collected and analyzed.
Results:
A total of 92 patients were admitted or transferred to the ICU from February 4 to February 29, 2020. Compared to survivors, the majority of non-survivors (73.8%) presented with dyspnea. A random forest classifier and ROC curve were used to develop a predictive model. IL-6, D-dimer, lymphocytes, and albumin achieved good performance with AUCs of 0.9476, 0.9165, 0.8994, and 0.9251, respectively, which were consistent with clinical observations, such as inflammation, lymphopenia, and coagulation dysfunction. Combining IL-6 and D-dimer improved the performance of this model with an excellent AUC (0.997).
Conclusions:
Mortality in COVID-19 was not rare in critically ill patients. The model that combined IL-6 and D-dimer was valuable for predicting the mortality of patients with COVID-19 with excellent performance. This model needs to be further optimized by adding more indicators and then evaluated with a multicenter study.
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