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Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression - 13/07/21

Doi : 10.1016/j.ajem.2020.08.090 
Zeqiang Linli, PhD a, b, 1, Yinyin Chen, PhD c, d, e, 1, Guoliang Tian, PhD f, Shuixia Guo, PhD a, b, , Yu Fei, PhD g, ⁎⁎
a MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China 
b Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China 
c Hunan Provincial People's Hospital, Hunan Normal University, Changsha, PR China 
d Changsha Clinical Research Center for Kidney Disease, Changsha, PR China 
e Hunan Clinical Research Center for Chronic Kidney Disease, Changsha, PR China 
f Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, PR China 
g School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, PR China 

Corresponding author at: School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China.School of Mathematics and StatisticsHunan Normal UniversityChangshaPR China⁎⁎Corresponding author.

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Abstract

Objective

Many laboratory indicators form a skewed distribution with outliers in critically ill patients with COVID-19, for which robust methods are needed to precisely determine and quantify fatality risk factors.

Method

A total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID-19 were included in the sample. Quantile regression was used to determine discrepant laboratory indexes between survivors and non-survivors and quantile shift (QS) was used to quantify the difference. Logistic regression was then used to calculate the odds ratio (OR) and the predictive power of death for each risk indicator.

Results

After adjusting for multiple comparisons and controlling numerous confounders, quantile regression revealed that the laboratory indexes of non-survivors were significantly higher in C-reactive protein (CRP; QS = 0.835, p < .001), white blood cell counts (WBC; QS = 0.743, p < .001), glutamic oxaloacetic transaminase (AST; QS = 0.735, p < .001), blood glucose (BG; QS = 0.608, p = .059), fibrin degradation product (FDP; QS = 0.730, p = .080), and partial pressure of carbon dioxide (PCO2), and lower in oxygen saturation (SO2; QS = 0.312, p < .001), calcium (Ca2+; QS = 0.306, p = .073), and pH. Most of these indexes were associated with an increased fatality risk, and predictive for the probability of death. Especially, CRP is the most prominent index with and odds ratio of 205.97 and predictive accuracy of 93.2%.

Conclusion

Laboratory indexes provided reliable information on mortality in critically ill patients with COVID-19, which might help improve clinical prediction and treatment at an early stage.

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Keywords : COVID-19, Mortality, Laboratory Indicator, Risk factor, Quantile regression


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