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

Zeqiang Linli, Yinyin Chen, Guoliang Tian, Shuixia Guo, Yu Fei, Zeqiang Linli, Yinyin Chen, Guoliang Tian, Shuixia Guo, Yu Fei

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.

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

Conflict of interest statement

Declaration of Competing Interest All authors declare no competing interests.

Copyright © 2020 Elsevier Inc. All rights reserved.

Figures

Fig. 1
Fig. 1
Kernel density plot of 30 laboratory indexes between the two groups. Each subgraph presented a separate laboratory indicator. Non-survivors were represented in Red; survivors were represented in Blue. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
(A) Beta weights of laboratory indicators that differed significantly between the two groups. (B) QS effect size of the significant indicators. Significant QS values indicate that we can reject the null hypothesis of equal distribution. QS values indicate the quantile to which the distribution has shifted from the population median (0.5). (C) The odds ratio of death by laboratory indicators: Single-index Models. “High” represented odds ratio of death by indicator values more than reference range, “Low” was similar meaning. (D) The average predictive power of indicators across 10 iterations. To be more cautious and clinically significant, subjects with a probability of fatality of more than 0.3 were deemed non-survivors in the prediction procedures.

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