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

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 labo

SUBMITTER: Linli Z 

PROVIDER: S-EPMC7467869 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature