Low-density lipoprotein cholesterol levels are positively associated with the risk of endobronchial biopsy-induced refractory hemorrhage in patients with lung cancer

Abstract

Background: Lipoprotein concentrations have been associated with the major risk of bleeding events. However, whether plasma levels of LDL-C are associated with the risk of biopsy-related endobronchial hemorrhage remain elusive. Therefore, the present study was initiated to investigate the explicit association of low-density lipoprotein cholesterol (LDL-C) with endobronchial biopsy (EBB)-induced refractory hemorrhage in patients with lung cancer. Methods: This retrospective study included a total of 659 consecutive patients with lung cancer who had undergone EBB at a tertiary hospital between January 2014 and April 2018. Using multiple regression analysis, the association between LDL-C and the risk of EBB-induced refractory hemorrhage was assessed after adjusting for potential confounding factors. Results: A significant proportion (13.8%, 91/659) of the patients experienced refractory hemorrhage following EBB. In multivariate regression analysis, higher plasma LDL-C concentrations were associated with increased risk of EBBinduced refractory hemorrhage in patients with lung cancer after adjusting for potential confounders (P < 0.05). Using the lowest quartile of plasma LDL-C as the reference group, the odds ratio (95% confidence interval) of Q2, Q3, and Q4 were 2.32 (1.07, 5.03), 2.37 (0.94, 5.95), and 3.65 (1.16, 11.51), respectively (P for trend < 0.05). Moreover, this association was noticeably more pronounced in male patients with lung cancer in the subgroup analysis (P < 0.05). Conclusions: Plasma LDL-C was positively correlated with the increased risk of EBB-induced refractory hemorrhage in patients with lung cancer; predominantly, the associated risk was more pronounced in male patients with lung cancer.

Association between peripheral lymphocyte count and the mortality risk of COVID-19 inpatients

“…The Kaplan–Meier curve was used to show the death of COVID-19 patients in two lymphocyte groups (i.e., above or below the infection point of PLC) within a month, and the log-rank test was used to assess whether there was a statistical significant difference in mortality between the two groups. Strategies for adjusting confounding variables were as follows [ 10 ]: Strategy I—determining the variables (age and gender) that needed to be adjusted based on clinical significance; Strategy II—including the variable in Strategy I, and variables that adding the covariate to the basic model or removing the covariate from the complete model affected the regression coefficient of “X (lymphocytes)” > 10% as well as variables whose regression coefficient for “Y (death of COVID-1)” had P < 0.1. By calculating the area under the curve (AUC) to evaluate the discrimination ability of PLCs in predicting the death of COVID-19, a nomogram was conducted to display the predictive model.…”