Association between red blood cell distribution width-to-albumin ratio and the prognosis in patients with autoimmune encephalitis: a retrospective cohort study

四分位数 医学 内科学 优势比 置信区间 回顾性队列研究 改良兰金量表 队列 自身免疫性脑炎 免疫学 脑炎 病毒 缺血性中风 缺血
作者
Dan Li,An–Gang Yang,Mingrong Xia,Kai Ma,Jiewen Zhang,Yang Guo,Weizhou Zang
出处
期刊:Frontiers in Neurology [Frontiers Media SA]
卷期号:14
标识
DOI:10.3389/fneur.2023.1276026
摘要

Red blood cell distribution width-to-albumin ratio (RAR) is a combined new indicator reflecting immunology and has been reported to predict the prognosis of inflammation-related diseases and brain diseases. However, the association and predictive value of RAR in the prognosis of patients with autoimmune encephalitis (AE) has not been reported.This was a retrospective cohort study, and data were collected from the Henan Provincial People's Hospital. RAR was categorized according to quartile. The prognosis was assessed using the modified Rankin Scale (mRS), and an mRS score of ≥3 was defined as a poor prognosis. The logistical regression model was used to explore the association between RAR and the prognosis, with results reported as odds ratio (OR) and 95% confidence interval (CI). The predictive value of RAR was evaluated by calculating the area under the receiving operating curve (AUC), sensitivity, specificity, and accuracy.A total of 175 eligible patients were included for analysis, and 51 patients were identified as having poor prognosis. After adjusting age, cancer, other diseases, histological subtype, antiepileptic therapy, anti-tumor treatment, ICU treatment, and length of stay, RAR in the highest quartile (Q4) was found to be significantly associated with the high odds of poor prognosis (OR = 5.63, 95%CI: 1.98-16.02) compared to RAR in the lowest quartile (Q1). In addition, RAR was identified as a predictor for the prognosis of AE patients (AUC = 0.660, 95%CI: 0.574-0.746).This study found the close association and predictive value of RAR for the prognosis of AE patients, indicating that RAR might help clinicians identify high-risk populations.
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