| Literature DB >> 32756123 |
Yong Zhao1, Shun Yi Feng, Yong Li.
Abstract
Paraquat (PQ) poisoning is associated with high mortality rate. Therefore, an accurate method for predicting the survival of patients with PQ poisoning is required. This study evaluated the value of serum anion gap (AG) at admission in predicting the survival of such patients.Cases of patients with PQ poisoning admitted to Cangzhou Central Hospital between May 2012 and March 2019 were retrospectively analyzed. The patients were classified into survival and nonsurvival groups on the basis of their 90-day prognosis. Correlation analysis, Cox regression analysis, and receiver operating characteristic and Kaplan-Meier curve analyses were performed to assess the value of AG in predicting the 90-day survival of patients with PQ poisoning.Only 44 of the 108 patients with PQ poisoning survived; thus, the 90-day survival was 40.74%. AG levels at admission were significantly higher in nonsurvivors (26.53 ± 4.93 mmol/L) than in survivors (20.88 ± 2.74 mmol/L) (P < .001) and negatively correlated with 90-day survival (r = -0.557; P < .001). Cox regression analysis revealed that AG at admission is an independent prognostic marker of the 90-day survival of patients with PQ poisoning. AG level at admission had an area under the receiver operating characteristic curve of 0.836 (95% confidence interval: 0.763-0.909) and an optimal cut-off value of 25.5 mmol/L (59.4% sensitivity and 95.5% specificity).AG level at admission may serve as a candidate marker for predicting the survival of patients with PQ poisoning.Entities:
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Year: 2020 PMID: 32756123 PMCID: PMC7402740 DOI: 10.1097/MD.0000000000021351
Source DB: PubMed Journal: Medicine (Baltimore) ISSN: 0025-7974 Impact factor: 1.817
Clinical characteristics and biologic data at admission of the nonsurvival and survival groups.
Clinical characteristics and biologic data stratified on the basis of AG level at admission.
Cox regression model.
ROC curve analysis.
Figure 1Area under the receiver operating characteristic curve analysis.
Figure 2Kaplan–Meier curve analysis of survival curves for the groups stratified according to anion gap level at admission.