PurposeThe aspartate transaminase/alanine transaminase ratio (De Ritis ratio, AAR) was reported to be associated with patients’ prognosis in certain diseases recently. The objective of the current study was to determine the association between the AAR at admission and poor outcome at 3 months in acute ischemic stroke (AIS) patients.Patients and methodsThis retrospective cohort study included patients who experienced their first-ever AIS between June 2015 and March 2016. The primary outcome measure was a poor outcome at 3 months (modified Rankin Scale score >2). Multivariate logistic regression models were used to assess the relationship between AAR quartiles and clinical outcomes among the AIS patients. Receiver operating characteristic curve analysis was applied to identify the optimal cutoff for AAR in predicting the prognosis of AIS.ResultsIn terms of the relationship between poor outcome and AAR, the adjusted odds ratio comparing the highest and lowest AAR quartiles was 2.15 (95% confidence interval =1.14–4.05). An AAR of 1.53 was identified as the optimal cutoff. In a prespecified subgroup analysis according to the time from symptom onset to treatment (>24 vs ≤24 hours), there was no significant difference in the effect of AAR >1.53 between the two groups.ConclusionAn increased AAR at admission is significantly associated with a poor outcome at 3 months in AIS patients.
Objective
To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes.
Materials and Methods
Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses.
Results
Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (
p
> 0.05). The decision curve analysis indicated its clinical usefulness.
Conclusion
The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.