RESUMEN
Objective: Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS. Methods: In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models. Results: Of the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 (N = 401), patients were relatively young and obese and had significantly elevated levels of lipids. Phenotype 2 (N = 463) was associated with abnormal ion levels. Phenotype 3 (N = 45) was characterized by the highest level of inflammation, accompanied by mild multiple-organ dysfunction. The external validation cohort prospectively collected 507 AIS patients [median age 60 (IQR: 18) years, 70% male]. Phenotype characteristics were similar in the validation cohort. After LASSO analysis, Clinlabomics models of phenotype 1 and 2 were constructed by the SVM algorithm, yielding high AUC (0.977, 95% CI: 0.961-0.993 and 0.984, 95% CI: 0.971-0.997), accuracy (0.936, 95% CI: 0.922-0.956 and 0.952, 95% CI: 0.938-0.972), sensitivity (0.984, 95% CI: 0.968-0.998 and 0.958, 95% CI: 0.939-0.984), and specificity (0.892, 95% CI: 0.874-0.926 and 0.945, 95% CI: 0.923-0.969). Conclusion: In this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.
RESUMEN
Objective: In this study, serum markers of acute ischemic stroke (AICS) with carotid artery plaque were retrospectively evaluated to establish a basis for discovering serological indicators for early warning of acute ischemic stroke (AICS). Methods: A total of 248 patients with AICS were enrolled in Lanzhou University Second Hospital from January 2019 to December 2020. The study population included 136 males and 112 females, 64 ± 11 years of age. Of these, there were 90 patients with a transient ischemic attack (TIA), including 60 males and 30 females, aged 64 ± 8 years old. Patients with AICS were stratified by carotid ultrasound into a plaque group (n = 154) and a non-plaque group (n = 94). A total of 160 healthy subjects were selected as the control group. Serum lipoprotein-associated phospholipase A2 (Lp-PLA2), amyloid A (SAA), immunoglobulin E (IgE), D-dimer (D-D), total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) were collected from all subjects. Multivariate logistic regression was used to analyze the risk factors of AICS with carotid plaque. ROC curve was used to analyze the diagnostic efficacy of AICS with carotid plaque. Results: The IgE, Lp-PLA2, SAA, LDL-C, TC, TG, and D-D levels in the AICS group were higher than those in the TIA group and healthy control group (P < 0.05). The IgE level was significantly higher than that in the healthy control group and TIA group. The IgE level in the AICS plaque group was significantly higher than that in the AICS non-plaque group (P < 0.01), and the Lp-PLA2 level was also different (P < 0.05). The incidence of AICS was positively correlated with Lp-PLA2, TC, IgE, TG, D-D, SAA and LDL-C (r = 0.611, 0.499, 0.478, 0.431, 0.386, 0.332, 0.280, all P < 0.05). The incidence of AICS with plaque was only positively correlated with IgE and Lp-PLA2 (r = 0.588, 0.246, P < 0.05). Logistic regression analysis showed that IgE and Lp-PLA2 were independent risk factors for predicting the occurrence of AICS with carotid plaque (P < 0.05). ROC curve analysis showed that the AUC of IgE (0.849) was significantly higher than other indicators; its sensitivity and specificity were also the highest, indicating that IgE can improve the diagnostic efficiency of AICS with carotid plaque. Conclusion: IgE is a serum laboratory indicator used to diagnose AICS disease with carotid plaque, which lays a foundation for further research on potential early warning indicators of AICS disease.