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Serum Occludin as a Biomarker to Predict the Severity of Acute Ischemic Stroke, Hemorrhagic Transformation, and Patient Prognosis.

ABSTRACT: Blood-brain barrier (BBB) damage plays an important role in overall brain injury following acute ischemic stroke (AIS). We investigated the potential utility of serum occludin, a BBB damage biomarker, in predicting the severity of AIS, hemorrhagic transformation (HT) and patient prognosis. A total of 243 patients, suspected of suffering an AIS and admitted to the emergency room at Xuanwu Hospital between November 2018 to March 2019, were enrolled in this study. Serum occludin levels were measured by enzyme linked immunosorbent assay and clinical data were collected from each patient. Receiver operating characteristic curves (ROC) were used to analyze the relationship between serum occludin and AIS. Multiple logistic regression analysis was used to analyze the relationship between serum occludin and stroke prognosis. Serum occludin levels were significantly elevated in acute stroke cases compared with those with stroke-like symptoms (P<0.001). In the moderate and severe cerebral infarction (CI) groups, serum occludin levels were significantly higher than those in the mild CI group (P<0.001). Patients with HT had higher occludin levels than non-HT patients (P<0.05). In addition, serum occludin level of patients with poor prognosis was significantly higher than that of the patients with good prognosis for non-reperfusion therapy. The ROC curve showed that serum occludin could reasonably predict HT and poor prognosis. Moreover, serum occludin were independently associated with 90-day poor prognosis. These findings suggest that the serum occludin levels could be used to identify early acute stroke cases and may predict the severity of AIS and HT as well as the prognosis at 90 days.

SUBMITTER: Li W

PROVIDER: S-EPMC7673856 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Serum Occludin as a Biomarker to Predict the Severity of Acute Ischemic Stroke, Hemorrhagic Transformation, and Patient Prognosis.

Li Weili W Qi Zhifeng Z Kang Huining H Qin Xuzhen X Song Haiqing H Sui Xueqin X Ren Yi Y Ji Xunming X Ma Qingfeng Q Jian Liu Ke K

Aging and disease 20201201 6

Blood-brain barrier (BBB) damage plays an important role in overall brain injury following acute ischemic stroke (AIS). We investigated the potential utility of serum occludin, a BBB damage biomarker, in predicting the severity of AIS, hemorrhagic transformation (HT) and patient prognosis. A total of 243 patients, suspected of suffering an AIS and admitted to the emergency room at Xuanwu Hospital between November 2018 to March 2019, were enrolled in this study. Serum occludin levels were measure ...[more]

PMID: 33269096

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Project description:BackgroundHemorrhagic transformation (HT) is the most serious complication of ischemic stroke patients after intravenous thrombolysis and leads to a poor clinical prognosis. This study aimed to determine the independent predictors associated with HT in stroke patients with intravenous thrombolysis and to establish and validate a nomogram that combines with predictors to predict the probability of HT after intravenous thrombolysis in patients with ischemic stroke.MethodThis study enrolled ischemic stroke patients with intravenous thrombolysis from December 2016 to June 2022. All the patients were divided into training and validation cohorts. The nomogram was composed of the significant predictors for HT in the training cohort as obtained by the multivariate logistic regression analysis. The area under the receiver operating characteristic curve was used to assess the discriminative performance of the nomogram. The calibration performance of the nomogram was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Decision curve analysis was used to test the clinical validity of the nomogram.ResultsA total of 394 patients with intravenous thrombolysis were enrolled in the study. In the training cohort (n = 257), 45 patients had HT after intravenous thrombolysis. Multivariate logistic regression analysis demonstrated early infarct signs (OR, 7.954; 95% CI, 3.553-17.803; P < 0.001), NIHSS scores (OR, 1.110; 95% CI, 1.054-1.168; P < 0.001), uric acid (OR, 0.993; 95% CI, 0.989-0.997; P = 0.001), and albumin-to-globulin ratio (OR, 0.109; 95% CI, 0.023-0.508; P = 0.005) were independent predictors for HT and constructed the nomogram. In the training and validation cohorts, the AUC of the nomogram was 0.859 and 0.839, respectively. The Hosmer-Lemeshow goodness-of-fit test and calibration plot showed good concordance between predicted and observed probability in the training and validation cohorts. Decision curve analysis indicated that the nomogram was significantly useful for predicting HT in the training and further confirmed in the validation cohort.ConclusionThis study proposes a novel and practical nomogram based on early infarct signs, NIHSS scores, uric acid, and albumin-to-globulin ratio that can well predict the probability of HT after intravenous thrombolysis in patients with ischemic stroke.

Project description:ObjectiveTo develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.Materials and methodsA total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.ResultsOf the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.ConclusionThis model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.

Dataset Information

Serum Occludin as a Biomarker to Predict the Severity of Acute Ischemic Stroke, Hemorrhagic Transformation, and Patient Prognosis.

Publications

Serum Occludin as a Biomarker to Predict the Severity of Acute Ischemic Stroke, Hemorrhagic Transformation, and Patient Prognosis.

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