首页|Beijing Hospital Reports Findings in Stroke (Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning)
Beijing Hospital Reports Findings in Stroke (Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning)
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New research on Cerebrovascular Diseases and Conditions - Stroke is the subject of a report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy. Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited.” The news correspondents obtained a quote from the research from Beijing Hospital, “Unfavorable outcome was defined as 3-month modified Rankin Scale (mRS) score 3-6. The independent risk factors associated with unfavorable outcome were obtained by regression analysis and included in the prediction model. The performance of nomogram was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). ML models were compared with nomogram using AUC; the generalizability of all models was ascertained in an external cohort. A total of 505 patients were enrolled, with 256 in the model construction, and 249 in the external validation. Five variables were identified as prognostic factors: baseline NIHSS, D-dimer level, random blood glucose (RBG), blood urea nitrogen (BUN), and systolic blood pressure (SBP) before reperfusion. The AUC values of nomogram were 0.865, 0.818, and 0.779 in the training set, test set, and external validation, respectively. The calibration curve and DCA indicated appreciable reliability and good net benefits. The best three ML models were extra trees (ET), CatBoost, and random forest (RF) models; all of them showed favorable discrimination in the training cohort, and confirmed in the test and external sets. Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients.”
BeijingPeople’s Republic of ChinaAsiaBlood TransfusionCerebrovascular Diseases and ConditionsCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMedical DevicesReperfusionRisk and PreventionStrokeTherapyTransfusion Medicine
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