首页|Beijing Hospital Reports Findings in Stroke (Machine learning models reveal the critical role of nighttime systolic blood pressure in predicting functional outc ome for acute ischemic stroke after endovascular thrombectomy)
Beijing Hospital Reports Findings in Stroke (Machine learning models reveal the critical role of nighttime systolic blood pressure in predicting functional outc ome for acute ischemic stroke after endovascular thrombectomy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Cerebrovascular Diseases and Cond itions - Stroke is the subject of a report. According to news reporting originat ing from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Blood pressure (BP) is a key factor for the clinical outcomes of acute ischemic stroke (AIS) receiving endovascular thrombectomy (EVT). However, the e ffect of the circadian pattern of BP on functional outcome is unclear.” Our news editors obtained a quote from the research from Beijing Hospital, “This multicenter, retrospective, observational study was conducted from 2016 to 2023 at three hospitals in China (ChiCTR2300077202). A total of 407 patients who und erwent endovascular thrombectomy (EVT) and continuous 24-h BP monitoring were in cluded. Two hundred forty-one cases from Beijing Hospital were allocated to the development group, while 166 cases from Peking University Shenzhen Hospital and Hainan General Hospital were used for external validation. Postoperative systoli c BP (SBP) included daytime SBP, nighttime SBP, and 24-h average SBP. Least abso lute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), Boruta were used to screen for potential features associated with functional dependence defined as 3-month modified Rankin scale (mRS) score 3. Nine algorithms were applied for model construction and evaluated using area under the receiver operating characteristic curve (AUC), sensitivity , specificity, and accuracy. Three hundred twenty-eight of 407 (80.6 % ) patients achieved successful recanalization and 182 patients (44.7% ) were functional independent.NIHSS at onset, modified cerebral infarction thro mbolysis grade, atrial fibrillation, coronary atherosclerotic heart disease, hyp ertension were identified as prognostic factors by the intersection of three alg orithms to construct the baseline model. Compared to daytime SBP and 24-h SBP mo dels, the AUC of baseline + nighttime SBP showed the highest AUC in all algorith ms. The XGboost model performed the best among all the algorithms. ROC results s howed an AUC of 0.841 in the development set and an AUC of 0.752 in the validati on set for the baseline plus nighttime SBP model, with a brier score of 0.198. T his study firstly explored the association between circadian BP patterns with fu nctional outcome for AIS.”
BeijingPeople’s Republic of ChinaAsi aAlgorithmsAngiologyBlood PressureCerebrovascular Diseases and Condition sCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and Medicin eHospitalsMachine LearningStrokeSurgerySystolic Blood PressureThromb ectomy
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