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Dynamic Prediction of Mechanical Thrombectomy Outcome for Acute Ischemic Stroke Patients Using Machine Learning.

ABSTRACT: The unfavorable outcome of acute ischemic stroke (AIS) with large vessel occlusion (LVO) is related to clinical factors at multiple time points. However, predictive models used for dynamically predicting unfavorable outcomes using clinically relevant preoperative and postoperative time point variables have not been developed. Our goal was to develop a machine learning (ML) model for the dynamic prediction of unfavorable outcomes. We retrospectively reviewed patients with AIS who underwent a consecutive mechanical thrombectomy (MT) from three centers in China between January 2014 and December 2018. Based on the eXtreme gradient boosting (XGBoost) algorithm, we used clinical characteristics on admission ("Admission" Model) and additional variables regarding intraoperative management and the postoperative National Institute of Health stroke scale (NIHSS) score ("24-Hour" Model, "3-Day" Model and "Discharge" Model). The outcome was an unfavorable outcome at the three-month mark (modified Rankin scale, mRS 3-6: unfavorable). The area under the receiver operating characteristic curve and Brier scores were the main evaluating indexes. The unfavorable outcome at the three-month mark was observed in 156 (62.0%) of 238 patients. These four models had a high accuracy in the range of 75.0% to 87.5% and had a good discrimination with AUC in the range of 0.824 to 0.945 on the testing set. The Brier scores of the four models ranged from 0.122 to 0.083 and showed a good predictive ability on the testing set. This is the first dynamic, preoperative and postoperative predictive model constructed for AIS patients who underwent MT, which is more accurate than the previous prediction model. The preoperative model could be used to predict the clinical outcome before MT and support the decision to perform MT, and the postoperative models would further improve the predictive accuracy of the clinical outcome after MT and timely adjust therapeutic strategies.

SUBMITTER: Hu Y

PROVIDER: S-EPMC9313360 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and purposeOutcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0-2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.MethodsWe retrospectively analyzed patients with AIS and LVO undergoing MT from 2009 to 2018. Prognostic variables were grouped in baseline clinical (A), MRI-derived variables including mismatch [apparent diffusion coefficient (ADC) and time-to-maximum (Tmax) lesion volume] (B), and variables reflecting speed and extent of reperfusion (C) [modified treatment in cerebral ischemia (mTICI) score and time from onset to mTICI]. Three different scenarios were analyzed: (1) baseline clinical parameters only, (2) baseline clinical and MRI-derived parameters, and (3) all baseline clinical, imaging-derived, and reperfusion-associated parameters. For each scenario, we assessed prediction for favorable and poor outcome with seven different machine learning algorithms.ResultsIn 210 patients, prediction of favorable outcome was improved after including speed and extent of recanalization [highest area under the curve (AUC) 0.73] compared to using baseline clinical variables only (highest AUC 0.67). Prediction of poor outcome remained stable by using baseline clinical variables only (highest AUC 0.71) and did not improve further by additional variables. Prediction of favorable and poor outcomes was not improved by adding MR-mismatch variables. Most important baseline clinical variables for both outcomes were age, National Institutes of Health Stroke Scale, and premorbid mRS.ConclusionsOur results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance.

Project description:BackgroundEosinophils contribute to antibacterial defense by releasing mitochondrial DNA, which are decreased in patients with acute ischemic stroke (AIS). However, the impact of eosinophils on stroke-associated pneumonia (SAP) among patients with AIS remains unclear. Moreover, whether SAP is in the path of the association between eosinophils and clinical outcomes also remains unclear. We aimed to assess the relationships between eosinophils, SAP, and clinical outcome after mechanical thrombectomy in patients with AIS.MethodsA total of 328 consecutive patients with AIS who underwent mechanical thrombectomy between May 2017 and March 2021 were analyzed. Their baseline data and peripheral eosinophil counts were recorded on admission. Regression analysis was used to assess the effect of eosinophils on SAP, and its effect on poor outcome is defined as a modified Rankin Scale score of 3-6 at month 3 after admission. Mediation analysis was utilized to assess the proportion of the total effect of SAP on the association between eosinophils and poor outcomes.ResultsMultivariate analysis revealed that eosinophils was independently associated with SAP after adjusting for potential confounders (odds ratio, 0.00; 95% CI, 0.00-0.38; P = 0.0267), which are consistent with the result of eosinophils (dichotomous) as a categorical variable (odds ratio, 0.54; 95% CI, 0.31-0.96; P = 0.0342). A non-linear relationship was detected between eosinophils and SAP, whose inflection point was 0.06. Subgroup analyses further confirmed these associations. Eosinophils were also associated with poor outcomes (odds ratio, 0.00; 95% CI, 0.00-0.14; P = 0.0124). Additionally, mediation analysis found that SAP partially mediated the negative relationship between eosinophils and poor outcome (indirect effect = -0.169; 95% CI:-0.339 --0.040, P < 0.001).ConclusionOur findings suggested that a lower eosinophil level was associated with higher SAP and poorer outcome, and SAP might play an important effect in the association between eosinophils and poor outcomes.

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Dynamic Prediction of Mechanical Thrombectomy Outcome for Acute Ischemic Stroke Patients Using Machine Learning.

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