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Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.

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Title: Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.
Authors: Wang, Qianwen (AUTHOR), Yin, Jiawen (AUTHOR), Xu, Lei (AUTHOR), Lu, Jun (AUTHOR), Chen, Juan (AUTHOR), Chen, Yuhui (AUTHOR), Wufuer, Alimu (AUTHOR), Gong, Tao (AUTHOR)
Source: Neurological Sciences. Jul2024, Vol. 45 Issue 7, p3255-3266. 12p.
Subjects: Ischemic stroke, Machine learning, Nomography (Mathematics), Reperfusion, Prediction models
Abstract: Objective: 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. Methods: Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited. 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. Results: 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. Conclusion: Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients. Both nomogram and ML models showed good discrimination and generalizability. [ABSTRACT FROM AUTHOR]
Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Qianwen%22">Wang, Qianwen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Jiawen%22">Yin, Jiawen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Lei%22">Xu, Lei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Jun%22">Lu, Jun</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Juan%22">Chen, Juan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yuhui%22">Chen, Yuhui</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wufuer%2C+Alimu%22">Wufuer, Alimu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gong%2C+Tao%22">Gong, Tao</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Neurological+Sciences%22">Neurological Sciences</searchLink>. Jul2024, Vol. 45 Issue 7, p3255-3266. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Ischemic+stroke%22">Ischemic stroke</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Nomography+%28Mathematics%29%22">Nomography (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Reperfusion%22">Reperfusion</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Objective: 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. Methods: Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited. 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. Results: 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. Conclusion: Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients. Both nomogram and ML models showed good discrimination and generalizability. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Nomography (Mathematics)
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              Text: Jul2024
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