A NAC nomogram to predict the probability of three-month unfavorable outcome in Chinese acute ischemic stroke patients treated with mechanical thrombectomy

Li, Xiang; Yang, Zhi‐gang; Hu, Jue; Li, Xue Mei; Huang, Chao; Shan, Ya Jie; Nyame, Linda; Zhao, Zheng; Sun, Chao; Ibrahim, Mako; Pan, Xi; Liu, Chao; Zhao, Zhuang; Zou, Jian

doi:10.1080/00207454.2020.1733565

Cited by 11 publications

(17 citation statements)

References 26 publications

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“…Previously, several models have been developed to predict the probability of 3-month mortality in AIS patients receiving MT [3,8,9]. Among them, an Italian cohort study developed a nomogram that included NIHSS score, age, pre-stroke mRS score, bridging therapy or direct thrombectomy, grade of recanalization according to the mTICI grading system, and onset-to-end procedure time [8].…”

Section: Comparison With Prior Studiesmentioning

confidence: 99%

“…Among them, an Italian cohort study developed a nomogram that included NIHSS score, age, pre-stroke mRS score, bridging therapy or direct thrombectomy, grade of recanalization according to the mTICI grading system, and onset-to-end procedure time [8]. While two Chinese studies identi ed many demographic and clinical characteristics, including baseline NIHSS score, age, creatinine, blood glucose level, collateral status, and sICH [3,9]. However, due to the inconsistent inclusion and exclusion criteria, the potential predictors nally included in the nomogram in their studies are still quite different.…”

Section: Comparison With Prior Studiesmentioning

confidence: 99%

“…Nomogram is a computational tool that can generate numerical probabilities of clinical events based on each patient's personal pro le by creating graphical representation of statistical prediction models [4], which has demonstrated signi cant advantages over traditional staging systems and has been used to predict many diseases, such as pneumonia, myocardial infarction, and cancer [5][6][7]. In recent years, several prognostic models based on clinical and radiological variables have been used to predict prognosis after thrombectomy for stroke [3,8,9]. The patients included in the studies mainly had symptoms of acute anterior circulation stroke within 6 h of onset, representing the largest population of stroke candidates for thrombectomy according to the 2018 guideline for early management of patients with acute ischemic stroke [10].…”

Section: Introductionmentioning

confidence: 99%

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A novel clinical nomogram for predicting 3-month unfavorable outcomes of stroke patients treated with mechanical thrombectomy

Zhang

Wang

Yang

et al. 2021

Preprint

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Background: Mechanical thrombectomy (MT) is an effective treatment for large-vessel occlusion in acute ischemic stroke, however, only some revascularized patients have a good prognosis. For stroke patients undergoing MT, predicting the risk of unfavorable outcomes and adjusting the treatment strategies accordingly can greatly improve prognosis. Therefore, we aimed to develop and validate a nomogram that can predict 3-month unfavorable outcomes for individual stroke patient treated with MT. Methods: We analyzed 238 patients with acute ischemic stroke who underwent MT from January 2018 to October 2020. The primary outcome was a 3-month unfavorable outcome, assessed using the modified Rankin Scale (mRS), 3-6. A nomogram was generated based on a multivariable logistic model. We used the area under the receiver-operating characteristic curve to evaluate the discriminative performance and used the calibration curve and Spiegelhalter’s Z-test to assess the calibration performance of the risk prediction model. Results: After multivariable logistic regression, six variables (gender, bridging therapy, postoperative mTICI, stroke-associated pneumonia, preoperative creatinine and Na) remained independent predictors of 3-month unfavorable outcomes in stroke patients treated with MT, thus forming a nomogram. The area under the nomogram curve was 0.848 with good calibration performance (P = 0.946 for the Spiegelhalter’s Z-test). Conclusions: A novel nomogram consisting of gender, bridging therapy, postoperative mTICI, stroke-associated pneumonia, preoperative creatinine and Na can predict the 3-month unfavorable outcomes in stroke patients treated with MT.

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Section: Comparison With Prior Studiesmentioning

confidence: 99%

Section: Comparison With Prior Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel clinical nomogram for predicting 3-month unfavorable outcomes of stroke patients treated with mechanical thrombectomy

Zhang

Wang

Yang

et al. 2021

Preprint

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“…Models used for dynamically predicting three-month unfavorable outcomes in patients with AIS treated with MT using clinically relevant preoperative and postoperative time point variables have not been developed. Currently, most are built based on preoperative variables, such as THRIVE [ 10 ], HIAT [ 14 ], GADIS [ 19 ], NAC [ 20 ], and IER-START [ 21 ], and several machine learning (ML) models [ 22 , 23 , 24 , 25 ], and these scores and models cannot be updated according to the changes in the patient’s state and examination results over time. In clinical practice, these scores are intended to inform treatment, and they lack focus on determining functional outcomes after treatment.…”

Section: Introductionmentioning

confidence: 99%

“…In clinical practice, these scores are intended to inform treatment, and they lack focus on determining functional outcomes after treatment. Additionally, the above scores have been validated externally, and the AUC range was from 0.680 to 0.838 in recent studies [ 10 , 20 , 21 ], indicating that there is still room to improve accuracy. In addition, these scores, based on the linear regression algorithm, have some limitations in addressing nonlinear problems between the variables in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhang

et al. 2022

Brain Sciences

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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.

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A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy

Haranhalli

Javed

Boyke

et al. 2021

Journal of Stroke and Cerebrovascular Diseases

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A NAC nomogram to predict the probability of three-month unfavorable outcome in Chinese acute ischemic stroke patients treated with mechanical thrombectomy

Cited by 11 publications

References 26 publications

A novel clinical nomogram for predicting 3-month unfavorable outcomes of stroke patients treated with mechanical thrombectomy

A novel clinical nomogram for predicting 3-month unfavorable outcomes of stroke patients treated with mechanical thrombectomy

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

A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy

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