Baseline platelet count may predict short-term functional outcome of cerebral infarction

Kanazawa, Kazo; Miyamoto, Nobukazu; Hira, Kenichiro; Kijima, Chikage; Ueno, Yoshio

doi:10.1186/s12883-022-02845-5

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…However, Guo et al [23] and Jo et al [24] published an article and our study did not find a significant correlation between WBC count and MCE, which could be attributed to the differences in the populations included in the study. Many previous studies on MCE have not included PLT as a variable, and conclusions regarding PLT count and the prognosis of cerebral infarction have also been inconsistent [27,43,49,50]. However, our study found that a reduced PLT count was a risk factor for MCE.…”

Section: Discussioncontrasting

confidence: 68%

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An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction

Cao,

Ma,

Huang

et al. 2024

Eur Neurol

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Introduction: Malignant cerebral edema (MCE) is a serious complication and the main cause of poor prognosis in patients with large-hemisphere infarction (LHI). Therefore, the rapid and accurate identification of potential patients with MCE is essential for timely therapy. This study utilized an artificial intelligence-based machine learning approach to establish an interpretable model for predicting MCE in patients with LHI. Methods: This study included 314 patients with LHI not undergoing recanalization therapy. The patients were divided into MCE and non-MCE groups, the extreme Gradient boosting (XGBoost) model was developed. A confusion matrix was used to measure the prediction performance of the XGBoost model. We also utilized the SHapley Additive extension (SHAP) method to explain the XGBoost model. Decision curve analysis and receiver operating characteristic (ROC) curve were performed to evaluate the net benefits of the model. Results: MCE was observed in 121(38.5%) of the 314 patients with LHI. The model showed excellent predictive performance, with an area under the curve of 0.916. The SHAP method revealed the top 10 predictive variables of the MCE such as ASPECTS score, NIHSS score, CS score, APACHE II score, HbA1c, AF, NLR, PLT, GCS and Age based on their importance ranking. Conclusion: An interpretable predictive model can increase transparency and help doctors accurately predict the occurrence of MCE in LHI patients, not undergoing recanalization therapy within 48h from onset, providing patients with better treatment strategies and enabling optimal resource allocation.

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Section: Discussioncontrasting

confidence: 68%

“…Many previous studies on MCE have not included PLT as a variable, and conclusions regarding PLT count and the prognosis of cerebral infarction have also been inconsistent [27, 43, 49, 50]. However, our study found that a reduced PLT count was a risk factor for MCE.…”

Section: Discussioncontrasting

confidence: 59%

An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction

Cao,

Ma,

Huang

et al. 2024

Eur Neurol

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Red blood cell distribution width to platelet ratio predicts early neurological deterioration in acute ischemic stroke patients receiving intravenous thrombolysis

Jiang¹,

Shen²,

Bilal³

et al. 2023

Journal of Stroke and Cerebrovascular Diseases

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An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large Hemisphere Infarction

Cao

et al. 2023

Preprint

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Background: Malignant cerebral edema (MCE) is a serious complication and the main cause of poor prognosis in large hemisphere infarction (LHI). Therefore, rapid and accurate identification of potential patients with MCE is essential for providing timely therapy. However, most prediction models lack interpretability, limiting their use in clinical practice.To establish an interpretable model to predict MCE in patients with LHI. We utilize the SHapley Additive exPlanations (SHAP) method to explain the eXtreme Gradient Boosting (XGBoost) model and identify prognostic factors, providing valuable data for clinical decision-making. Methods: In this retrospective cohort study, we included 314 consecutive patients with LHI admitted to the Third Affiliated Hospital of Soochow University from December 2018 to April 2023. The patients were divided into MCE and non-MCE groups, and we developed an explainable artificial intelligence prediction model. The dataset was randomly divided into two parts: 75% of the data were used for model training and 25% were used for model validation. Confusion matrix was utilized to measure the prediction performance of the XGBoost model. The SHAP method was used to explain the XGBoost model. Decision curve analysis was performed to evaluate the net benefit of the model. Results: A 38.5% (121/314) incidence of MCE was observed among the 314 patients with LHI. The XGBoost model showed excellent predictive performance, with an area under the curve of 0.916 in validation. The SHAP method revealed the top 10 predictive variables of MCE based on their importance ranking, while the Alberta Stroke Program Early CT Score (ASPECTS) score was considered the most important predictive variable, followed by National Institutes of Health Stroke Scale (NIHSS) score, Collateral Status (CS) score, APACHE II score, glycated hemoglobin (HbA1c), atrial fibrillation (AF), neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, Glasgow Coma Scale (GCS) and Age. We found that ASPECTS score < 6, NIHSS score >17, CS score < 2, APACHE II >14, HbA1c >6.3 and AF were associated with increased risks of malignant cerebral edema. Conclusion: An interpretable predictive model can increase transparency and help doctors to accurately predict the occurrence of MCE in patients with LHI, providing patients with better treatment strategies and enabling optimal resource allocation.

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Baseline platelet count may predict short-term functional outcome of cerebral infarction

Cited by 4 publications

References 31 publications

An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction

An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction

Red blood cell distribution width to platelet ratio predicts early neurological deterioration in acute ischemic stroke patients receiving intravenous thrombolysis

An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large Hemisphere Infarction

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