Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients

Yu, Decai; Williams, George W.; Aguilar, David; Yamal, José‐Miguel; Maroufy, Vahed; Zhang, Chenguang; Huang, Yu-Ting; Gu, Yuxuan; Talebi, Yashar; Wu, Hulin

doi:10.1002/acn3.51208

Cited by 17 publications

(22 citation statements)

References 21 publications

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“…One previous study explored performance of various machine learning algorithms including XGBoost on predicting mortality of non- traumatic SAH patients. 23 While only records of laboratory tests and drugs usage were collected for training machine learning models without inclusion of complications such as DCI and hydrocephalus which may significantly affect prognosis of aSAH patients. The machine learning algorithm may show it’s superiority in outcome prediction when more clinically significant factors were included or framework of datasets were more complicated.…”

Section: Introductionmentioning

confidence: 99%

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

Wang¹,

Zhang²,

Shan³

et al. 2022

NDT

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Background Patients suffered aneurysmal subarachnoid hemorrhage (aSAH) usually develop poor survival and functional outcome. Evaluating aSAH patients at high risk of poor outcome is necessary for clinicians to make suitable therapeutical strategy. This study is conducted to develop prognostic model using XGBoost (extreme gradient boosting) algorithm in aSAH. Methods A total of 351 aSAH patients admitted to West China hospital were identified. Patients were divided into training set and test set with ratio of 7:3 to testify the predictive value of XGBoost based prognostic model. Additionally, logistic regression model was also constructed and compared with XGBoost based model. Area under the receiver operating characteristic curve (AUC), sensitivity and specificity were calculated to evaluate the value of XGBoost and logistic regression. Results There were 74 (21.1%) non-survivors and 148 (42.1%) patients with unfavorable functional outcome. Non-survivors had older age (p=0.025), lower Glasgow coma scale (GCS) (p<0.001), higher World Federation of Neurosurgical Societies WFNS score (p<0.001), mFisher score (p<0.001). The incidence of intraventricular hemorrhage (IVH) (p=0.025) and delayed cerebral ischemia (DCI) (p<0.001) was higher in non-survivors than survivors. The AUC of XGBoost model for predicting mortality and unfavorable functional outcome were 0.950 and 0.958, which were higher than 0.767 and 0.829 of logistic regression model. Conclusion XGBoost based model is more precise than logistic regression model in predicting outcome of aSAH patients. Using XGBoost prognostic model is helpful for clinicians to identify high-risk aSAH patients and therefore strengthen medical care.

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Section: Introductionmentioning

confidence: 99%

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

Wang¹,

Zhang²,

Shan³

et al. 2022

NDT

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“…Early diagnosis and treatment of SAH patients are important to ensure optimal cerebral blood flow and will also potentially improve the long-term outcome of patient's health [ 18 , 19 ]. Some researchers are considering that changes in heart rate variability (HRV) with clinical events provide relevant features for prediction [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Sengupta

Alzbutas

2022

BioMed Research International

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Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

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“…In fact, ML also explores non-linear correlations among variables and detects not only significant associations with outcomes, but also the synergy among variables in outcome prediction. Indeed, an electronic health record–based prediction model has been shown to be more accurate in predicting the risk of adverse outcome than traditional models using the a priori selected clinical variables and predictors representing a source of agnostic assessment that is independent of practitioner experience and provide additional assurance to families when considering ongoing intervention [ 42 ]. Moreover, the relative weight of single variables may vary among patients due to the interaction and interplay with the others.…”

Section: Discussionmentioning

confidence: 99%

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage

et al. 2022

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Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognostic expectations. We evaluated the performance of a machine learning (ML) model to predict the 6-month functional status in elderly patients with ICH leveraging the predictive value of the clinical characteristics at hospital admission. Data were extracted by a retrospective multicentric database of patients ≥ 70 years of age consecutively admitted for the management of spontaneous ICH between January 1, 2014 and December 31, 2019. Relevant demographic, clinical, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML model. Outcome was determined according to the Glasgow Outcome Scale (GOS) at 6 months from ICH: dead (GOS 1), poor outcome (GOS 2–3: vegetative status/severe disability), and good outcome (GOS 4–5: moderate disability/good recovery). Ten features were selected by Boruta with the following relative importance order in the ML model: Glasgow Coma Scale, Charlson Comorbidity Index, ICH score, ICH volume, pupillary status, brainstem location, age, anticoagulant/antiplatelet agents, intraventricular hemorrhage, and cerebellar location. Random forest prediction model, evaluated on the hold-out test set, achieved an AUC of 0.96 (0.94–0.98), 0.89 (0.86–0.93), and 0.93 (0.90–0.95) for dead, poor, and good outcome classes, respectively, demonstrating high discriminative ability. A random forest classifier was successfully trained and internally validated to stratify elderly patients with spontaneous ICH into prognostic subclasses. The predictive value is enhanced by the ability of ML model to identify synergy among variables.

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Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients

Cited by 17 publications

References 21 publications

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

[Retracted] Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage

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