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Chung CC, Su ECY, Chen JH, Chen YT, Kuo CY. XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke. Diagnostics (Basel) 2023; 13:diagnostics13050842. [PMID: 36899986 PMCID: PMC10000880 DOI: 10.3390/diagnostics13050842] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
An all-inclusive and accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. This study developed extreme gradient boosting (XGBoost)-based models using three simple factors-age, fasting glucose, and National Institutes of Health Stroke Scale (NIHSS) scores-to predict the three-month functional outcomes after AIS. We retrieved the medical records of 1848 patients diagnosed with AIS and managed at a single medical center between 2016 and 2020. We developed and validated the predictions and ranked the importance of each variable. The XGBoost model achieved notable performance, with an area under the curve of 0.8595. As predicted by the model, the patients with initial NIHSS score > 5, aged over 64 years, and fasting blood glucose > 86 mg/dL were associated with unfavorable prognoses. For patients receiving endovascular therapy, fasting glucose was the most important predictor. The NIHSS score at admission was the most significant predictor for those who received other treatments. Our proposed XGBoost model showed a reliable predictive power of AIS outcomes using readily available and simple predictors and also demonstrated the validity of the model for application in patients receiving different AIS treatments, providing clinical evidence for future optimization of AIS treatment strategies.
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Affiliation(s)
- Chen-Chih Chung
- Department of Neurology, Taipei Medical University—Shuang Ho Hospital, New Taipei City 235, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University—Shuang Ho Hospital, New Taipei City 235, Taiwan
| | - Emily Chia-Yu Su
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei City 110, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei City 110, Taiwan
| | - Jia-Hung Chen
- Department of Neurology, Taipei Medical University—Shuang Ho Hospital, New Taipei City 235, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University—Shuang Ho Hospital, New Taipei City 235, Taiwan
| | - Yi-Tui Chen
- Smart Healthcare Interdisciplinary College, National Taipei University of Nursing and Health Sciences, Taipei City 112, Taiwan
- Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei City 112, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei City 103, Taiwan
| | - Chao-Yang Kuo
- Smart Healthcare Interdisciplinary College, National Taipei University of Nursing and Health Sciences, Taipei City 112, Taiwan
- Correspondence: ; Tel.: +886-2-28227101 (ext. 1385)
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Yang CC, Bamodu OA, Chan L, Chen JH, Hong CT, Huang YT, Chung CC. Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks. Front Neurol 2023; 14:1085178. [PMID: 36846116 PMCID: PMC9947790 DOI: 10.3389/fneur.2023.1085178] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/18/2023] [Indexed: 02/11/2023] Open
Abstract
Background Accurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on medical expenditure and subsequent disposition. This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time of hospitalization. Methods We retrieved the medical records of patients who received acute ischemic stroke diagnoses and were treated at a stroke center between January 2016 and June 2020, and a retrospective analysis of these data was performed. Prolonged length of stay was defined as a hospital stay longer than the median number of days. We applied artificial neural networks to derive prediction models using parameters associated with the length of stay that was collected at admission, and a sensitivity analysis was performed to assess the effect of each predictor. We applied 5-fold cross-validation and used the validation set to evaluate the classification performance of the artificial neural network models. Results Overall, 2,240 patients were enrolled in this study. The median length of hospital stay was 9 days. A total of 1,101 patients (49.2%) had a prolonged hospital stay. A prolonged length of stay is associated with worse neurological outcomes at discharge. Univariate analysis identified 14 baseline parameters associated with prolonged length of stay, and with these parameters as input, the artificial neural network model achieved training and validation areas under the curve of 0.808 and 0.788, respectively. The mean accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of prediction models were 74.5, 74.9, 74.2, 75.2, and 73.9%, respectively. The key factors associated with prolonged length of stay were National Institutes of Health Stroke Scale scores at admission, atrial fibrillation, receiving thrombolytic therapy, history of hypertension, diabetes, and previous stroke. Conclusion The artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.
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Affiliation(s)
- Cheng-Chang Yang
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Research Center for Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Oluwaseun Adebayo Bamodu
- Department of Medical Research and Education, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Urology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Hematology and Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Lung Chan
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jia-Hung Chen
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chien-Tai Hong
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yi-Ting Huang
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Nursing, School of Nursing, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chen-Chih Chung
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan,*Correspondence: Chen-Chih Chung ✉
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Stroke mortality prediction using machine learning: systematic review. J Neurol Sci 2023; 444:120529. [PMID: 36580703 DOI: 10.1016/j.jns.2022.120529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/30/2022] [Accepted: 12/18/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning-based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality. MATERIALS AND METHODS We searched MEDLINE/PubMed and Web of Science databases for original publications on machine learning applications in stroke mortality prediction, published between January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated using the tailored QUADAS-2 tool. RESULTS Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction (0.67-0.98). In most of the articles, the models were applied for short-term post stroke mortality. The number of explanatory features used in the models to predict mortality ranged from 5 to 200, with substantial overlap in the variables included. Age, high BMI and high NIHSS score were identified as important predictors for mortality. Almost all studies had a high risk of bias in at least one category and concerns regarding applicability. CONCLUSION Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.
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Shao H, Chan WCL, Du H, Chen XF, Ma Q, Shao Z. A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study. Digit Health 2023; 9:20552076221149528. [PMID: 36636727 PMCID: PMC9829886 DOI: 10.1177/20552076221149528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Background Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice. Methods Our proposed algorithm is an advanced version of classical k-nearest neighbors classification algorithm (KNN). We achieved high interpretability by changing the isotropy in feature space of classical KNN. We leveraged a cohort of 189 patients to prove that our algorithm maintains the interpretability of previous models while in the meantime improving the predictive power when compared with the existing algorithms. The predictive powers of models were assessed by area under the receiver operating characteristic curve (AUC). Results In terms of interpretability, only onset time, diabetes, and baseline National Institutes of Health Stroke Scale (NIHSS) were statistically significant and their contributions to the final prediction were forced to be proportional to their feature importance values by the rescaling formula we defined. In terms of predictive power, our advanced KNN (AUC 0.88) outperformed the classical KNN (AUC 0.75, p = 0.0192 ). Conclusions Our preliminary results show that the advanced KNN achieved high AUC and identified consistent significant clinical features as previous clinical trials/observational studies did. This model shows the potential to assist in thrombolysis patient selection for improving the successful rate of thrombolysis.
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Affiliation(s)
- Huiling Shao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong,Huiling Shao, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y934, 9/F, Lee Shau Kee Building, Hung Hom, Kowloon, 999077, Hong Kong.
| | - Wing Chi Lawrence Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Heng Du
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Xiangyan Fiona Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Qilin Ma
- Department of Neurology, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Zhiyu Shao
- Department of Neurology, The First Affiliated Hospital of Xiamen University, Xiamen, China
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Shao H, Chen X, Ma Q, Shao Z, Du H, Chan LWC. The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements. Front Neurol 2022; 13:934929. [PMID: 36341121 PMCID: PMC9630915 DOI: 10.3389/fneur.2022.934929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/28/2022] [Indexed: 11/30/2022] Open
Abstract
In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.
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Affiliation(s)
- Huiling Shao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Xiangyan Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Qilin Ma
- Department of Neurology, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Zhiyu Shao
- Department of Neurology, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Heng Du
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Lawrence Wing Chi Chan
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Chou SY, Bamodu OA, Chiu WT, Hong CT, Chan L, Chung CC. Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management. Sci Rep 2022; 12:7254. [PMID: 35508580 PMCID: PMC9068683 DOI: 10.1038/s41598-022-11201-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 04/14/2022] [Indexed: 01/04/2023] Open
Abstract
Existing prognostic models to predict the neurological recovery in patients with cardiac arrest receiving targeted temperature management (TTM) either exhibit moderate accuracy or are too complicated for clinical application. This necessitates the development of a simple and generalizable prediction model to inform clinical decision-making for patients receiving TTM. The present study explores the predictive validity of the Cardiac Arrest Survival Post-resuscitation In-hospital (CASPRI) score in cardiac arrest patients receiving TTM, regardless of cardiac event location, and uses artificial neural network (ANN) algorithms to boost the prediction performance. This retrospective observational study evaluated the prognostic relevance of the CASPRI score and applied ANN to develop outcome prediction models in a cohort of 570 patients with cardiac arrest and treated with TTM between 2014 and 2019 in a nationwide multicenter registry in Taiwan. In univariate logistic regression analysis, the CASPRI score was significantly associated with neurological outcome, with the area under the receiver operating characteristics curve (AUC) of 0.811. The generated ANN model, based on 10 items of the CASPRI score, achieved a training AUC of 0.976 and validation AUC of 0.921, with the accuracy, precision, sensitivity, and specificity of 89.2%, 91.6%, 87.6%, and 91.2%, respectively, for the validation set. CASPRI score has prognostic relevance in patients who received TTM after cardiac arrest. The generated ANN-boosted, CASPRI-based model exhibited good performance for predicting TTM neurological outcome, thus, we propose its clinical application to improve outcome prediction, facilitate decision-making, and formulate individualized therapeutic plans for patients receiving TTM.
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Affiliation(s)
- Szu-Yi Chou
- Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, ROC.,Ph.D. Program for Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University and National Health Research Institutes, Taipei, Taiwan, ROC
| | - Oluwaseun Adebayo Bamodu
- Department of Medical Research & Education, Shuang Ho Hospital, Taipei Medical University, New Taipei City, 235, Taiwan, ROC.,Department of Urology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, 235, Taiwan, ROC.,Department of Hematology & Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, 235, Taiwan, ROC
| | - Wei-Ting Chiu
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, 291, Zhongzheng Road, Zhonghe District, New Taipei City, 235, Taiwan, ROC.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, 110, Taiwan, ROC.,Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, 235, Taiwan, ROC
| | - Chien-Tai Hong
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, 291, Zhongzheng Road, Zhonghe District, New Taipei City, 235, Taiwan, ROC.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, 110, Taiwan, ROC
| | - Lung Chan
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, 291, Zhongzheng Road, Zhonghe District, New Taipei City, 235, Taiwan, ROC. .,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, 110, Taiwan, ROC.
| | - Chen-Chih Chung
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, 291, Zhongzheng Road, Zhonghe District, New Taipei City, 235, Taiwan, ROC. .,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, 110, Taiwan, ROC. .,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei City, 110, Taiwan, ROC.
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