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Hajiesmaeili M, Nooraei N, Alamdari NM, Bidgoli BF, Jame SZB, Moghaddam NM, Fathi M. Clinical phenotypes of patients with acute stroke: a secondary analysis. ROMANIAN JOURNAL OF INTERNAL MEDICINE = REVUE ROUMAINE DE MEDECINE INTERNE 2024; 62:168-177. [PMID: 38299606 DOI: 10.2478/rjim-2024-0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Stroke is a leading cause of mortality worldwide and a major cause of disability having a high burden on patients, society, and caregiving systems. This study was conducted to investigate the presence of clusters of in-hospital patients with acute stroke based on demographic and clinical data. Cluster analysis reveals patterns in patient characteristics without requiring knowledge of a predefined patient category or assumptions about likely groupings within the data. METHODS We performed a secondary analysis of open-access anonymized data from patients with acute stroke admitted to a hospital between December 2019 to June 2021. In total, 216 patients (78; 36.1% men) were included in the analytical dataset with a mean (SD) age of 60.3 (14.4). Many demographic and clinical features were included in the analysis and the Barthel Index on discharge was used for comparing the functional recovery of the identified clusters. RESULTS Hierarchical clustering based on the principal components identified two clusters of 109 and 107 patients. The clusters were different in the Barthel Index scores on discharge with the mean (SD) of 39.3 (29.3) versus 62.6 (29.4); t (213.87) = -5.818, P <0.001, Cohen's d (95%CI) = -0.80 (-1.07, -0.52). A logistic model showed that age, systolic blood pressure, pulse rate, D-dimer blood level, low-density lipoprotein, hemoglobin, creatinine concentration, the National Institute of Health Stroke Scale value, and the Barthel Index scores on admission were significant predictors of cluster profiles (all P ≤0.029). CONCLUSION There are two clusters in hospitalized patients with acute stroke with significantly different functional recovery. This allows prognostic grouping of hospitalized acute stroke patients for prioritization of care or resource allocation. The clusters can be recognized using easily measured demographic and clinical features.
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Affiliation(s)
- Mohammadreza Hajiesmaeili
- 1Critical Care Quality Improvement Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Navid Nooraei
- 2Critical Care Quality Improvement Research Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nasser Malekpour Alamdari
- 2Critical Care Quality Improvement Research Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Behruz Farzanegan Bidgoli
- 3Critical Care Quality Improvement Research Center, Dr. Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sanaz Zargar Balaye Jame
- 4Department of Health Management and Economics, Faculty of Medicine, Aja University of Medical Sciences, Tehran, Iran
| | - Nader Markazi Moghaddam
- 2Critical Care Quality Improvement Research Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- 4Department of Health Management and Economics, Faculty of Medicine, Aja University of Medical Sciences, Tehran, Iran
| | - Mohammad Fathi
- 2Critical Care Quality Improvement Research Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Lee CC, Su SY, Sung SF. Machine learning-based survival analysis approaches for predicting the risk of pneumonia post-stroke discharge. Int J Med Inform 2024; 186:105422. [PMID: 38518677 DOI: 10.1016/j.ijmedinf.2024.105422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/25/2024] [Accepted: 03/19/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND Post-stroke pneumonia (PSP) is common among stroke patients. PSP occurring after hospital discharge continues to increase the risk of poor functional outcomes and death among stroke survivors. Currently, there is no prediction model specifically designed to predict the occurrence of PSP beyond the acute stage of stroke. This study aimed to explore the use of machine learning (ML) methods in predicting the risk of PSP after hospital discharge. METHODS This study analyzed data from 5,754 hospitalized stroke patients. The dataset was randomly divided into a training set and a holdout test set, with a ratio of 80:20. Several clinical and laboratory variables were utilized as predictors and different ML algorithms were employed to model time-to-event data. The ML model's predictive performance was compared to existing risk-scoring systems. A model-agnostic method based on Shapley additive explanations was utilized to interpret the ML model. RESULTS The study found that 5.7% of the study patients experienced pneumonia within one year after discharge. Based on repeated 5-fold cross-validation on the training set, the random survival forest (RSF) model had the highest C-index among the various ML algorithms and traditional Cox regression analysis. The final RSF model achieved a C-index of 0.787 (95% confidence interval: 0.737-0.840) on the holdout test set, outperforming five existing risk-scoring systems. The top three important predictors were the Glasgow Coma Scale score, age, and length of hospital stay. CONCLUSIONS The RSF model demonstrated superior discriminative ability compared to other ML algorithms and traditional Cox regression analysis, suggesting a non-linear relationship between predictors and outcomes. The developed ML model can be integrated into the hospital information system to provide personalized risk assessments.
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Affiliation(s)
- Chang-Ching Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Sheng-You Su
- Clinical Medicine Research Center, Department of Medical Research, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan; Department of Beauty & Health Care, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan.
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Lin PJ, Li W, Zhai X, Li Z, Sun J, Xu Q, Pan Y, Ji L, Li C. Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1546-1555. [PMID: 38578854 DOI: 10.1109/tnsre.2024.3384498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.
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Azzam AY, Vaishnav D, Essibayi MA, Unda SR, Jabal MS, Liriano G, Fortunel A, Holland R, Khatri D, Haranhalli N, Altschul D. Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study. J Stroke Cerebrovasc Dis 2024; 33:107553. [PMID: 38340555 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/25/2023] [Indexed: 02/12/2024] Open
Abstract
INTRODUCTION Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification. METHODS In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH. Patient data, including various clinical variables and co-factors, were collected. Six different machine learning models, including logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting (XGB), were trained and evaluated using performance metrics such as accuracy, area under the curve (AUC), precision, recall, and F1 score. RESULTS After data augmentation, the random forest model demonstrated the best performance, with an AUC of 0.85. The multilayer perceptron neural network model achieved an accuracy of 0.93 and an F1 score of 0.85, making it the best performing model. The presence of positive clinical vasospasm was identified as the most important feature for predicting DCI. CONCLUSIONS Our study highlights the potential of machine learning models in predicting the occurrence of DCI in patients with aSAH. The multilayer perceptron model showed excellent performance, indicating its utility in risk stratification and clinical decision-making. However, further validation and refinement of the models are necessary to ensure their generalizability and applicability in real-world settings. Machine learning techniques have the potential to enhance patient care and improve outcomes in aSAH, but their implementation should be accompanied by careful evaluation and validation.
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Affiliation(s)
- Ahmed Y Azzam
- Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Dhrumil Vaishnav
- Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Muhammed Amir Essibayi
- Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Santiago R Unda
- Department of Neurological Surgery, Weill Cornell Medical College, Cornell University NY, NY, USA
| | | | - Genesis Liriano
- Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Adisson Fortunel
- Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ryan Holland
- Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Deepak Khatri
- Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Neil Haranhalli
- Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - David Altschul
- Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
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Sahriar S, Akther S, Mauya J, Amin R, Mia MS, Ruhi S, Reza MS. Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms. Heliyon 2024; 10:e27411. [PMID: 38495193 PMCID: PMC10943390 DOI: 10.1016/j.heliyon.2024.e27411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024] Open
Abstract
Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. Due to different data measurement scales and their probability distributional assumptions, ML-based algorithms struggle to detect risk factors. Furthermore, when dealing with risk factors with high-dimensional features, learning algorithms struggle with complexity. In this study, rigorous statistical tests are used to identify risk factors, and PCA-FA (Integration of Principal Components and Factors) and FPCA (Factor Based PCA) approaches are proposed for projecting suitable feature representations for improving learning algorithm performances. The study dataset consists of different clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with stroke, which contains 5110 patient records. Using significant test (P-value <0.05), chi-square and independent sample t-test identified age, heart_disease, hypertension, work_type, ever_married, bmi, and smoking_status as risk factors for stroke. To develop the predicting model with proposed feature extraction techniques, random forests approach provides the best results when utilizing the PCA-FA method. The best accuracy rate for this approach is 92.55%, while the AUC score is 98.15%. The prediction accuracy has increased from 2.19% to 19.03% compared to the existing work. Additionally, the prediction results is robustified and reproducible with a stacking ensemble-based classification algorithm. We also developed a web-based application to help doctors diagnose stroke risk based on the findings of this study, which could be used as an additional tool to help doctors diagnose.
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Affiliation(s)
- Saad Sahriar
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Sanjida Akther
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Jannatul Mauya
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Ruhul Amin
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Md Shahajada Mia
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Sabba Ruhi
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Md Shamim Reza
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
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Cashaback JGA, Allen JL, Chou AHY, Lin DJ, Price MA, Secerovic NK, Song S, Zhang H, Miller HL. NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework. J Neuroeng Rehabil 2024; 21:23. [PMID: 38347597 PMCID: PMC10863253 DOI: 10.1186/s12984-024-01318-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/25/2024] [Indexed: 02/15/2024] Open
Abstract
In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.
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Affiliation(s)
- Joshua G A Cashaback
- Biomedical Engineering, Mechanical Engineering, Kinesiology and Applied Physiology, Biome chanics and Movement Science Program, Interdisciplinary Neuroscience Graduate Program, University of Delaware, 540 S College Ave, Newark, DE, 19711, USA.
| | - Jessica L Allen
- Department of Mechanical Engineering, University of Florida, Gainesville, USA
| | | | - David J Lin
- Division of Neurocritical Care and Stroke Service, Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Providence, USA
| | - Mark A Price
- Department of Mechanical and Industrial Engineering, Department of Kinesiology, University of Massachusetts Amherst, Amherst, USA
| | - Natalija K Secerovic
- School of Electrical Engineering, The Mihajlo Pupin Institute, University of Belgrade, Belgrade, Serbia
- Laboratory for Neuroengineering, Institute for Robotics and Intelligent Systems ETH Zürich, Zurich, Switzerland
| | - Seungmoon Song
- Mechanical and Industrial Engineering, Northeastern University, Boston, USA
| | - Haohan Zhang
- Department of Mechanical Engineering, University of Utah, Salt Lake City, USA
| | - Haylie L Miller
- School of Kinesiology, University of Michigan, 830 N University Ave, Ann Arbor, MI, 48109, USA.
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Wu M, Yu K, Zhao Z, Zhu B. Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis. Heliyon 2024; 10:e24230. [PMID: 38288018 PMCID: PMC10823080 DOI: 10.1016/j.heliyon.2024.e24230] [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] [Received: 04/18/2023] [Revised: 11/23/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024] Open
Abstract
Objective Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends. Methods All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis. Results 2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of "acute ischemic stroke" and "atrial fibrillation-related stroke". The keyword "classification" had the first and longest burst, spanning from 2013 to 2018. 'Upport vector machine' got the strongest burst strength with 6.2. Keywords such as 'mechanical thrombectomy', 'expression', and 'prognosis' experienced bursts in 2022 and have continued to be prominent. Conclusion The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice.
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Affiliation(s)
- Mingfen Wu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Kefu Yu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zhigang Zhao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
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Wang Q, Yin J, Xu L, Lu J, Chen J, Chen Y, Wufuer A, Gong T. Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning. Neurol Sci 2024:10.1007/s10072-024-07329-7. [PMID: 38277052 DOI: 10.1007/s10072-024-07329-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024]
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.
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Affiliation(s)
- Qianwen Wang
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100084, People's Republic of China
| | - Jiawen Yin
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China
| | - Lei Xu
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China
| | - Jun Lu
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China
| | - Juan Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China
| | - Yuhui Chen
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China.
| | - Alimu Wufuer
- Department of Neurology, the First Affiliated Hospital of Xinjiang Medical University, No. 137 South Liyushan Road, Urumqi, 830054, Xinjiang, People's Republic of China.
| | - Tao Gong
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, People's Republic of China.
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100084, People's Republic of China.
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Nakaizumi D, Miyata S, Uchiyama K, Takahashi I. Development and Validation of a Decision Tree Analysis Model for Predicting Home Discharge in a Convalescent Ward: A Single Institution Study. Phys Ther Res 2024; 27:14-20. [PMID: 38690531 PMCID: PMC11057389 DOI: 10.1298/ptr.e10267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/17/2023] [Indexed: 05/02/2024]
Abstract
OBJECTIVES Accurately predicting the likelihood of inpatients' home discharge in a convalescent ward is crucial for assisting patients and families in decision-making. While logistic regression analysis has been commonly used, its complexity limits practicality in clinical settings. We focused on decision tree analysis, which is visually straightforward. This study aimed to develop and validate the accuracy of a prediction model for home discharge for inpatients in a convalescent ward using a decision tree analysis. METHODS The cohort consisted of 651 patients admitted to our convalescent ward from 2018 to 2020. We collected data from medical records, including disease classification, sex, age, duration of acute hospitalization, discharge destination (home or nonhome), and Functional Independence Measure (FIM) subitems at admission. We divided the cohort data into training and validation sets and developed a prediction model using decision tree analysis with discharge destination as the target and other variables as predictors. The model's accuracy was validated using the validation data set. RESULTS The decision tree model identified FIM grooming as the first single discriminator of home discharge, diverging at four points and identifying subsequent branching for the duration of acute hospitalization. The model's accuracy was 86.7%, with a sensitivity of 0.96, specificity of 0.52, positive predictive accuracy of 0.88, and negative predictive accuracy of 0.80. The area under the receiver operating characteristic curve was 0.75. CONCLUSION The predictive model demonstrated more than moderate predictive accuracy, suggesting its utility in clinical practice. Grooming emerged as a variable with the highest explanatory power for determining home discharge.
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Affiliation(s)
- Dai Nakaizumi
- Department of Rehabilitation, Japanese Red Cross Kanazawa Hospital, Japan
- Department of Physical Therapy, Graduate Course of Rehabilitation Science, School of Health Sciences, College of Medical, Pharmaceutical, and Health Sciences, Kanazawa University, Japan
| | - Shingo Miyata
- Department of Rehabilitation, Japanese Red Cross Kanazawa Hospital, Japan
| | - Keita Uchiyama
- Department of Rehabilitation, Japanese Red Cross Kanazawa Hospital, Japan
| | - Ikki Takahashi
- Department of Rehabilitation, Suzuki Clinic Orthopaedics River City, Japan
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Chen M, Qian D, Wang Y, An J, Meng K, Xu S, Liu S, Sun M, Li M, Pang C. Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke. J Med Syst 2024; 48:8. [PMID: 38165495 DOI: 10.1007/s10916-023-02020-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024]
Abstract
Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.
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Affiliation(s)
- Meng Chen
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Dongbao Qian
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Yixuan Wang
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Junyan An
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Ke Meng
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Shuai Xu
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Sheng Liu
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Meiyan Sun
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Miao Li
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China.
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China.
| | - Chunying Pang
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China.
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11
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Feng J, Zhang Q, Wu F, Peng J, Li Z, Chen Z. The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis. J Med Internet Res 2023; 25:e44895. [PMID: 37824198 PMCID: PMC10603565 DOI: 10.2196/44895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/02/2023] [Accepted: 09/14/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable. OBJECTIVE We aimed to assess the value of applying machine learning in predicting the time of stroke onset. METHODS PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team). RESULTS Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86). CONCLUSIONS Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds. TRIAL REGISTRATION PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898.
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Affiliation(s)
- Jing Feng
- Department of Neurology, Fifth People's Hospital of Jinan, Jinan, China
| | - Qizhi Zhang
- Department of Neurology, Fifth People's Hospital of Jinan, Jinan, China
| | - Feng Wu
- Department of Pulmonary Disease and Diabetes Mellitus, Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, China
| | - Jinxiang Peng
- Medical Department, Hubei Enshi College, Enshi, China
| | - Ziwei Li
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhuang Chen
- Department of Cardiovascular Medicine, Fifth People's Hospital of Jinan, Jinan, China
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12
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Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Tripsianis G, Iliopoulos I, Aggelousis N, Vadikolias K. From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning. J Pers Med 2023; 13:1375. [PMID: 37763143 PMCID: PMC10532952 DOI: 10.3390/jpm13091375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.
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Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece;
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Dimitrios Tsiptsios
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece;
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece;
| | - Gregory Tripsianis
- Laboratory of Medical Statistics, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Ioannis Iliopoulos
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Konstantinos Vadikolias
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
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13
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Senadheera I, Larssen BC, Mak-Yuen YYK, Steinfort S, Carey LM, Alahakoon D. Profiling Somatosensory Impairment after Stroke: Characterizing Common "Fingerprints" of Impairment Using Unsupervised Machine Learning-Based Cluster Analysis of Quantitative Measures of the Upper Limb. Brain Sci 2023; 13:1253. [PMID: 37759854 PMCID: PMC10526214 DOI: 10.3390/brainsci13091253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Altered somatosensory function is common among stroke survivors, yet is often poorly characterized. Methods of profiling somatosensation that illustrate the variability in impairment within and across different modalities remain limited. We aimed to characterize post-stroke somatosensation profiles ("fingerprints") of the upper limb using an unsupervised machine learning cluster analysis to capture hidden relationships between measures of touch, proprioception, and haptic object recognition. Raw data were pooled from six studies where multiple quantitative measures of upper limb somatosensation were collected from stroke survivors (n = 207) using the Tactile Discrimination Test (TDT), Wrist Position Sense Test (WPST) and functional Tactile Object Recognition Test (fTORT) on the contralesional and ipsilesional upper limbs. The Growing Self Organizing Map (GSOM) unsupervised machine learning algorithm was used to generate a topology-preserving two-dimensional mapping of the pooled data and then separate it into clusters. Signature profiles of somatosensory impairment across two modalities (TDT and WPST; n = 203) and three modalities (TDT, WPST, and fTORT; n = 141) were characterized for both hands. Distinct impairment subgroups were identified. The influence of background and clinical variables was also modelled. The study provided evidence of the utility of unsupervised cluster analysis that can profile stroke survivor signatures of somatosensory impairment, which may inform improved diagnosis and characterization of impairment patterns.
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Affiliation(s)
- Isuru Senadheera
- Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Melbourne, VIC 3086, Australia;
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
| | - Beverley C. Larssen
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Department of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Yvonne Y. K. Mak-Yuen
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3086, Australia
- Department of Occupational Therapy, St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Sarah Steinfort
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3086, Australia
| | - Leeanne M. Carey
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3086, Australia
| | - Damminda Alahakoon
- Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Melbourne, VIC 3086, Australia;
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14
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Xing Y, Xiao J, Zeng B, Wang Q. ICTs and interventions in telerehabilitation and their effects on stroke recovery. Front Neurol 2023; 14:1234003. [PMID: 37645607 PMCID: PMC10460969 DOI: 10.3389/fneur.2023.1234003] [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: 06/03/2023] [Accepted: 08/04/2023] [Indexed: 08/31/2023] Open
Abstract
Telerehabilitation (TR) is a new model to provide rehabilitation services to stroke survivors. It is a promising approach to deliver mainstream interventions for movement, cognitive, speech and language, and other disorders. TR has two major components: information and communication technologies (ICTs) and stroke interventions. ICTs provide a platform on which interventions are delivered and subsequently result in stroke recovery. In this mini-review, we went over features of ICTs that facilitate TR, as well as stroke interventions that can be delivered via TR platforms. Then, we reviewed the effects of TR on various stroke disorders. In most studies, TR is a feasible and effective solution in delivering interventions to patients. It is not inferior to usual care and in-clinic therapy with matching dose and intensity. With new technologies, TR may result in better outcomes than usual care for some disorders. One the other hand, TR also have many limitations that could lead to worse outcomes than traditional rehabilitation. In the end, we discussed major concerns and possible solutions related to TR, and also discussed potential directions for TR development.
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Affiliation(s)
- Yanghui Xing
- Department of Biomedical Engineering, Shantou University, Shantou, China
| | - Jianxin Xiao
- Department of Biomedical Engineering, Shantou University, Shantou, China
| | - Buhui Zeng
- Department of Biomedical Engineering, Shantou University, Shantou, China
| | - Qiang Wang
- National Research Center for Rehabilitation Technical Aids, Ministry of Civil Affairs, Beijing, China
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15
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Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Front Neurol 2023; 14:1039794. [PMID: 37388543 PMCID: PMC10299899 DOI: 10.3389/fneur.2023.1039794] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Background Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters. Results A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
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Affiliation(s)
- Qinglin Li
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Chi
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Weiying Zhao
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Wu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuanxu Jiao
- Department of Neurorehabilitation, Taizhou Enze Medical Center Luqiao Hospital, Taizhou, Zhejiang, China
| | - Xue Zheng
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Kaiyue Zhang
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Xiaoning Li
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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16
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Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. J Clin Med 2023; 12:jcm12113755. [PMID: 37297949 DOI: 10.3390/jcm12113755] [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: 04/30/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Elyse A Kuo
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Jennifer D Sokolowski
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Ryan T Kellogg
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Min Park
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Panagiotis Mastorakos
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, Thomas Jefferson University Hospital, 111 S 11th Street, Philadelphia, PA 19107, USA
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17
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Liu L, Zhang R, Shi D, Li R, Wang Q, Feng Y, Lu F, Zong Y, Xu X. Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor. Front Oncol 2023; 13:1190987. [PMID: 37234977 PMCID: PMC10206233 DOI: 10.3389/fonc.2023.1190987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
Background Accurate preoperative assessment of surgical difficulty is crucial to the success of the surgery and patient safety. This study aimed to evaluate the difficulty for endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) using multiple machine learning (ML) algorithms. Methods From December 2010 to December 2022, 555 patients with gGISTs in multi-centers were retrospectively studied and assigned to a training, validation, and test cohort. A difficult case was defined as meeting one of the following criteria: an operative time ≥ 90 min, severe intraoperative bleeding, or conversion to laparoscopic resection. Five types of algorithms were employed in building models, including traditional logistic regression (LR) and automated machine learning (AutoML) analysis (gradient boost machine (GBM), deep neural net (DL), generalized linear model (GLM), and default random forest (DRF)). We assessed the performance of the models using the areas under the receiver operating characteristic curves (AUC), the calibration curve, and the decision curve analysis (DCA) based on LR, as well as feature importance, SHapley Additive exPlanation (SHAP) Plots and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML. Results The GBM model outperformed other models with an AUC of 0.894 in the validation and 0.791 in the test cohorts. Furthermore, the GBM model achieved the highest accuracy among these AutoML models, with 0.935 and 0.911 in the validation and test cohorts, respectively. In addition, it was found that tumor size and endoscopists' experience were the most prominent features that significantly impacted the AutoML model's performance in predicting the difficulty for ER of gGISTs. Conclusion The AutoML model based on the GBM algorithm can accurately predict the difficulty for ER of gGISTs before surgery.
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Affiliation(s)
- Luojie Liu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, China
| | - Rufa Zhang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, China
| | - Dongtao Shi
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Rui Li
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qinghua Wang
- Department of Gastroenterology, No.1 People’s Hospital of Kunshan, Suzhou, China
| | - Yunfu Feng
- Department of Gastroenterology, No.1 People’s Hospital of Kunshan, Suzhou, China
| | - Fenying Lu
- Department of Gastroenterology, No.2 People’s Hospital of Changshu, Suzhou, China
| | - Yang Zong
- Department of General Surgery, Changshu Hospital Affiliated to Soochow University, Suzhou, China
| | - Xiaodan Xu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, China
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18
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Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Aggelousis N, Vadikolias K. Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning. Diagnostics (Basel) 2023; 13:diagnostics13030532. [PMID: 36766637 PMCID: PMC9914778 DOI: 10.3390/diagnostics13030532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.
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Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
- Correspondence:
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
- AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Estonia
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
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19
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Corsi L, Liuzzi P, Ballanti S, Scarpino M, Maiorelli A, Sterpu R, Macchi C, Cecchi F, Hakiki B, Grippo A, Lanatà A, Carrozza MC, Bocchi L, Mannini A. EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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20
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Bian R, Huo M, Liu W, Mansouri N, Tanglay O, Young I, Osipowicz K, Hu X, Zhang X, Doyen S, Sughrue ME, Liu L. Connectomics underlying motor functional outcomes in the acute period following stroke. Front Aging Neurosci 2023; 15:1131415. [PMID: 36875697 PMCID: PMC9975347 DOI: 10.3389/fnagi.2023.1131415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023] Open
Abstract
Objective Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes. Methods Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke. Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity. Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test. Results The area under the receiver operating characteristic curve ranged from 0.650 to 0.868. Models utilizing functional connectivity tended to have better performance than those utilizing structural connectivity. The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models. Conclusions Our study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary.
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Affiliation(s)
- Rong Bian
- Department of Rehabilitation, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ming Huo
- University of Health and Rehabilitation Sciences, Qingdao, China
| | - Wan Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Xiaorong Hu
- Xijia Medical Technology Company Limited, Shenzhen, China
| | - Xia Zhang
- Xijia Medical Technology Company Limited, Shenzhen, China.,International Joint Research Center on Precision Brain Medicine, Xidian Group Hospital, Xi'an, China
| | | | - Michael E Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia.,International Joint Research Center on Precision Brain Medicine, Xidian Group Hospital, Xi'an, China
| | - Li Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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21
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Anastasiev A, Kadone H, Marushima A, Watanabe H, Zaboronok A, Watanabe S, Matsumura A, Suzuki K, Matsumaru Y, Ishikawa E. Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides. SENSORS (BASEL, SWITZERLAND) 2022; 22:8733. [PMID: 36433330 PMCID: PMC9692557 DOI: 10.3390/s22228733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications.
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Affiliation(s)
- Alexey Anastasiev
- Department of Neurosurgery, Graduate School of Comprehensive Human Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Hideki Kadone
- Center for Cybernics Research, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Ibaraki, Japan
| | - Aiki Marushima
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Hiroki Watanabe
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Alexander Zaboronok
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Shinya Watanabe
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Akira Matsumura
- Ibaraki Prefectural University of Health Sciences, 4669-2 Amicho, Inashiki 300-0394, Ibaraki, Japan
| | - Kenji Suzuki
- Center for Cybernics Research, Artificial Intelligence Laboratory, Faculty of Engineering Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Ibaraki, Japan
| | - Yuji Matsumaru
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Eiichi Ishikawa
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
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22
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Chiavilli M, Campagnini S, Baretta T, Castagnoli C, Paperini A, Politi AM, Pellicciari L, Baccini M, Basagni B, Marignani S, Bardi D, Sodero A, Lombardi G, Guolo E, Navarro JS, Galeri S, Montesano A, Falco L, Rovaris MG, Carrozza MC, Macchi C, Mannini A, Cecchi F. Design and implementation of a Stroke Rehabilitation Registry for the systematic assessment of processes and outcomes and the development of data-driven prediction models: The STRATEGY study protocol. Front Neurol 2022; 13:919353. [PMID: 36299268 PMCID: PMC9588928 DOI: 10.3389/fneur.2022.919353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/09/2022] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Stroke represents the second preventable cause of death after cardiovascular disease and the third global cause of disability. In countries where national registries of the clinical quality of stroke care have been established, the publication and sharing of the collected data have led to an improvement in the quality of care and survival of patients. However, information on rehabilitation processes and outcomes is often lacking, and predictors of functional outcomes remain poorly explored. This paper describes a multicenter study protocol to implement a Stroke rehabilitation Registry, mainly based on a multidimensional assessment proposed by the Italian Society of Physical and Rehabilitation Medicine (PMIC2020), in a pilot Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation, to provide a systematic assessment of processes and outcomes and develop data-driven prediction models of functional outcomes. METHODS All patients with a diagnosis of ischemic or haemorrhagic stroke confirmed by clinical assessment, admitted to intensive rehabilitation units within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled. Measures will be taken at admission (T0), at discharge (T1), and at follow-up, 3 months (T2) and 6 months (T3) after the stroke. Assessment variables include anamnestic data, clinical and nursing complexity information and measures of body structures and function, activity and participation (PMIC2020), rehabilitation interventions, adverse events and discharge data. The modified Barthel Index will be our primary outcome. In addition to classical biostatistical analysis, learning algorithms will be cross-validated to achieve data-driven prognosis prediction models. CONCLUSIONS This study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization. CLINICAL TIAL REGISTRATION The registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed.
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Affiliation(s)
| | - Silvia Campagnini
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Teresa Baretta
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | | | - Anita Paperini
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | | | | | - Marco Baccini
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | | | - Sara Marignani
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | - Donata Bardi
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | - Alessandro Sodero
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Gemma Lombardi
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | - Erika Guolo
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | | | - Silvia Galeri
- IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | | | - Lucia Falco
- IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | | | | | - Claudio Macchi
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
| | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi onlus, Firenze, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
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23
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Cross-validation of predictive models for functional recovery after post-stroke rehabilitation. J Neuroeng Rehabil 2022; 19:96. [PMID: 36071452 PMCID: PMC9454118 DOI: 10.1186/s12984-022-01075-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/21/2022] [Indexed: 11/10/2022] Open
Abstract
Background Rehabilitation treatments and services are essential for the recovery of post-stroke patients’ functions; however, the increasing number of available therapies and the lack of consensus among outcome measures compromises the possibility to determine an appropriate level of evidence. Machine learning techniques for prognostic applications offer accurate and interpretable predictions, supporting the clinical decision for personalised treatment. The aim of this study is to develop and cross-validate predictive models for the functional prognosis of patients, highlighting the contributions of each predictor.
Methods A dataset of 278 post-stroke patients was used for the prediction of the class transition, obtained from the modified Barthel Index. Four classification algorithms were cross-validated and compared. On the best performing model on the validation set, an analysis of predictors contribution was conducted. Results The Random Forest obtained the best overall results on the accuracy (76.2%), balanced accuracy (74.3%), sensitivity (0.80), and specificity (0.68). The combination of all the classification results on the test set, by weighted voting, reached 80.2% accuracy. The predictors analysis applied on the Support Vector Machine, showed that a good trunk control and communication level, and the absence of bedsores retain the major contribution in the prediction of a good functional outcome. Conclusions Despite a more comprehensive assessment of the patients is needed, this work paves the way for the implementation of solutions for clinical decision support in the rehabilitation of post-stroke patients. Indeed, offering good prognostic accuracies for class transition and patient-wise view of the predictors contributions, it might help in a personalised optimisation of the patients’ rehabilitation path.
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