1
|
Abujaber AA, Imam Y, Albalkhi I, Yaseen S, Nashwan AJ, Akhtar N. Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke. BMC Neurol 2024; 24:156. [PMID: 38714968 PMCID: PMC11075305 DOI: 10.1186/s12883-024-03638-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. METHODS We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. RESULTS The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. CONCLUSION This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.
Collapse
Affiliation(s)
- Ahmad A Abujaber
- Nursing Department, Hamad Medical Corporation (HMC), Doha, Qatar
| | - Yahia Imam
- Neurology Section, Neuroscience Institute, Hamad Medical Corporation (HMC), Doha, Qatar
| | - Ibrahem Albalkhi
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
- Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St, London, WC1N 3JH, UK
| | - Said Yaseen
- School of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Abdulqadir J Nashwan
- Nursing Department, Hamad Medical Corporation (HMC), Doha, Qatar.
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
| | - Naveed Akhtar
- Neuroradiology Department, Neuroscience Institute, Hamad Medical Corporation (HMC), Doha, Qatar
| |
Collapse
|
2
|
Abujaber AA, Albalkhi I, Imam Y, Nashwan A, Akhtar N, Alkhawaldeh IM. Machine learning-based prognostication of mortality in stroke patients. Heliyon 2024; 10:e28869. [PMID: 38601648 PMCID: PMC11004568 DOI: 10.1016/j.heliyon.2024.e28869] [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: 09/11/2023] [Revised: 02/22/2024] [Accepted: 03/26/2024] [Indexed: 04/12/2024] Open
Abstract
Objectives Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. Results The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. Conclusions This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.
Collapse
Affiliation(s)
| | - Ibrahem Albalkhi
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
- Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St, London WC1N 3JH, United Kingdom
| | - Yahia Imam
- Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar
| | | | - Naveed Akhtar
- Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar
| | | |
Collapse
|
3
|
Zhao Z, Zhang Y, Su J, Yang L, Pang L, Gao Y, Wang H. A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke. Front Neurol 2024; 15:1367854. [PMID: 38606275 PMCID: PMC11007047 DOI: 10.3389/fneur.2024.1367854] [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: 01/09/2024] [Accepted: 03/08/2024] [Indexed: 04/13/2024] Open
Abstract
Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.
Collapse
Affiliation(s)
- Zijian Zhao
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yuanyuan Zhang
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Jiuhui Su
- Department of Orthopedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning Province, China
| | - Lianbo Yang
- Department of Reparative and Reconstructive Surgery, The Second Hospital of Dalian Medical University, Dalian Liaoning Province, China
| | - Luhang Pang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yingshan Gao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| |
Collapse
|
4
|
Axford D, Sohel F, Abedi V, Zhu Y, Zand R, Barkoudah E, Krupica T, Iheasirim K, Sharma UM, Dugani SB, Takahashi PY, Bhagra S, Murad MH, Saposnik G, Yousufuddin M. Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:109-122. [PMID: 38505491 PMCID: PMC10944684 DOI: 10.1093/ehjdh/ztad073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/14/2023] [Accepted: 10/30/2023] [Indexed: 03/21/2024]
Abstract
Aims We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method-based risk prediction tools for outcome prediction after AIS in new data sets.
Collapse
Affiliation(s)
- Daniel Axford
- Department of Information Technology, Mathematics and Statistics, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Australia
| | - Ferdous Sohel
- Department of Information Technology, Mathematics and Statistics, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Australia
| | - Vida Abedi
- Department of Public Health Science, Penn State College of Medicine, Hershey, PA, USA
| | - Ye Zhu
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Ramin Zand
- Neuroscience Institute, Geisinger Health System, 100 North Academy Ave, Danville, PA 17822, USA
- Neuroscience Institute, The Pennsylvania State University, Hershey, PA 17033, USA
| | - Ebrahim Barkoudah
- Internal Medicine/Hospital Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, USA
| | - Troy Krupica
- Internal Medicine/Hospital Medicine, West Virginial University, Morgantown, WV, USA
| | - Kingsley Iheasirim
- Internal Medicine/Hospital Internal Medicine, Mayo Clinic Health System, Mankato, MN, USA
| | - Umesh M Sharma
- Hospital Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Sagar B Dugani
- Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Sumit Bhagra
- Endocrinology, Diabetes and Metabolism, Mayo Clinic Health System, Austin, MN, USA
| | - Mohammad H Murad
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, MN, USA
| | - Gustavo Saposnik
- Stroke Outcomes and Decision Neuroscience Research Unit, Division of Neurology, Department of Medicine and Li Ka Shing Knowledge Institute, St.Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Mohammed Yousufuddin
- Hospital Internal Medicine, Mayo Clinic Health System, 1000 1st Drive NW, Austin, MN 55912, USA
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
Hu W, Jin T, Pan Z, Xu H, Yu L, Chen T, Zhang W, Jiang H, Yang W, Xu J, Zhu F, Dai H. An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in intensive care unit: Development and external validation of ICU-ISPM. Comput Biol Med 2023; 166:107577. [PMID: 37852108 DOI: 10.1016/j.compbiomed.2023.107577] [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: 08/07/2023] [Revised: 09/13/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023]
Abstract
Ischemic stroke (IS) is a common and severe condition that requires intensive care unit (ICU) admission, with high mortality and variable prognosis. Accurate and reliable predictive tools that enable early risk stratification can facilitate interventions to improve patient outcomes; however, such tools are currently lacking. In this study, we developed and validated novel ensemble learning models based on soft voting and stacking methods to predict in-hospital mortality from IS in the ICU using two public databases: MIMIC-IV and eICU-CRD. Additionally, we identified the key predictors of mortality and developed a user-friendly online prediction tool for clinical use. The soft voting ensemble model, named ICU-ISPM, achieved an AUROC of 0.861 (95% CI: 0.829-0.892) and 0.844 (95% CI: 0.819-0.869) in the internal and external test cohorts, respectively. It significantly outperformed the APACHE scoring system and was more robust than individual models. ICU-ISPM obtained the highest performance compared to other models in similar studies. Using the SHAP method, the model was interpretable, revealing that GCS score, age, and intubation were the most important predictors of mortality. This model also provided a risk stratification system that can effectively distinguish between low-, medium-, and high-risk patients. Therefore, the ICU-ISPM is an accurate, reliable, interpretable, and clinically applicable tool, which is expected to assist clinicians in stratifying IS patients by the risk of mortality and rationally allocating medical resources. Based on ICU-ISPM, an online risk prediction tool was further developed, which was freely available at: http://ispm.idrblab.cn/.
Collapse
Affiliation(s)
- Wei Hu
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Tingting Jin
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Huimin Xu
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Lingyan Yu
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Tingting Chen
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Huifang Jiang
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Wenjun Yang
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Junjun Xu
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Feng Zhu
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China; College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Haibin Dai
- Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China; Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou, 310009, China.
| |
Collapse
|
7
|
Abujaber AA, Albalkhi I, Imam Y, Nashwan AJ, Yaseen S, Akhtar N, Alkhawaldeh IM. Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning. J Pers Med 2023; 13:1555. [PMID: 38003870 PMCID: PMC10672468 DOI: 10.3390/jpm13111555] [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: 09/07/2023] [Revised: 09/26/2023] [Accepted: 10/11/2023] [Indexed: 11/26/2023] Open
Abstract
(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.
Collapse
Affiliation(s)
- Ahmad A. Abujaber
- Nursing Department, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
| | - Ibrahem Albalkhi
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St., London WC1N 3JH, UK
| | - Yahia Imam
- Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
| | | | - Said Yaseen
- School of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Naveed Akhtar
- Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
| | | |
Collapse
|
8
|
Li J, Chaudhary D, Sharma V, Sharma V, Avula V, Ssentongo P, Wolk DM, Zand R, Abedi V. An integrated pipeline for prediction of Clostridioides difficile infection. Sci Rep 2023; 13:16532. [PMID: 37783691 PMCID: PMC10545794 DOI: 10.1038/s41598-023-41753-7] [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: 01/10/2022] [Accepted: 08/31/2023] [Indexed: 10/04/2023] Open
Abstract
With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.
Collapse
Affiliation(s)
- Jiang Li
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - Durgesh Chaudhary
- Neuroscience Institute, Geisinger Health System, Danville, PA, USA
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA, 17033, USA
| | - Vaibhav Sharma
- Geisinger Commonwealth School of Medicine, Danville, PA, USA
| | - Vishakha Sharma
- College of Osteopathic Medicine, Kansas City University, Kansas City, MO, USA
| | - Venkatesh Avula
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - Paddy Ssentongo
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, USA
| | - Donna M Wolk
- Molecular and Microbial Diagnostics and Development, Geisinger Medical Center, Danville, PA, USA
| | - Ramin Zand
- Neuroscience Institute, Geisinger Health System, Danville, PA, USA
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA, 17033, USA
| | - Vida Abedi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA.
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, USA.
| |
Collapse
|
9
|
Miceli G, Basso MG, Rizzo G, Pintus C, Cocciola E, Pennacchio AR, Tuttolomondo A. Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review. Biomedicines 2023; 11:biomedicines11041138. [PMID: 37189756 DOI: 10.3390/biomedicines11041138] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/29/2023] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.
Collapse
Affiliation(s)
- Giuseppe Miceli
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Maria Grazia Basso
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Giuliana Rizzo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Chiara Pintus
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Elena Cocciola
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Andrea Roberta Pennacchio
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| |
Collapse
|
10
|
Huang R, Liu J, Wan TK, Siriwanna D, Woo YMP, Vodencarevic A, Wong CW, Chan KHK. Stroke mortality prediction based on ensemble learning and the combination of structured and textual data. Comput Biol Med 2023; 155:106176. [PMID: 36805232 DOI: 10.1016/j.compbiomed.2022.106176] [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: 04/26/2022] [Revised: 09/12/2022] [Accepted: 10/01/2022] [Indexed: 11/23/2022]
Abstract
For severe cerebrovascular diseases such as stroke, the prediction of short-term mortality of patients has tremendous medical significance. In this study, we combined machine learning models Random Forest classifier (RF), Adaptive Boosting (AdaBoost), Extremely Randomised Trees (ExtraTree) classifier, XGBoost classifier, TabNet, and DistilBERT to construct a multi-level prediction model that used bioassay data and radiology text reports from haemorrhagic and ischaemic stroke patients to predict six-month mortality. The performances of the prediction models were measured using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), precision, recall, and F1-score. The prediction models were built with the use of data from 19,616 haemorrhagic stroke patients and 50,178 ischaemic stroke patients. Novel six-month mortality prediction models for these patients were developed, which enhanced the performance of the prediction models by combining laboratory test data, structured data, and textual radiology report data. The achieved performances were as follows: AUROC = 0.89, AUPRC = 0.70, precision = 0.52, recall = 0.78, and F1 score = 0.63 for haemorrhagic patients, and AUROC = 0.88, AUPRC = 0.54, precision = 0.34, recall = 0.80, and F1 score = 0.48 for ischaemic patients. Such models could be used for mortality risk assessment and early identification of high-risk stroke patients. This could contribute to more efficient utilisation of healthcare resources for stroke survivors.
Collapse
Affiliation(s)
- Ruixuan Huang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Jundong Liu
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Tsz Kin Wan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Damrongrat Siriwanna
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | | | | | - Chi Wah Wong
- Department of Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA, 91010, United States
| | - Kei Hang Katie Chan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China; Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China; Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Department of Medicine, The Warrant Alpert School of Medicine, Brown University, Providence, RI, United States.
| |
Collapse
|
11
|
Kim DY, Choi KH, Kim JH, Hong J, Choi SM, Park MS, Cho KH. Deep learning-based personalised outcome prediction after acute ischaemic stroke. J Neurol Neurosurg Psychiatry 2023; 94:369-378. [PMID: 36650037 DOI: 10.1136/jnnp-2022-330230] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 12/06/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied. METHODS A total of 8590 patients with AIS admitted within 5 days of symptom onset were enrolled. The primary outcome was the occurrence of MACEs (a composite of stroke, acute myocardial infarction or death) over 12 months. The performance of deep learning models (DeepSurv and Deep-Survival-Machines (DeepSM)) and traditional survival models (Cox proportional hazards (CoxPH) and random survival forest (RSF)) were compared using the time-dependent concordance index ([Formula: see text] index). RESULTS Given the top 1 to all 60 clinical factors according to feature importance, CoxPH and RSF yielded [Formula: see text] index of 0.7236-0.8222 and 0.7279-0.8335, respectively. Adding image features improved the performance of deep learning models and traditional models assisted by deep learning models. DeepSurv and DeepSM yielded the best [Formula: see text] index of 0.8496 and 0.8531 when images were added to all 39 relevant clinical factors, respectively. In feature importance, brain image was consistently ranked highly. Deep learning models automatically extracted the image features directly from personalised brain images and predicted the risk and date of future MACEs at the individual level. CONCLUSIONS Deep learning models using clinical data and brain images could improve the prediction of MACEs and provide personalised outcome prediction for patients with AIS. Deep learning models will allow us to develop more accurate and tailored prognostic prediction systems that outperform traditional models.
Collapse
Affiliation(s)
- Doo-Young Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea (the Republic of)
| | - Kang-Ho Choi
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of) .,Department of Biomedical Sciences, Chonnam National University, Gwangju, Korea (the Republic of)
| | - Ja-Hae Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea (the Republic of) .,Department of Nuclear Medicine, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
| | - Jina Hong
- Department of Biomedical Sciences, Chonnam National University, Gwangju, Korea (the Republic of)
| | - Seong-Min Choi
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
| | - Man-Seok Park
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
| | - Ki-Hyun Cho
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
| |
Collapse
|
12
|
Nambiar V, Raj M, Vasudevan D, Bhaskaran R, Sudevan R. One-year mortality after acute stroke: a prospective cohort study from a comprehensive stroke care centre, Kerala, India. BMJ Open 2022; 12:e061258. [PMID: 36442894 PMCID: PMC9710353 DOI: 10.1136/bmjopen-2022-061258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES The primary objective was to report the 1-year all-cause mortality among patients with stroke. The secondary objectives were (1) to report the mortality stratified by type of stroke and sex and (2) to report predictors of 1-year mortality among patients with stroke. DESIGN A prospective cohort study. SETTING Institutional-stroke care unit of a tertiary care hospital PARTICIPANTS: Patients who were treated in the study institution during 2016-2020 for acute stroke and were followed up for a period of 1 year after stroke in the same institution. MAIN OUTCOME MEASURES The main outcome measures were the mortality proportion of any stroke and first ever stroke cohorts at select time points, including in-hospital stay, along with 2 weeks, 2 months, 6 months and 1 year after index stroke. The secondary outcomes were (1) mortality proportions stratified by sex and type of stroke and (2) predictors of 1-year mortality for any stroke and first ever stroke. RESULTS We recruited a total of 1336 patients. The mean age of participants was 61.6 years (13.5 years). The mortality figures for 2 weeks, 2 months, 6 months and 12 months after discharge were 79 (5.9%), 88 (6.7%), 101 (7.6%) and 114 (8.5%), respectively, in the full cohort. The in-hospital mortality was 45 (3.4%). The adjusted analysis revealed 3 predictors for 1-year mortality after first ever stroke-age, pre-treatment National Institutes of Health Stroke Scale (NIHSS) score and Modified Rankin Scale (mRS) score at baseline. The same for the full cohort had only two predictors-age and pre-treatment NIHSS score. CONCLUSION Mortality of stroke at 1-year follow-up in the study population is low in comparison to several studies published earlier. The predictors of 1-year mortality after stroke included age, NIHSS score at baseline and mRS score at baseline.
Collapse
Affiliation(s)
- Vivek Nambiar
- Division of Stroke, Department of Neurology, Amrita Institute of Medical Sciences and Research Centre, Cochin, India
| | - Manu Raj
- Department of Pediatrics and Pediatric Cardiology, Amrita Institute of Medical Sciences and Research Centre, Cochin, India
| | - Damodaran Vasudevan
- Department of Health Sciences Research, Amrita Institute of Medical Sciences and Research Centre, Cochin, India
| | - Renjitha Bhaskaran
- Department of Biostatistics, Amrita Institute of Medical Sciences, Cochin, India
| | - Remya Sudevan
- Department of Health Sciences Research, Amrita Institute of Medical Sciences, Amrita viswa vidyapeetham, Cochin, India
| |
Collapse
|
13
|
Hwangbo L, Kang YJ, Kwon H, Lee JI, Cho HJ, Ko JK, Sung SM, Lee TH. Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients. Sci Rep 2022; 12:17389. [PMID: 36253488 PMCID: PMC9576722 DOI: 10.1038/s41598-022-22323-9] [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: 01/31/2022] [Accepted: 10/12/2022] [Indexed: 01/10/2023] Open
Abstract
Patients with acute ischemic stroke can benefit from reperfusion therapy. Nevertheless, there are gray areas where initiation of reperfusion therapy is neither supported nor contraindicated by the current practice guidelines. In these situations, a prediction model for mortality can be beneficial in decision-making. This study aimed to develop a mortality prediction model for acute ischemic stroke patients not receiving reperfusion therapies using a stacking ensemble learning model. The model used an artificial neural network as an ensemble classifier. Seven base classifiers were K-nearest neighbors, support vector machine, extreme gradient boosting, random forest, naive Bayes, artificial neural network, and logistic regression algorithms. From the clinical data in the International Stroke Trial database, we selected a concise set of variables assessable at the presentation. The primary study outcome was all-cause mortality at 6 months. Our stacking ensemble model predicted 6-month mortality with acceptable performance in ischemic stroke patients not receiving reperfusion therapy. The area under the curve of receiver-operating characteristics, accuracy, sensitivity, and specificity of the stacking ensemble classifier on a put-aside validation set were 0.783 (95% confidence interval 0.758-0.808), 71.6% (69.3-74.2), 72.3% (69.2-76.4%), and 70.9% (68.9-74.3%), respectively.
Collapse
Affiliation(s)
- Lee Hwangbo
- grid.412588.20000 0000 8611 7824Department of Radiology, Pusan National University Hospital, Gudeokro 179, Seogu, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea
| | - Yoon Jung Kang
- grid.412588.20000 0000 8611 7824Department of Neurology, Pusan National University Hospital, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea
| | - Hoon Kwon
- grid.412588.20000 0000 8611 7824Department of Radiology, Pusan National University Hospital, Gudeokro 179, Seogu, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea
| | - Jae Il Lee
- grid.412588.20000 0000 8611 7824Department of Neurosurgery, Pusan National University Hospital, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea
| | - Han-Jin Cho
- grid.412588.20000 0000 8611 7824Department of Neurology, Pusan National University Hospital, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea
| | - Jun-Kyeung Ko
- grid.412588.20000 0000 8611 7824Department of Neurosurgery, Pusan National University Hospital, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea
| | - Sang Min Sung
- grid.412588.20000 0000 8611 7824Department of Neurology, Pusan National University Hospital, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea ,grid.262229.f0000 0001 0719 8572College of Medicine, Pusan National University, Yangsan, 50612 South Korea
| | - Tae Hong Lee
- grid.412588.20000 0000 8611 7824Department of Radiology, Pusan National University Hospital, Gudeokro 179, Seogu, Pusan, 49241 South Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Pusan, 49241 South Korea ,grid.262229.f0000 0001 0719 8572College of Medicine, Pusan National University, Yangsan, 50612 South Korea
| |
Collapse
|
14
|
Li J, Abedi V, Zand R. Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine. J Clin Med 2022; 11:jcm11205980. [PMID: 36294301 PMCID: PMC9604604 DOI: 10.3390/jcm11205980] [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: 07/20/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 12/03/2022] Open
Abstract
Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.
Collapse
Affiliation(s)
- Jiang Li
- Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA 17822, USA
| | - Vida Abedi
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
- Correspondence: (V.A.); (R.Z.)
| | - Ramin Zand
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
- Neuroscience Institute, Geisinger Health System, 100 North Academy Avenue, Danville, PA 17822, USA
- Correspondence: (V.A.); (R.Z.)
| |
Collapse
|
15
|
Li J, Chaudhary D, Griessenauer CJ, Carey DJ, Zand R, Abedi V. Predicting mortality among ischemic stroke patients using pathways-derived polygenic risk scores. Sci Rep 2022; 12:12358. [PMID: 35853973 PMCID: PMC9296485 DOI: 10.1038/s41598-022-16510-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/11/2022] [Indexed: 12/19/2022] Open
Abstract
We aim to determine whether ischemic stroke(IS)-related PRSs are also associated with and further predict 3-year all-cause mortality. 1756 IS patients with European ancestry were randomly split into training (n = 1226) and testing (n = 530) groups with 3-year post-event observations. Univariate Cox proportional hazards regression model (CoxPH) was used for primary screening of individual prognostic PRSs. Only the significantly associated PRSs and clinical risk factors with the same direction for a causal relationship with IS were used to construct a multivariate CoxPH. Feature selection was conducted by the LASSO method. After feature selection, a prediction model with 11 disease-associated pathway-specific PRSs outperformed the base model, as demonstrated by a higher concordance index (0.751, 95%CI [0.693–0.809] versus 0.729, 95%CI [0.676–0.782]) in the testing sample. A PRS derived from endothelial cell apoptosis showed independent predictability in the multivariate CoxPH (Hazard Ratio = 1.193 [1.027–1.385], p = 0.021). These PRSs fine-tuned the model by better stratifying high, intermediate, and low-risk groups. Several pathway-specific PRSs were associated with clinical risk factors in an age-dependent manner and further confirmed some known etiologies of IS and all-cause mortality. In conclusion, Pathway-specific PRSs for IS are associated with all-cause mortality, and the integrated multivariate risk model provides prognostic value in this context.
Collapse
Affiliation(s)
- Jiang Li
- Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, 17822, USA
| | - Durgesh Chaudhary
- Neuroscience Institute, Geisinger Health System, Danville, PA, 17822, USA
| | - Christoph J Griessenauer
- Neuroscience Institute, Geisinger Health System, Danville, PA, 17822, USA.,Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
| | - David J Carey
- Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, 17822, USA
| | - Ramin Zand
- Neuroscience Institute, Geisinger Health System, Danville, PA, 17822, USA.
| | - Vida Abedi
- Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, 17822, USA. .,Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, USA.
| |
Collapse
|
16
|
Liu D, Ji Q, Cheng Y, Liu M, Zhang B, Mei Q, Huan M, Zhou S. Cyclosporine A loaded brain targeting nanoparticle to treat cerebral ischemia/reperfusion injury in mice. J Nanobiotechnology 2022; 20:256. [PMID: 35658867 PMCID: PMC9164331 DOI: 10.1186/s12951-022-01474-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/23/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Ischemic stroke is one of the main causes of death and disability in the world. The treatment for ischemic stroke is to restore blood perfusion as soon as possible. However, when ischemic brain tissue is re-perfused by blood, the mitochondrial permeability transition pore (mPTP) in neuron and microglia is excessively opened, resulting in the apoptosis of neuron and nerve inflammation. This aggravates nerve injury. Cyclosporine A (CsA) inhibits the over-opening of mPTP, subsequently reducing the release of ROS and the apoptosis of cerebral ischemia/reperfusion injured neuron and microglia. However, CsA is insoluble in water and present in high concentrations in lymphatic tissue. Herein, cerebral infarction tissue targeted nanoparticle (CsA@HFn) was developed to treat cerebral ischemia/reperfusion injury. RESULTS CsA@HFn efficiently penetrated the blood-brain barrier (BBB) and selectively accumulated in ischemic area, inhibiting the opening of mPTP and ROS production in neuron. This subsequently reduced the apoptosis of neuron and the damage of BBB. Consequently, CsA@HFn significantly reduced the infarct area. Moreover, CsA@HFn inhibited the recruitment of astrocytes and microglia in ischemic region and polarized microglia into M2 type microglia, which subsequently alleviated the nerve inflammation. CONCLUSIONS CsA@HFn showed a significant therapeutic effect on cerebral ischemia/reperfusion injury by alleviating the apoptosis of neuron, nerve inflammation and the damage of BBB in ischemic area. CsA@HFn has great potential in the treatment of ischemic stroke.
Collapse
Affiliation(s)
- Daozhou Liu
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| | - Qifeng Ji
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| | - Ying Cheng
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| | - Miao Liu
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| | - Bangle Zhang
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| | - Qibing Mei
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| | - Menglei Huan
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| | - Siyuan Zhou
- grid.233520.50000 0004 1761 4404Department of Pharmaceutics, School of Pharmacy, Air Force Medical University, Changle West Road 169, Xi’an, 710032 Shaanxi China
| |
Collapse
|
17
|
Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021; 10:jcm10235710. [PMID: 34884412 PMCID: PMC8658222 DOI: 10.3390/jcm10235710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.
Collapse
|
18
|
Imputation of missing values for electronic health record laboratory data. NPJ Digit Med 2021; 4:147. [PMID: 34635760 PMCID: PMC8505441 DOI: 10.1038/s41746-021-00518-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/13/2021] [Indexed: 11/08/2022] Open
Abstract
Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients' comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.
Collapse
|