Prediction of Ischemic Stroke Recurrence Based on COX Proportional Risk Regression Model and Evaluation of the Effectiveness of Patient Intensive Care Interventions
With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distr...
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| Published in | Computational and mathematical methods in medicine Vol. 2022; pp. 1 - 9 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hindawi
20.06.2022
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| Online Access | Get full text |
| ISSN | 1748-670X 1748-6718 1748-6718 |
| DOI | 10.1155/2022/8392854 |
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| Abstract | With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distress to patients and their families. As the number of strokes increases, neurological impairment becomes more and more severe, affecting patients’ ability to live, socialize, and work, and seriously reducing their quality of life. Clustered care is a combination of evidence-based linked interventions and a multidisciplinary team providing the best possible care through evidence-based research and highly operational practice, and it can improve outcomes for ischemic stroke patients more than implementation alone. This paper presents a Cox proportional risk regression-based model, using it to build the most used semi-parametric model for multifactorial survival analysis, due to its advantages of both parametric and nonparametric models, and to analyze the factors influencing survival time in study subjects with incomplete data. The proposed strategy has been found to be useful in predicting ischemic stroke recurrence and cluster care interventions for patients. |
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| AbstractList | With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distress to patients and their families. As the number of strokes increases, neurological impairment becomes more and more severe, affecting patients’ ability to live, socialize, and work, and seriously reducing their quality of life. Clustered care is a combination of evidence-based linked interventions and a multidisciplinary team providing the best possible care through evidence-based research and highly operational practice, and it can improve outcomes for ischemic stroke patients more than implementation alone. This paper presents a Cox proportional risk regression-based model, using it to build the most used semi-parametric model for multifactorial survival analysis, due to its advantages of both parametric and nonparametric models, and to analyze the factors influencing survival time in study subjects with incomplete data. The proposed strategy has been found to be useful in predicting ischemic stroke recurrence and cluster care interventions for patients. With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distress to patients and their families. As the number of strokes increases, neurological impairment becomes more and more severe, affecting patients' ability to live, socialize, and work, and seriously reducing their quality of life. Clustered care is a combination of evidence-based linked interventions and a multidisciplinary team providing the best possible care through evidence-based research and highly operational practice, and it can improve outcomes for ischemic stroke patients more than implementation alone. This paper presents a Cox proportional risk regression-based model, using it to build the most used semi-parametric model for multifactorial survival analysis, due to its advantages of both parametric and nonparametric models, and to analyze the factors influencing survival time in study subjects with incomplete data. The proposed strategy has been found to be useful in predicting ischemic stroke recurrence and cluster care interventions for patients.With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distress to patients and their families. As the number of strokes increases, neurological impairment becomes more and more severe, affecting patients' ability to live, socialize, and work, and seriously reducing their quality of life. Clustered care is a combination of evidence-based linked interventions and a multidisciplinary team providing the best possible care through evidence-based research and highly operational practice, and it can improve outcomes for ischemic stroke patients more than implementation alone. This paper presents a Cox proportional risk regression-based model, using it to build the most used semi-parametric model for multifactorial survival analysis, due to its advantages of both parametric and nonparametric models, and to analyze the factors influencing survival time in study subjects with incomplete data. The proposed strategy has been found to be useful in predicting ischemic stroke recurrence and cluster care interventions for patients. |
| Author | Lu, Ting Wang, Yun |
| AuthorAffiliation | Department of Neurology Nursing, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, Sichuan 610072, China |
| AuthorAffiliation_xml | – name: Department of Neurology Nursing, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, Sichuan 610072, China |
| Author_xml | – sequence: 1 givenname: Yun orcidid: 0000-0003-4558-1964 surname: Wang fullname: Wang, Yun organization: Department of Neurology NursingSichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChinese Academy of Sciences Sichuan Translational Medicine Research HospitalChengduSichuan 610072Chinauestc.edu.cn – sequence: 2 givenname: Ting orcidid: 0000-0001-5165-3691 surname: Lu fullname: Lu, Ting organization: Department of Neurology NursingSichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChinese Academy of Sciences Sichuan Translational Medicine Research HospitalChengduSichuan 610072Chinauestc.edu.cn |
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| Cites_doi | 10.1177/10760296221090503 10.1007/s00380-019-01445-7 10.1016/j.jstrokecerebrovasdis.2019.104415 10.2147/JIR.S328383 10.1161/STROKEAHA.120.029740 10.1186/s12883-022-02588-3 10.1007/s11011-021-00725-4 10.1161/STROKEAHA.120.032424 10.1111/jth.15448 10.1109/tetc.2020.2971831 10.1002/brb3.1369 10.1111/jth.14714 10.1109/TVT.2020.2989297 10.2147/RMHP.S289761 10.1161/STROKEAHA.120.032634 10.12122/j.issn.1673-4254.2022.01.16 10.3390/nu14071337 10.1109/TCSS.2019.2917335 10.5551/jat.52373 10.1007/s00234-020-02418-8 10.1007/s12031-021-01889-5 |
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| Copyright | Copyright © 2022 Yun Wang and Ting Lu. Copyright © 2022 Yun Wang and Ting Lu. 2022 |
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| Title | Prediction of Ischemic Stroke Recurrence Based on COX Proportional Risk Regression Model and Evaluation of the Effectiveness of Patient Intensive Care Interventions |
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