{"title":"基于智能康复护理模式的老年脑卒中患者跌倒风险预防研究。","authors":"Xiaohui Li, Mengmeng Liu, Chunyan Wang","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Fall is a public health problem that cannot be ignored by elderly stroke patients, and rehabilitation care plays an important role in the rehabilitation process of elderly stroke patients.</p><p><strong>Objective: </strong>To investigate the prevention effect of fall risk in elderly stroke patients under the intelligent model of rehabilitation care.</p><p><strong>Methods: </strong>The general data of elderly patients who were diagnosed as stroke and admitted to our hospital between June 2021 and June 2022 were retrospectively analyzed, with exclusion like unclear clinical data or combined with other severe organ insufficiency. A total of 150 of them were selected for the study, and the patients were divided into a fall group and a non-fall group according to whether they had a fall or not. The factors associated with falls in stroke patients were analyzed univariately, and the rehabilitation care intelligence model of the predictive model of falls in stroke patients was established using multiple covariance ridge regression analysis to observe the predictive value of patients' risk of falling in the rehabilitation care intelligence model.</p><p><strong>Results: </strong>Results of multiple covariance ridge regression analysis to build the model showed age (P < .001), low MNA-SF score (P < .001), hypertension (P = .035), anaemia(P = .048), gout (P < .001), assistive devices (P = .002), visual impairment (P = .033), elevated ALB (P < .001), and elevated HGB (P < .001) as risk factors for falls in stroke patients. The diagnostic threshold for screening elderly stroke patients for falls based on risk factors was 0.272, with a sensitivity of 90.7%, specificity of 98.1% and an area under the ROC curve of 0.976 (P < .05), which was superior to other single indicators in terms of diagnostic value. The calibration of the prediction model, based on the Hosmer and Lemeshow test of goodness of fit, showed P = 1.14, indicating a high calibration of the prediction model.</p><p><strong>Conclusion: </strong>There are many risk factors for falls in stroke elderly patients, such as low MNA-SF score, gout, elevated ALB, and elevated HGB. Building a rehabilitation nursing intelligent model based on the above inducement factors can reduce the risk of patients falling to a certain extent, and the prediction model has a high degree of calibration. Therefore, a simple and standardized intelligent rehabilitation nursing model for stroke patients in the early stage can effectively prevent the occurrence of falls.</p>","PeriodicalId":7571,"journal":{"name":"Alternative therapies in health and medicine","volume":" ","pages":"107-113"},"PeriodicalIF":1.9000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on the Prevention of Fall Risk in Elderly Stroke Patients Based on an Intelligent Model of Rehabilitation Care.\",\"authors\":\"Xiaohui Li, Mengmeng Liu, Chunyan Wang\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Fall is a public health problem that cannot be ignored by elderly stroke patients, and rehabilitation care plays an important role in the rehabilitation process of elderly stroke patients.</p><p><strong>Objective: </strong>To investigate the prevention effect of fall risk in elderly stroke patients under the intelligent model of rehabilitation care.</p><p><strong>Methods: </strong>The general data of elderly patients who were diagnosed as stroke and admitted to our hospital between June 2021 and June 2022 were retrospectively analyzed, with exclusion like unclear clinical data or combined with other severe organ insufficiency. A total of 150 of them were selected for the study, and the patients were divided into a fall group and a non-fall group according to whether they had a fall or not. The factors associated with falls in stroke patients were analyzed univariately, and the rehabilitation care intelligence model of the predictive model of falls in stroke patients was established using multiple covariance ridge regression analysis to observe the predictive value of patients' risk of falling in the rehabilitation care intelligence model.</p><p><strong>Results: </strong>Results of multiple covariance ridge regression analysis to build the model showed age (P < .001), low MNA-SF score (P < .001), hypertension (P = .035), anaemia(P = .048), gout (P < .001), assistive devices (P = .002), visual impairment (P = .033), elevated ALB (P < .001), and elevated HGB (P < .001) as risk factors for falls in stroke patients. The diagnostic threshold for screening elderly stroke patients for falls based on risk factors was 0.272, with a sensitivity of 90.7%, specificity of 98.1% and an area under the ROC curve of 0.976 (P < .05), which was superior to other single indicators in terms of diagnostic value. The calibration of the prediction model, based on the Hosmer and Lemeshow test of goodness of fit, showed P = 1.14, indicating a high calibration of the prediction model.</p><p><strong>Conclusion: </strong>There are many risk factors for falls in stroke elderly patients, such as low MNA-SF score, gout, elevated ALB, and elevated HGB. Building a rehabilitation nursing intelligent model based on the above inducement factors can reduce the risk of patients falling to a certain extent, and the prediction model has a high degree of calibration. Therefore, a simple and standardized intelligent rehabilitation nursing model for stroke patients in the early stage can effectively prevent the occurrence of falls.</p>\",\"PeriodicalId\":7571,\"journal\":{\"name\":\"Alternative therapies in health and medicine\",\"volume\":\" \",\"pages\":\"107-113\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alternative therapies in health and medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INTEGRATIVE & COMPLEMENTARY MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alternative therapies in health and medicine","FirstCategoryId":"3","ListUrlMain":"","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INTEGRATIVE & COMPLEMENTARY MEDICINE","Score":null,"Total":0}
Study on the Prevention of Fall Risk in Elderly Stroke Patients Based on an Intelligent Model of Rehabilitation Care.
Background: Fall is a public health problem that cannot be ignored by elderly stroke patients, and rehabilitation care plays an important role in the rehabilitation process of elderly stroke patients.
Objective: To investigate the prevention effect of fall risk in elderly stroke patients under the intelligent model of rehabilitation care.
Methods: The general data of elderly patients who were diagnosed as stroke and admitted to our hospital between June 2021 and June 2022 were retrospectively analyzed, with exclusion like unclear clinical data or combined with other severe organ insufficiency. A total of 150 of them were selected for the study, and the patients were divided into a fall group and a non-fall group according to whether they had a fall or not. The factors associated with falls in stroke patients were analyzed univariately, and the rehabilitation care intelligence model of the predictive model of falls in stroke patients was established using multiple covariance ridge regression analysis to observe the predictive value of patients' risk of falling in the rehabilitation care intelligence model.
Results: Results of multiple covariance ridge regression analysis to build the model showed age (P < .001), low MNA-SF score (P < .001), hypertension (P = .035), anaemia(P = .048), gout (P < .001), assistive devices (P = .002), visual impairment (P = .033), elevated ALB (P < .001), and elevated HGB (P < .001) as risk factors for falls in stroke patients. The diagnostic threshold for screening elderly stroke patients for falls based on risk factors was 0.272, with a sensitivity of 90.7%, specificity of 98.1% and an area under the ROC curve of 0.976 (P < .05), which was superior to other single indicators in terms of diagnostic value. The calibration of the prediction model, based on the Hosmer and Lemeshow test of goodness of fit, showed P = 1.14, indicating a high calibration of the prediction model.
Conclusion: There are many risk factors for falls in stroke elderly patients, such as low MNA-SF score, gout, elevated ALB, and elevated HGB. Building a rehabilitation nursing intelligent model based on the above inducement factors can reduce the risk of patients falling to a certain extent, and the prediction model has a high degree of calibration. Therefore, a simple and standardized intelligent rehabilitation nursing model for stroke patients in the early stage can effectively prevent the occurrence of falls.
期刊介绍：
Launched in 1995, Alternative Therapies in Health and Medicine has a mission to promote the art and science of integrative medicine and a responsibility to improve public health. We strive to maintain the highest standards of ethical medical journalism independent of special interests that is timely, accurate, and a pleasure to read. We publish original, peer-reviewed scientific articles that provide health care providers with continuing education to promote health, prevent illness, and treat disease. Alternative Therapies in Health and Medicine was the first journal in this field to be indexed in the National Library of Medicine. In 2006, 2007, and 2008, ATHM had the highest impact factor ranking of any independently published peer-reviewed CAM journal in the United States—meaning that its research articles were cited more frequently than any other journal’s in the field.
Alternative Therapies in Health and Medicine does not endorse any particular system or method but promotes the evaluation and appropriate use of all effective therapeutic approaches. Each issue contains a variety of disciplined inquiry methods, from case reports to original scientific research to systematic reviews. The editors encourage the integration of evidence-based emerging therapies with conventional medical practices by licensed health care providers in a way that promotes a comprehensive approach to health care that is focused on wellness, prevention, and healing. Alternative Therapies in Health and Medicine hopes to inform all licensed health care practitioners about developments in fields other than their own and to foster an ongoing debate about the scientific, clinical, historical, legal, political, and cultural issues that affect all of health care.
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