{"title":"老年2型糖尿病患者认知衰弱风险预测模型的建立与验证","authors":"Qian Yu, Hongyu Yu","doi":"10.1111/jocn.17508","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>This study aimed to develop and validate a risk prediction model for cognitive frailty in elderly patients with Type 2 diabetes mellitus (T2DM).</p><p><strong>Design: </strong>A cross-sectional design.</p><p><strong>Methods: </strong>From February to November 2023, a convenience sample of 430 older adults with T2DM was enrolled at a tertiary hospital in Jinzhou. The study analysed 22 indicators, including sociodemographic characteristics, behavioural factors, information related to T2DM, nutritional status, instrumental activities of daily living (IADL) and depression. Independent risk factors related to cognitive frailty were identified using LASSO and multivariate logistic regression analysis. A prediction model was created using a nomogram. The calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve were used to evaluate model performance. This study was reported using the STARD checklist (Data S1).</p><p><strong>Results: </strong>The study found that cognitive frailty was prevalent in 30.7% of elderly patients with T2DM. Age, physical activity, glycosylated haemoglobin (HBA1c), duration of diabetes, nutritional status, IADL and depression were predictors of cognitive frailty. The ROC curve shows that the nomogram has good discriminative power. The calibration plots demonstrated a good fit between the observed and ideal curves. Additionally, DCA highlighted the clinical application of the nomogram.</p><p><strong>Conclusions: </strong>This study provided an effective and convenient approach to evaluating the risk of cognitive frailty among elderly T2DM patients, which can help in the clinical screening of high-risk individuals.</p><p><strong>Impact: </strong>Nurses should emphasise the care of comorbid cognitive frailty in elderly patients with T2DM. The intuitive and noninvasive nomogram can help clinical nurses assess the risk probability of cognitive frailty in this population. Tailored prevention strategies for high-risk populations can be rapidly developed with this tool, significantly improving patients' quality of life.</p><p><strong>Patient or public contribution: </strong>Some patients were involved in data interpretation. No public contribution.</p>","PeriodicalId":50236,"journal":{"name":"Journal of Clinical Nursing","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Validation of a Risk Prediction Model for Cognitive Frailty in Elderly Patients With Type 2 Diabetes Mellitus.\",\"authors\":\"Qian Yu, Hongyu Yu\",\"doi\":\"10.1111/jocn.17508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>This study aimed to develop and validate a risk prediction model for cognitive frailty in elderly patients with Type 2 diabetes mellitus (T2DM).</p><p><strong>Design: </strong>A cross-sectional design.</p><p><strong>Methods: </strong>From February to November 2023, a convenience sample of 430 older adults with T2DM was enrolled at a tertiary hospital in Jinzhou. The study analysed 22 indicators, including sociodemographic characteristics, behavioural factors, information related to T2DM, nutritional status, instrumental activities of daily living (IADL) and depression. Independent risk factors related to cognitive frailty were identified using LASSO and multivariate logistic regression analysis. A prediction model was created using a nomogram. The calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve were used to evaluate model performance. This study was reported using the STARD checklist (Data S1).</p><p><strong>Results: </strong>The study found that cognitive frailty was prevalent in 30.7% of elderly patients with T2DM. Age, physical activity, glycosylated haemoglobin (HBA1c), duration of diabetes, nutritional status, IADL and depression were predictors of cognitive frailty. The ROC curve shows that the nomogram has good discriminative power. The calibration plots demonstrated a good fit between the observed and ideal curves. Additionally, DCA highlighted the clinical application of the nomogram.</p><p><strong>Conclusions: </strong>This study provided an effective and convenient approach to evaluating the risk of cognitive frailty among elderly T2DM patients, which can help in the clinical screening of high-risk individuals.</p><p><strong>Impact: </strong>Nurses should emphasise the care of comorbid cognitive frailty in elderly patients with T2DM. The intuitive and noninvasive nomogram can help clinical nurses assess the risk probability of cognitive frailty in this population. Tailored prevention strategies for high-risk populations can be rapidly developed with this tool, significantly improving patients' quality of life.</p><p><strong>Patient or public contribution: </strong>Some patients were involved in data interpretation. 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Development and Validation of a Risk Prediction Model for Cognitive Frailty in Elderly Patients With Type 2 Diabetes Mellitus.
Aims: This study aimed to develop and validate a risk prediction model for cognitive frailty in elderly patients with Type 2 diabetes mellitus (T2DM).
Design: A cross-sectional design.
Methods: From February to November 2023, a convenience sample of 430 older adults with T2DM was enrolled at a tertiary hospital in Jinzhou. The study analysed 22 indicators, including sociodemographic characteristics, behavioural factors, information related to T2DM, nutritional status, instrumental activities of daily living (IADL) and depression. Independent risk factors related to cognitive frailty were identified using LASSO and multivariate logistic regression analysis. A prediction model was created using a nomogram. The calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve were used to evaluate model performance. This study was reported using the STARD checklist (Data S1).
Results: The study found that cognitive frailty was prevalent in 30.7% of elderly patients with T2DM. Age, physical activity, glycosylated haemoglobin (HBA1c), duration of diabetes, nutritional status, IADL and depression were predictors of cognitive frailty. The ROC curve shows that the nomogram has good discriminative power. The calibration plots demonstrated a good fit between the observed and ideal curves. Additionally, DCA highlighted the clinical application of the nomogram.
Conclusions: This study provided an effective and convenient approach to evaluating the risk of cognitive frailty among elderly T2DM patients, which can help in the clinical screening of high-risk individuals.
Impact: Nurses should emphasise the care of comorbid cognitive frailty in elderly patients with T2DM. The intuitive and noninvasive nomogram can help clinical nurses assess the risk probability of cognitive frailty in this population. Tailored prevention strategies for high-risk populations can be rapidly developed with this tool, significantly improving patients' quality of life.
Patient or public contribution: Some patients were involved in data interpretation. No public contribution.
期刊介绍：
The Journal of Clinical Nursing (JCN) is an international, peer reviewed, scientific journal that seeks to promote the development and exchange of knowledge that is directly relevant to all spheres of nursing practice. The primary aim is to promote a high standard of clinically related scholarship which advances and supports the practice and discipline of nursing. The Journal also aims to promote the international exchange of ideas and experience that draws from the different cultures in which practice takes place. Further, JCN seeks to enrich insight into clinical need and the implications for nursing intervention and models of service delivery. Emphasis is placed on promoting critical debate on the art and science of nursing practice.
JCN is essential reading for anyone involved in nursing practice, whether clinicians, researchers, educators, managers, policy makers, or students. The development of clinical practice and the changing patterns of inter-professional working are also central to JCN''s scope of interest. Contributions are welcomed from other health professionals on issues that have a direct impact on nursing practice.
We publish high quality papers from across the methodological spectrum that make an important and novel contribution to the field of clinical nursing (regardless of where care is provided), and which demonstrate clinical application and international relevance.
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