Frailty Prediction Model for Elderly Diabetic Peripheral Neuropathy: A Comprehensive Approach to Enhance Patient Care and Outcomes
Introduction:
The aging global population has led to a significant rise in diabetes prevalence among the elderly. Diabetic Peripheral Neuropathy (DPN) is a common chronic complication, impacting quality of life and placing a substantial medical burden on elderly diabetic patients. Frailty, characterized by reduced physiological reserves and increased susceptibility to stressors, further exacerbates the challenges faced by these patients. This study aims to develop and validate a frailty prediction model based on clinical data, integrating demographic information, health-related data, psychosocial factors, and laboratory results to enhance the prediction ability of frailty risk in elderly DPN patients.
Methodology:
The study was conducted at a tertiary hospital in Guangzhou, China, from December 2024 to July 2025. A convenience sampling method was employed to select inpatients diagnosed with DPN. The Chinese version of the Tilburg Frailty Indicator (TFI) was used to assess frailty across three domains: physical, psychological, and social. The Pittsburgh Sleep Quality Index (PSQI) and Mini Nutritional Assessment-Short Form (MNA-SF) were applied to assess sleep quality and nutritional status, respectively. The Hospital Anxiety and Depression Scale (HADS) was utilized to screen for anxiety and depression. The Summary of Diabetes Self-Care Activities (SDSCA) was adopted to assess self-management behaviors. The Social Support Rating Scale (SSRS) was used to assess social support levels.
Results:
The study included 400 elderly DPN patients, with a frailty prevalence of 28.25%. The model revealed that age, marital status, regular exercise, PSQI score, MNA-SF score, and HADS-D score were independently associated with frailty. The nomogram developed demonstrated good discriminatory ability and predictive performance, providing a simple and efficient tool for clinicians to identify high-risk elderly DPN patients at an early stage.
Discussion:
This study highlights the importance of a comprehensive approach to frailty prediction in elderly DPN patients. By integrating physical, psychological, and social factors, the model offers a more holistic and individualized assessment of frailty risk. The findings emphasize the need for early identification and intervention to improve patient outcomes and quality of life.
Limitations:
The study's limitations include a single-center design with a limited sample size, which may restrict the generalizability of the findings. Future research should aim to expand the sample size, incorporate multi-center data, and perform external validation across diverse settings to enhance the model's reliability and broader applicability.
Conclusion:
In conclusion, the nomogram developed in this study provides a comprehensive and personalized tool for predicting frailty risk in elderly DPN patients. By integrating key physical, psychological, and social factors, the model offers a more holistic and individualized approach to frailty risk prediction, enabling proactive management and improved patient outcomes.
