Abstract
The objective of this research was to identify the factors contributing to the decline in handgrip strength among middle-aged and elderly individuals with this condition. In addition, an algorithmic model for the detection of probable sarcopenia will be developed. This research encompassed the collection and evaluation of fundamental data, laboratory indicators, body composition metrics, and lifestyle factors. Patients were diagnosed with handgrip strength loss according to the diagnostic criteria established by the Asian Working Group for Sarcopenia in 2019, specifically for "probable sarcopenia". A multifactorial logistic regression model was employed to discern the independent variables that significantly influence the occurrence of handgrip strength reduction among patients suffering from coronary artery disease. An internal validation of this model was conducted using the bootstrap repetitive sampling technique. The predictive efficacy of the model was assessed through comparisons of the area under the receiver operating characteristic curve, the calibration curve, and the decision curve for the subjects. High gait speed (OR 0.015; 95%CI 0.001-0.232), high calf circumference (OR 0.650; 95%CI 0.503-0.839), and high albumin level (OR 0.714; 95%CI 0.572-0.891) were significantly and negatively associated with reduced handgrip strength, which were protective factors for the development of probable sarcopenia. (all p < 0.05). Gait speed, calf circumference, and serum albumin levels were independent factors that influenced the likelihood of developing probable sarcopenia. The nomogram model based on these factors has a certain predictive value of probable sarcopenia, which can guide the development of disease prevention strategies.