Abstract
PURPOSE: To establish, validate, and clinically evaluate a nomogram for predicting the risk of sarcopenia in patients with peripheral arterial disease (PAD) based on clinical and lower extremity computed tomography angiography (LE-CTA) imaging characteristics. METHODS: Clinical data and CTA imaging features from 281 PAD patients treated between January 1, 2019, and May 1, 2023, at two hospitals were retrospectively analyzed using binary logistic regression to identify the independent risk factors for sarcopenia. These identified risk factors were used to develop a predictive nomogram. The nomogram's effectiveness was assessed through various metrics, including the receiver operating characteristic (ROC) curve, area under the curve (AUC), concordance index (C-index), Hosmer-Lemeshow (HL) test, and calibration curve. Its clinical utility was demonstrated using decision curve analysis (DCA). RESULTS: Several key independent risk factors for sarcopenia in PAD patients were identified, namely age, body mass index (BMI), history of coronary heart disease (CHD), and white blood cell (WBC) count, as well as the severity of luminal stenosis (P < 0.05). The discriminative ability of the nomogram was supported by the C-index and an AUC of 0.810 (95% confidence interval: 0.757-0.862). A robust concordance between predicted and observed outcomes was reflected by the calibration curve. The HL test further affirmed the model's calibration with a P-value of 0.40. The DCA curve validated the nomogram's favorable clinical utility. Lastly, the model underwent internal validation. CONCLUSIONS: A simple nomogram based on five independent factors, namely age, BMI, history of CHD, WBC count, and the severity of luminal stenosis, was developed to assist clinicians in estimating sarcopenia risk among PAD patients. This tool boasts impressive predictive capabilities and broad utility, significantly aiding clinicians in identifying high-risk individuals and enhancing the prognosis of PAD patients.