Developing a Nomogram-Based Prediction Model for Malnutrition Risk in Preoperative Elderly Patients with Hip Fracture

J Multidiscip Healthc. 2024 Dec 30:17:6177-6186. doi: 10.2147/JMDH.S487495. eCollection 2024.

Abstract

Objective: To evaluate the risk factors contributing to preoperative malnutrition in elderly patients with hip fractures.

Methods: The study retrospectively analysed clinical data from 182 elderly patients aged 60 years or older with hip fractures. Nutritional status was assessed according to the Global Leadership Initiative on Malnutrition diagnostic criteria, and risk factors associated with malnutrition were identified through univariate and logistic regression analyses. Based on the findings, a nomogram was developed, and a calibration curve model was constructed. The predictive performance of the model was evaluated using the receiver operating characteristic (ROC) curve. Finally, the model was validated using an independent cohort of 78 patients.

Results: Data analysis revealed that among the 182 elderly patients with hip fractures, 76 were men and 106 were women, with a mean age of 75.77 ± 8.66 years. The fractures included 135 femoral neck fractures and 47 intertrochanteric fractures. Malnutrition was identified in 39.01% (71/182) of the patients. Independent risk factors for malnutrition included age, body mass index, the number of comorbidities, haemoglobin level and serum albumin level. A nomogram model incorporating these indicators was developed, demonstrating robust predictive performance, with an area under the ROC curve of 0.886 (95% confidence interval: 0.809-0.962).

Conclusion: It is anticipated that the proposed model will serve as a valuable tool for the timely and accurate clinical identification of malnutrition risk in elderly patients with hip fractures.

Keywords: hip fracture; malnutrition; older adults; prediction model.

Grants and funding

This project is funded under the Medical Health Science and Technology Project of Zhejiang Province (2022KY1118), the Traditional Chinese Medicine Technology Project of Zhejiang Province (2023ZL154), and the Science and Technology Planning Project of Ningbo CIty (2023S092).