A study on predicting malnutrition risk in Parkinson's disease patients using a nomogram model.

Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder that signif- icantly impacts the quality of life of affected individuals. Among the myriad of complications associated with PD, malnutrition has emerged as a critical concern, contributing to adverse clinical outcomes, including increased morbidity and mortality. Existing clinical assessments for identify- ing malnutrition, however, often lack the requisite precision and efficacy for early prediction, thus necessitating improved methodologies to address this gap. Methods: This study aimed to develop and validate a predictive nomogram model specifically de- signed for the early identification of malnutrition risk among individuals diagnosed with PD. Con- ducted between February 2022 and December 2023, this cross-sectional research enrolled a cohort of 163 patients from various inpatient and outpatient settings. Nutritional status was assessed using the Mini Nutritional Assessment (MNA) tool, while univariate and multivariate logistic regression analyses were employed to pinpoint critical risk factors contributing to malnutrition. Results: The analysis revealed several significant risk factors, including gender, body mass index (BMI), Gastrointestinal Symptom Rating Scale (GCSI) scores, Montreal Cognitive Assessment (MoCA) scores, and Barthel Index scores. The developed nomogram demonstrated an impressive area under the curve (AUC) of 0.92, with a sensitivity of 77.5% and specificity of 88%. Further- more, a cutoff risk score of 0.39 was established. Internal validation utilizing bootstrap methods yielded a concordance index (C-index) of 0.92, while calibration curves illustrated a strong align- ment between actual and predicted malnutrition risks. Conclusions: The notable prevalence of malnutrition among patients with PD accentuates the ur- gent need for effective screening tools. The validated nomogram model proposed in this study offers a promising approach for predicting malnutrition risk, ultimately aiming to enhance clin- ical outcomes within this vulnerable population. Future research may focus on integrating this nomogram into routine clinical practice to facilitate timely interventions and improve patient man- agement.