ArticleClinical parkinsonism & related disorders2025
A study on predicting malnutrition risk in Parkinson's disease patients using a nomogram model.
Article in Clinical parkinsonism & related disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.