开发和验证预测帕金森病患者营养不良风险的nomogram：一项回顾性队列研究

IF 1.8 4区医学 Q3 CLINICAL NEUROLOGY

Journal of Clinical Neuroscience Pub Date : 2025-09-19 DOI:10.1016/j.jocn.2025.111635

Qiaomin Tang, Weiya Ma, Yuanyuan Sun, Chen Hu, Sumin Ma

{"title":"开发和验证预测帕金森病患者营养不良风险的nomogram：一项回顾性队列研究","authors":"Qiaomin Tang, Weiya Ma, Yuanyuan Sun, Chen Hu, Sumin Ma","doi":"10.1016/j.jocn.2025.111635","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to establish a nomogram to predict malnutrition risk in patients with Parkinson’s disease (PD).</div></div><div><h3>Design</h3><div>A retrospective cohort study.</div></div><div><h3>Setting</h3><div>A Grade III, Class A hospital in Zhejiang Province.</div></div><div><h3>Participants</h3><div>Patients with primary PD meeting the inclusion criteria were retrospectively identified from the electronic medical record system (January 2023–December 2024) for study inclusion.</div></div><div><h3>Methods</h3><div>This study included 21 research variables, encompassing demographic characteristics, physiological features, physical functional status, disease type, and severity. Optimal variables were selected using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. Internal validation was performed via bootstrap resampling (1,000 iterations), and a nomogram was constructed to predict the risk of malnutrition in patients with PD.</div></div><div><h3>Results</h3><div>This study included 215 patients with PD for model construction, with a malnutrition prevalence of 35.6 %. The LASSO regression and logistic regression models identified seven significant predictors of malnutrition: lower body mass index, advanced H-Y stage, decreased poorer Unified Parkinson’s Disease Rating Scale Part III Maximum Improvement Rate, decreased red blood cell count, reduced total cholesterol, elevated blood urea nitrogen, and dysphagia (<em>P</em> < 0.05). The model achieved an area under the curve of 0.814 (95 % CI: 0.754–0.874), with 70.1 % sensitivity, 76.1 % specificity, and a Youden’s index of 0.462, indicating robust predictive performance.</div></div><div><h3>Conclusion</h3><div>The prediction model constructed based on Mini Nutritional Assessment scores demonstrated strong predictive performance and holds significant clinical importance for identifying malnutrition in patients with PD.</div></div>","PeriodicalId":15487,"journal":{"name":"Journal of Clinical Neuroscience","volume":"142 ","pages":"Article 111635"},"PeriodicalIF":1.8000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a nomogram for predicting malnutrition risk among patients with Parkinson’s disease: A retrospective cohort study\",\"authors\":\"Qiaomin Tang, Weiya Ma, Yuanyuan Sun, Chen Hu, Sumin Ma\",\"doi\":\"10.1016/j.jocn.2025.111635\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><div>This study aimed to establish a nomogram to predict malnutrition risk in patients with Parkinson’s disease (PD).</div></div><div><h3>Design</h3><div>A retrospective cohort study.</div></div><div><h3>Setting</h3><div>A Grade III, Class A hospital in Zhejiang Province.</div></div><div><h3>Participants</h3><div>Patients with primary PD meeting the inclusion criteria were retrospectively identified from the electronic medical record system (January 2023–December 2024) for study inclusion.</div></div><div><h3>Methods</h3><div>This study included 21 research variables, encompassing demographic characteristics, physiological features, physical functional status, disease type, and severity. Optimal variables were selected using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. Internal validation was performed via bootstrap resampling (1,000 iterations), and a nomogram was constructed to predict the risk of malnutrition in patients with PD.</div></div><div><h3>Results</h3><div>This study included 215 patients with PD for model construction, with a malnutrition prevalence of 35.6 %. The LASSO regression and logistic regression models identified seven significant predictors of malnutrition: lower body mass index, advanced H-Y stage, decreased poorer Unified Parkinson’s Disease Rating Scale Part III Maximum Improvement Rate, decreased red blood cell count, reduced total cholesterol, elevated blood urea nitrogen, and dysphagia (<em>P</em> < 0.05). The model achieved an area under the curve of 0.814 (95 % CI: 0.754–0.874), with 70.1 % sensitivity, 76.1 % specificity, and a Youden’s index of 0.462, indicating robust predictive performance.</div></div><div><h3>Conclusion</h3><div>The prediction model constructed based on Mini Nutritional Assessment scores demonstrated strong predictive performance and holds significant clinical importance for identifying malnutrition in patients with PD.</div></div>\",\"PeriodicalId\":15487,\"journal\":{\"name\":\"Journal of Clinical Neuroscience\",\"volume\":\"142 \",\"pages\":\"Article 111635\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967586825006083\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967586825006083","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}

引用次数: 0

摘要

目的建立一种预测帕金森病（PD）患者营养不良风险的nomogram方法。设计：回顾性队列研究。单位：浙江省三级甲等医院。符合纳入标准的原发性PD患者回顾性地从电子病历系统（2023年1月- 2024年12月）中确定纳入研究。方法纳入21个研究变量，包括人口统计学特征、生理特征、身体功能状态、疾病类型和严重程度。使用最小绝对收缩和选择算子（LASSO）回归和多变量逻辑回归分析选择最优变量。通过自举重采样（1000次迭代）进行内部验证，并构建nomogram来预测PD患者营养不良的风险。结果本研究纳入215例PD患者进行模型构建，其中营养不良患病率为35.6%。LASSO回归和logistic回归模型确定了7个显著的营养不良预测因子：较低的体重指数、较晚期的H-Y分期、较差的统一帕金森病评定量表第三部分最大改善率降低、红细胞计数降低、总胆固醇降低、血尿素氮升高和吞咽困难（P < 0.05）。该模型的曲线下面积为0.814 (95% CI: 0.754-0.874)，敏感性为70.1%，特异性为76.1%，约登指数为0.462，表明预测效果良好。结论基于Mini nutrition Assessment评分构建的预测模型具有较强的预测能力，对识别PD患者的营养不良具有重要的临床意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Development and validation of a nomogram for predicting malnutrition risk among patients with Parkinson’s disease: A retrospective cohort study

Objectives

This study aimed to establish a nomogram to predict malnutrition risk in patients with Parkinson’s disease (PD).

Design

A retrospective cohort study.

Setting

A Grade III, Class A hospital in Zhejiang Province.

Participants

Patients with primary PD meeting the inclusion criteria were retrospectively identified from the electronic medical record system (January 2023–December 2024) for study inclusion.

Methods

This study included 21 research variables, encompassing demographic characteristics, physiological features, physical functional status, disease type, and severity. Optimal variables were selected using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. Internal validation was performed via bootstrap resampling (1,000 iterations), and a nomogram was constructed to predict the risk of malnutrition in patients with PD.

Results

This study included 215 patients with PD for model construction, with a malnutrition prevalence of 35.6 %. The LASSO regression and logistic regression models identified seven significant predictors of malnutrition: lower body mass index, advanced H-Y stage, decreased poorer Unified Parkinson’s Disease Rating Scale Part III Maximum Improvement Rate, decreased red blood cell count, reduced total cholesterol, elevated blood urea nitrogen, and dysphagia (P < 0.05). The model achieved an area under the curve of 0.814 (95 % CI: 0.754–0.874), with 70.1 % sensitivity, 76.1 % specificity, and a Youden’s index of 0.462, indicating robust predictive performance.

Conclusion

The prediction model constructed based on Mini Nutritional Assessment scores demonstrated strong predictive performance and holds significant clinical importance for identifying malnutrition in patients with PD.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Clinical Neuroscience 医学-临床神经学

CiteScore

4.50

自引率

0.00%

发文量

402

审稿时长

40 days

期刊介绍： This International journal, Journal of Clinical Neuroscience, publishes articles on clinical neurosurgery and neurology and the related neurosciences such as neuro-pathology, neuro-radiology, neuro-ophthalmology and neuro-physiology. The journal has a broad International perspective, and emphasises the advances occurring in Asia, the Pacific Rim region, Europe and North America. The Journal acts as a focus for publication of major clinical and laboratory research, as well as publishing solicited manuscripts on specific subjects from experts, case reports and other information of interest to clinicians working in the clinical neurosciences.