Liang Yan, Dai Xin, Wei Bing, Jia Haiyan, Zhang Jinxiu, Qiu Zi, Zhang Qian
Speech and Language Rehabilitation Department, Beijing Rehabilitation Hospital Affiliated with Capital Medical University, Beijing, China.
Br J Hosp Med (Lond). 2025 Jan 24;86(1):1-16. doi: 10.12968/hmed.2024.0366. Epub 2025 Jan 14.
The background for establishing and verifying a dehydration prediction model for elderly patients with post-stroke dysphagia (PSD) based on General Utility for Latent Process (GULP) is as follows: For elderly patients with PSD, GULP technology is utilized to build a dehydration prediction model. This aims to improve the accuracy of dehydration risk assessment and provide clinical intervention, thereby offering a scientific basis and enhancing patient prognosis. This research highlights the innovative application of GULP technology in constructing complex medical prediction models and addresses the special health needs of elderly stroke patients. Based on GULP criteria, the study aims to establish and validate a dehydration prediction model for elderly patients with dysphagia following a stroke. Two hundred patients with post-stroke dysphagia treated at Beijing Rehabilitation Hospital Affiliated with Capital Medical University, from January 2020 to December 2023, were selected retrospectively. The patients were randomly matched at a ratio of 1:4 to establish a verification group (n = 40) and a modelling group (n = 160). Based on the occurrence of dehydration, the modelling group patients were divided into two groups: the dehydration group (n = 55) and the non-dehydration group (n = 105). Univariate and multivariate logistic regression analyses were used to identify the influencing factors of dehydration in elderly patients with dysphagia after a stroke, and to establish a predictive model based on GULP. The predictive value of the model was evaluated using receiver operating characteristic (ROC) curve analysis. The results of univariate and multivariate logistic regression analyses showed significant differences in age, lesion location, muscle strength grade, homocysteine (Hcy), and swallowing function score ( < 0.05). When these influencing factors were included in the model, the slope of the calibration curve in both the training set and the validation set was close to 1, indicating that the predicted dehydration risk was consistent with the actual risk. ROC analysis results revealed that in the training set, the model predicted dehydration in elderly post-stroke patients with dysphagia with an area under the curve (AUC) of 0.934, a standard error of 0.034, and a 95% confidence interval (CI) of 0.916 to 0.981. The optimal cutoff value was 0.78, yielding a sensitivity of 88.84% and a specificity of 90.00%. In the validation set, the AUC was 0.867 with a standard error of 0.025 and a 95% CI of 0.694 to 0.934. The optimal cutoff value here was 0.66, with a sensitivity of 80.16% and a specificity of 85.94%. This study successfully established and validated a GULP-based dehydration prediction model for elderly patients with dysphagia following a stroke, demonstrating high application value.
基于潜在过程通用效用（GULP）建立和验证老年卒中后吞咽困难（PSD）患者脱水预测模型的背景如下：对于老年PSD患者，利用GULP技术构建脱水预测模型。目的是提高脱水风险评估的准确性并提供临床干预，从而提供科学依据并改善患者预后。本研究突出了GULP技术在构建复杂医学预测模型中的创新应用，并满足了老年卒中患者的特殊健康需求。基于GULP标准，该研究旨在建立并验证老年卒中后吞咽困难患者的脱水预测模型。回顾性选取了2020年1月至2023年12月在首都医科大学附属北京康复医院接受治疗的200例卒中后吞咽困难患者。患者按1:4的比例随机匹配，以建立验证组（n = 40）和建模组（n = 160）。根据脱水的发生情况，将建模组患者分为两组：脱水组（n = 55）和非脱水组（n = 105）。采用单因素和多因素逻辑回归分析来确定老年卒中后吞咽困难患者脱水的影响因素，并基于GULP建立预测模型。使用受试者工作特征（ROC）曲线分析评估模型的预测价值。单因素和多因素逻辑回归分析结果显示，年龄、病变部位、肌力分级、同型半胱氨酸（Hcy）和吞咽功能评分存在显著差异（<0.05）。当将这些影响因素纳入模型时，训练集和验证集校准曲线的斜率均接近1，表明预测的脱水风险与实际风险一致。ROC分析结果显示，在训练集中，该模型预测老年卒中后吞咽困难患者脱水的曲线下面积（AUC）为0.934，标准误为0.034，95%置信区间（CI）为0.916至0.981。最佳截断值为0.78，灵敏度为88.84%，特异度为90.00%。在验证集中，AUC为0.867，标准误为0.025，95%CI为0.694至0.934。此处最佳截断值为0.66，灵敏度为80.16%，特异度为85.94%）。本研究成功建立并验证了基于GULP的老年卒中后吞咽困难患者脱水预测模型，显示出较高的应用价值。
