Zhang Tinglin, Sun Feiyang, Ma Xiaodie, Liu Yaoyao, Li Fangyan, Zhang Lei
Nursing College, Jinzhou Medical University, Jinzhou, China.
Department of Nursing, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Sci Rep. 2025 Jan 21;15(1):2637. doi: 10.1038/s41598-025-86835-w.
Alexithymia, characterized by difficulty in expressing and recognizing emotions, is prevalent among young and middle-aged stroke survivors and can significantly impact rehabilitation outcomes. This study aims to develop and validate a dynamic nomogram to predict the risk of alexithymia in this population. This cross-sectional study was conducted from November 2022 to August 2023 at two tertiary hospitals in Jinzhou City and Cangzhou City, enrolling 319 patients. Predictive factors for alexithymia, such as Activities of Daily Living (ADL) scores, social support levels, lesion location, educational background, and National Institutes of Health Stroke Scale (NIHSS) scores, were identified through univariate and multivariate analyses. These factors were integrated into a web-based dynamic nomogram. The model's accuracy was evaluated using Receiver Operating Characteristic (ROC) curves and 1000 bootstrap resamples. In the training cohort, 47.8% of patients were diagnosed with alexithymia. The nomogram demonstrated excellent fit and reliability, with an Area Under the Curve (AUC) of 0.837 (95% CI: 0.787-0.889) in the training cohort and 0.847 (95% CI: 0.767-0.928) in the validation cohort, enabling reliable early detection of alexithymia. The dynamic nomogram provides healthcare professionals with an important tool for early detection and management of alexithymia in young and middle-aged stroke survivors. While the model shows high predictive accuracy, its applicability may be limited to similar clinical settings. Future studies should evaluate its utility across diverse healthcare systems. This tool has the potential to significantly improve rehabilitation outcomes by supporting personalized therapeutic strategies and interventions.
述情障碍的特征是在表达和识别情感方面存在困难，在中青年中风幸存者中很普遍，并且会显著影响康复结果。本研究旨在开发并验证一个动态列线图，以预测该人群中述情障碍的风险。这项横断面研究于2022年11月至2023年8月在锦州市和沧州市的两家三级医院进行，共纳入319例患者。通过单因素和多因素分析确定了述情障碍的预测因素，如日常生活活动（ADL）评分、社会支持水平、病变部位、教育背景和美国国立卫生研究院卒中量表（NIHSS）评分。这些因素被整合到一个基于网络的动态列线图中。使用受试者工作特征（ROC）曲线和1000次自助重抽样评估模型的准确性。在训练队列中，47.8%的患者被诊断为述情障碍。该列线图显示出良好的拟合度和可靠性，训练队列中的曲线下面积（AUC）为0.837（95%CI：0.787-0.889），验证队列中的AUC为0.847（95%CI：0.767-0.928），能够可靠地早期检测述情障碍。该动态列线图为医护人员提供了一个重要工具，用于早期检测和管理中青年中风幸存者中的述情障碍。虽然该模型显示出较高的预测准确性，但其适用性可能仅限于类似的临床环境。未来的研究应评估其在不同医疗系统中的效用。该工具有可能通过支持个性化治疗策略和干预措施显著改善康复结果。
