School of Nursing, Jinzhou Medical University, No. 40, Section 3, Songpo Road, Linghe District, Jinzhou City, Liaoning Province, P.R. China.
Foshan University, 18 Jiangwan 1st Road, Chancheng District, Foshan City, Guangdong Province, P.R. China.
BMC Geriatr. 2024 Oct 12;24(1):827. doi: 10.1186/s12877-024-05426-y.
This research aimed to develop and validate a dynamic nomogram for predicting the risk of high care dependency during the hospital-family transition periods in older stroke patients.
309 older stroke patients in the hospital-family transition periods who were treated in the Department of Neurology outpatient clinics of three general hospitals in Jinzhou, Liaoning Province from June to December 2023 were selected as the training set. The patients were investigated with the General Patient Information Questionnaire, the Care Dependency Scale (CDS), the Tilburg Frailty Inventory (TFI), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale-17 (HAMD-17), and the Mini Nutrition Assessment Short Form (MNA-SF). Lasso-logistic regression analysis was used to screen the risk factors for high care dependency in older stroke patients during the hospital-family transition period, and a dynamic nomogram model was constructed. The model was uploaded in the form of a web page based on Shiny apps. The Bootstrap method was employed to repeat the process 1000 times for internal validation. The model's predictive efficacy was assessed using the calibration plot, decision curve analysis curve (DCA), and area under the curve (AUC) of the receiver operator characteristic (ROC) curve. A total of 133 older stroke patients during the hospital-family transition periods who visited the outpatient department of Neurology of three general hospitals in Jinzhou from January to March 2024 were selected as the validation set for external validation of the model.
Based on the history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and frailty, build a dynamic nomogram. The AUC under the ROC curves of the training set was 0.830 (95% CI: 0.784-0.875), and that of the validation set was 0.833 (95% CI: 0.766-0.900). The calibration curve was close to the ideal curve, and DCA results confirmed that the nomogram performed well in terms of clinical applicability.
The online dynamic nomogram constructed in this study has good specificity, sensitivity, and clinical practicability, which can be applied to senior stroke patients as a prediction and assessment tool for high care dependency. It is of great significance to guide the development of early intervention strategies, optimize resource allocation, and reduce the care burden on families and society.
本研究旨在开发和验证一种适用于预测老年卒中患者在院-家过渡期高护理依赖风险的动态列线图。
本研究纳入了 2023 年 6 月至 12 月在辽宁省锦州市三家综合医院神经内科门诊治疗的 309 例处于院-家过渡期的老年卒中患者作为训练集。采用一般患者信息问卷、护理依赖量表（CDS）、蒂尔堡虚弱指数（TFI）、汉密尔顿焦虑量表（HAMA）、汉密尔顿抑郁量表-17 项（HAMD-17）和微型营养评估简表（MNA-SF）对患者进行调查。采用 Lasso 逻辑回归分析筛选老年卒中患者在院-家过渡期高护理依赖的风险因素，并构建动态列线图模型。该模型以 Shiny apps 的形式上传到网页上。采用 Bootstrap 方法重复 1000 次进行内部验证。通过校准图、决策曲线分析曲线（DCA）和受试者工作特征（ROC）曲线下面积（AUC）评估模型的预测效果。选择 2024 年 1 月至 3 月在锦州市三家综合医院神经内科门诊就诊的 133 例处于院-家过渡期的老年卒中患者作为模型的外部验证集。
基于卒中史、慢性病、过去 6 个月内跌倒、抑郁、营养不良和衰弱等因素，构建了一个动态列线图。训练集的 ROC 曲线下 AUC 为 0.830（95%CI：0.784-0.875），验证集的 AUC 为 0.833（95%CI：0.766-0.900）。校准曲线接近理想曲线，DCA 结果证实该列线图在临床适用性方面表现良好。
本研究构建的在线动态列线图具有良好的特异性、敏感性和临床实用性，可作为预测和评估老年卒中患者高护理依赖的工具。这对于指导早期干预策略的制定、优化资源配置以及减轻家庭和社会的护理负担具有重要意义。
