Development and validation of a risk prediction model for hospital discharge readiness in patients undergoing surgery for Stanford type B aortic dissection
10.3760/cma.j.cn115682-20250303-01010
- VernacularTitle:Stanford B型主动脉夹层术后患者出院准备度风险预测模型的构建与验证
- Author:
Hui WANG
1
;
Ying XU
;
Xiaoling HUANG
;
Xiaofei WANG
Author Information
1. 浙江省台州医院血管外科，台州 317000
- Publication Type:Journal Article
- Keywords:
Stanford type B aortic dissection;
Readiness for hospital discharge;
Nomogram;
Risk;
Prediction model;
Logistic regression
- From:
Chinese Journal of Modern Nursing
2025;31(32):4452-4458
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and validate a risk prediction model for hospital discharge readiness in patients undergoing surgery for Stanford type B aortic dissection (TBAD) .Methods:A total of 130 patients who underwent TBAD surgery at Taizhou Hospital of Zhejiang Province between January 2020 and September 2024 were recruited by convenience sampling and divided into a training set ( n=91) and a validation set ( n=39) at a 7∶3 ratio. Readiness for hospital discharge was assessed using the Chinese version of the Readiness for Hospital Discharge Scale, and patients were categorized into good and poor readiness groups. Univariate analysis was used to compare demographic and disease-related data as well as admission Aortic Dissection Detection Risk Score (ADD-RS) between groups. Logistic regression was employed to identify factors influencing discharge readiness. A nomogram was developed using R software, and its predictive performance was evaluated with receiver operating characteristic (ROC) curves. Calibration curves and decision curve analysis (DCA) were further used to assess the accuracy and clinical utility of the model. Results:Logistic regression identified feedback-based health education, number of hospitalizations, ADD-RS score, emergency surgery, and length of hospital stay as independent predictors of discharge readiness. ROC curve analysis showed that the area under the curve ( AUC) for predicting poor discharge readiness was 0.91 [95% CI (0.85, 0.97) ] in the training set and 0.84 [95% CI (0.80, 0.99) ] in the validation set. Calibration curves and the Hosmer-Lemeshow test confirmed good calibration in both sets ( P>0.05). DCA demonstrated significant net clinical benefit when the high-risk threshold exceeded 0.02, although the benefit decreased as the threshold approached 0.80. Conclusions:The risk prediction model developed in this study effectively predicts poor discharge readiness in patients after TBAD surgery, showing good discrimination and calibration. The identified risk factors provide targeted directions for clinical interventions, which may help improve patients' readiness for discharge.
