Development and validation of a risk prediction model for orthostatic intolerance in patients undergoing initial ambulation following minimally invasive lung surgery
10.3760/cma.j.cn115682-20240108-00145
- VernacularTitle:肺部微创术后患者首次下床活动发生直立性不耐受的风险预测模型构建及验证
- Author:
Jing MA
1
;
Yuanhang ZHANG
;
Xue GAO
;
Liyun BAO
;
Sijia WANG
;
Xintong TIAN
;
Baohua LI
Author Information
1. 北京大学第三医院胸外科，北京 100191
- Publication Type:Journal Article
- Keywords:
Microtraumatic operation;
Lung;
Initial ambulation;
Orthostatic intolerance;
Prediction model
- From:
Chinese Journal of Modern Nursing
2024;30(35):4842-4848
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify factors influencing orthostatic intolerance (OI) in patients during initial ambulation following minimally invasive lung surgery and develop and validate a risk prediction model to assist clinical practitioners in screening high-risk patients.Methods:Totally 1 000 patients who underwent minimally invasive lung surgery at the Department of Thoracic Surgery of Peking University Third Hospital from March 2022 to November 2023 were recruited by convenience sampling. Patients were randomly divided into a modeling group ( n=800) and an internal validation group ( n=200) in an 8∶2 ratio. Univariate analysis and logistic regression were applied to determine risk factors for OI in the modeling group. R software was utilized to construct a nomogram model. The model's predictive performance was assessed using the area under the ROC curve ( AUC) for both the modeling and validation groups. Calibration curves were plotted to evaluate consistency, and the Hosmer-Lemeshow test was conducted to confirm model fit. Results:The incidence of OI during initial ambulation was 37.2% (372/1 000). Logistic regression identified BMI, postoperative day 1 drainage volume, postoperative use of nonsteroidal anti-inflammatory drugs (NSAIDs), and initial ambulation pain score as independent risk factors for OI ( P<0.05). The AUC for the nomogram model in the modeling group was 0.645, and 0.694 in the validation group, indicating good predictive accuracy. Calibration curves showed strong agreement between predicted and observed outcomes ( P>0.05) . Conclusions:The constructed risk prediction model demonstrates good predictive ability for OI risk during initial ambulation following minimally invasive lung surgery, which can support clinical identification of high-risk patients. This tool may provide valuable guidance for implementing early, targeted preventive measures.
