Prediction Model for Delayed Behavior of Early Ambulation After Surgery for Varicose Veins of the Lower Extremity: A Prospective Case-Control Study.

Autor:	Fu S; The School of Nursing, Fujian Medical University, China; Department of Surgery, The Second Affiliated Hospital of Xiamen Medical College, China., Chen L; Department of Nursing, The Second Affiliated Hospital of Xiamen Medical College, China., Lin H; Department of Nursing, The Second Affiliated Hospital of Xiamen Medical College, China., Jiang X; Intensive Care Unit, The Second Affiliated Hospital of Xiamen Medical College, China., Zhang S; Department of General surgery, Zhongshan Hospital Xiamen University, China., Zhong F; Department of Surgery, Fujian Medical University Union Hospital, China., Liu D; The School of Nursing, Fujian Medical University, China. Electronic address: liudun2005@163.com.
Jazyk:	angličtina
Zdroj:	Archives of physical medicine and rehabilitation [Arch Phys Med Rehabil] 2024 Oct; Vol. 105 (10), pp. 1908-1920. Date of Electronic Publication: 2024 Jun 22.
DOI:	10.1016/j.apmr.2024.06.004
Abstrakt:	Objective: To analyze influencing factors and establish a prediction model for delayed behavior of early ambulation after surgery for varicose veins of the lower extremity (VVLE). Design: A prospective case-control study. Setting: Patients with VVLE were recruited from 2 local hospitals. Participants: In total, 498 patients with VVLE were selected using convenience sampling and divided into a training set and a test set. Interventions: Not applicable. Main Outcome Measures: We collected information from the selected participants before surgery and followed up until the day after surgery, then divided them into a normal and delayed ambulation group. Propensity score matching was applied to all participants by type of surgery and anesthesia. All the characteristics in the 2 groups were compared using logistic regression, back propagation neural network (BPNN), and decision tree models. The accuracy, sensitivity, specificity, and area under the curve (AUC) values of the 3 models were compared to determine the optimal model. Results: A total of 406 participants were included after propensity score matching. The AUC values for the training sets of logistic regression, BPNN, and decision tree models were 0.850, 0.932, and 0.757, respectively. The AUC values for the test sets were 0.928, 0.984, and 0.776, respectively. A BPNN was the optimal model. Social Support Rating Scale score, preoperative 30-second sit-stand test score, Clinical-Etiology-Anatomy-Pathophysiology (CEAP) grade, Medical Coping Modes Questionnaire score, and whether you know the need for early ambulation, in descending order of the result of a BPNN model. A probability value greater than 0.56 indicated delayed behavior of early ambulation. Conclusions: Clinicians should pay more attention to those with lower Social Support Rating Scale scores, poor lower limb strength, a higher CEAP grade, and poor medical coping ability, and make patients aware of the necessity and importance of early ambulation, thereby assisting decision-making regarding postoperative rehabilitation. Further research is needed to improve the method, add more variables, and transform the model into a scale to screen and intervene in the delayed behavior of early ambulation of VVLE in advance. (Copyright © 2024 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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