Analysis of influencing factors and predictive model construction of discharge readiness of patients after radical resection of esophageal cancer under enhanced recovery after surgery mode
10.3760/cma.j.cn115682-20200513-03321
- VernacularTitle:加速康复外科模式下食管癌根治术后患者出院准备度影响因素分析及预测模型构建
- Author:
Funa YANG
1
;
Lijuan LI
;
Limin ZOU
;
Xiaoxia XU
Author Information
1. 郑州大学附属肿瘤医院护理部，郑州 450008
- Keywords:
Esophageal cancer;
Enhanced recovery after surgery;
Discharge readiness;
Prediction model
- From:
Chinese Journal of Modern Nursing
2020;26(33):4591-4597
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the influencing factors of discharge readiness of patients after radical resection of esophageal cancer under enhanced recovery after surgery (ERAS) model and to explore the prediction model.Methods:Using the convenient sampling method, a total of 195 patients with esophageal cancer who were admitted to the department of thoracic surgery in a hospital in Henan Province from January 2019 to April 2020 were selected as the research objects. At discharge, the general and disease-related data collection form, Readiness for Hospital Discharge Scale, M.D.Anderson Symptom Inventory Gastrointestinal (MDAS-GI) , Frail Scale, Time Up and Go Test (TUGT) and Water Swallow Test were used to investigate. Multivariate unconditional Logistic regression analysis was used to analyze influencing factors.A total of 195 questionnaires were distributed in this study, and 189 valid questionnaires were collected.Results:Plasma albumin level, 24-hour food intake, MDASI-GI and TUGT were independent predictors of the readiness for discharge of patients after radical resection of esophageal cancer ( P<0.05) . Finally, the prediction model of postoperative discharge readiness for patients was Y=-3× X1+1× X2+5× X3-4× X4-1× X5 ( Y=discharge readiness; X1=pain score, X2=plasma albumin, X3=24 h food intake, X4=MDASI, X5=TUGT) . Conclusions:The readiness for discharge of patients with esophageal cancer under the ERAS mode is affected by many factors, and the prediction model of discharge assessment is effective, which can provide reference for clinical discharge decision-making.
