关键词: 胃肠肿瘤, 疲劳, Logistic模型, 神经网络, 决策树

Abstract: Background　The incidence of postoperative fatigue（POF） after gastrointestinal tumor surgery is high，which brings negative effects on patients' prognosis and quality of life.However，available studies about factors associated with POF are limited to physiological and psychological aspects，and predicting POF preoperatively and screening POF in high-risk patients have not been achieved.Objective　To establish a risk screening scale based on predictive model for POF after gastrointestinal tumor surgery，to provide a basis for early identification and delivering interventions for high-risk patients.Methods　360 patients who underwent gastrointestinal tumor surgery in the First Affiliated Hospital with Nanjing Medical University from January to June 2018 were included for prospective analysis.Physiological，psychological，and sociological data were collected before surgery，and POF was measured after surgery.The independent risk factors of POF were screened by univariate and multivariate analyses，and were substituted into the binary logistic regression model，BP neural network model and decision tree model，respectively.The AUC and generalization ability of the model tester were compared to select the optimal model.On this basis，POF Risk Screening Scale was formed，then its reliability and validity were analyzed and the cut-off value was determined.Finally，105 patients with gastrointestinal tumor surgery fro the same hospital were included to test the scale.Results　The AUC for the binary logistic regression model，BP neural network model and decision tree model was 0.857，0.894，0.774，respectively，indicating that all of them had a good generalization ability，but BP neural network model was the optimal，with tumor stage，education level，personal monthly income，subjective support，age，preoperative anxiety and depression，and preoperative albumin included.The risk screening scale scored 0 to 15 points.The CVI for the scale was 0.90，and ranged from 0.80 to 1.00 for its items.Exploratory factor analysis indicated that the scale consisted of 3 factors which explained 66.04% of the total variance，and factor loading of all items ranged from 0.552 to 0.751.The Cronbach's α coefficients for the scale，and its 3 factors were 0.730，0.839，0.763，and 0.637，respectively.The AUC of the scale in predicting POF was 0.839，and the cut-off value was 8.In the stage of verification，the scale showed an accuracy of 90.49% in predicting POF.Conclusion　The POF Risk Screening Scale for Gastrointestinal Tumor Patients based on BP neural network model has good reliability and validity，which can be used to effectively predict POF，providing guidance for POF risk screening and delivery of targeted interventions.

Key words: Gastrointestinal neoplasms, Fatigue, Logistic models, Nerve net, Decision trees