Abstract: Objective To analyze the influencing factors for symptom cluster distress in maintenance hemodialysis (MHD) patients and to construct a decision tree risk prediction model.  Methods A total of 398 MHD patients admitted to the First Affiliated Hospital of Nanjing Medical University from March 2019 to March 2024 were retrospectively reviewed as the study subjects. Their clinical data were recruited. They were then categorized into distress and non-distress groups according to the presence or absence of symptom cluster distress. The risk factors affecting the symptom cluster distress in MHD patients were analyzed using unifactorial and multifactorial logistic regressions. The risk prediction models were constructed using the decision tree model and logistic regression algorithm, and the predictive value of the 2 models for the occurrence of symptom cluster distress in MHD was compared.  Results A total of 398 patients were included, including 106 cases in distress group and 292 cases in non-distress group. Multifactorial logistic analysis showed that monthly income (OR=2.072, 95% CI:1.019～4.211, P=0.044), dialysis time (OR=2.046, 95% CI:1.055～3.970, P=0.034), anxiety and depression (OR=1.990, 95% CI:1.002～3.949, P=0.049), parathyroid hormone (OR=1.241, 95% CI:1.178～1.308, P＜0.001) and blood phosphorus (OR=18.581, 95% CI:5.963～57.897, P＜0.001) were the risk factors for the presence of symptom cluster distress in MHD patients, and hemoglobin (OR=0.947, 95% CI:0.925～0.969, P＜0.001) was a protective factor for the presence of symptom cluster distress in MHD patients. The constructed decision tree model contained 5 variables that divided into 4 levels with 24 nodes, and parathyroid hormone was the main influencing factor. The AUCs of logistic regression model and decision tree model were 0.898 and 0.923, respectively, and the delong test for comparison of the two models was Z=2.240 and P=0.025.  Conclusion   Monthly income, dialysis time, anxiety and depression, parathyroid hormone and blood phosphorus are the risk factors for symptom cluster distress in MHD patients, and hemoglobin is a protective factor. The predictive efficacy of the constructed decision tree risk prediction model was superior to that of the logistic regression model. 

Key words: Maintenance hemodialysis, Symptom cluster, Influencing factor, Decision tree, Risk prediction model