Development and analysis of clinical prediction model of acupuncture and moxibustion treatment for stroke at recovery stage

Abstract

To develop the clinical prediction model of therapeutic effect in treatment with acupuncture and moxibustion for the patients with stroke at recovery stage under different conditions so as to provide a tool for predicting the therapeutic effect of acupuncture and moxibustion. A total of 1410 patients with stroke at recovery stage were collected from the Third Affiliated Hospital of Zhejiang Chinese Medical University from 2012 to 2019. The relevant data were extracted, i.e. sex, age, time of onset, neurological functional deficit score (NFDS) and acupuncture and moxibustion therapy. The difference of NFDS before and after treatment was adopted to evaluate the therapeutic effect in the patients. Using SPSS26.0 software and CART decision tree analysis, the clinical prediction model was developed. The key variables in the prediction model of therapeutic effect in the patients with stroke at recovery stage under different conditions included age, time of onset, hypertension, cardiac disease, diabetes, TCM diagnosis, hemoglobin (HB), serum homocysteine (HCY) and acupuncture and moxibustion therapy. There were 12 main rules generated by the decision tree model, including 8 rules for predicting the improvements of therapeutic effect and 4 rules for predicting the absence of improvements (i.e. no change and deterioration). The accuracy rates of the model training set and test set were 80.0% and 72.8% respectively, the area under curve (AUC) of ROC was 0.797 and the model identification and classification results were satisfactory. The clinical prediction model developed by CART decision tree analysis is high in accuracy for the prediction of the therapeutic effect in the patients with stroke at recovery stage under different conditions. Based on the therapeutic effect predicted in the hospital visit, the physicians may adopt the corresponding regimens of acupuncture and moxibustion therapy in patients.