Abstract: Objective To construct a prognostic model of chronic heart failure (CHF) by traditional Chinese medicine (TCM) syndrome elements by machine learning method. Methods Patients with CHF admitted to the Department of Cardiology of China-Japan Friendship Hospital from January 1, 2018 to April 30, 2021 were included, and their demographic data, vital signs, co-morbidities, laboratory tests, echo-cardiographic indicators, TCM syndrome elements and treatment information were collected. The primary end point for this analysis was a model to predict cardiovascular death or hospitalization because of heart failure in one year follow-up. Least absolute shrinkage, selection operator(LASSO) regression and Cox multivariate analysis were used to screen independent risk factors that potentially affect the prognosis of CHF. A nomogram was used to establish a risk prediction model based on TCM syndrome elements. Results Totally 164 patients with an average age of (72.23±14.16) years old and 37.2% male were included in this study. The LASSO screened 9 factors from clinical variables, including coronary heart disease, hypertension, uric acid, N-terminal pro-B type natriuretic peptide (NT-proBNP), left ventricular ejection fraction (LVEF), creatine kinase-myocardial band, myoglobin, Qi deficiency and Yin deficiency. Cox multivariate regression analysis showed that Qi deficiency, hypertension, coronary heart disease, NT-proBNP and LVEF were associated with prognosis in patients with CHF. Conclusions Qi deficiency was an independent predictor of cardiovascular death or heart failure readmission in CHF patients within 1 year. The prognostic model of CHF with integrated Chinese and Western medicine has demonstrated a high accuracy.

Key words: chronic heart failure, syndrome elements, integrated Chinese and Western medicines, machine learning, prognostic model