A nomogram for predicting the readmission within 6 months after treatment in patients with acute coronary syndrome

PURPOSE: To explore predictors for readmission within 6 months of ACS patients, and to build a prediction model, and generate a nomogram. METHODS: The retrospective cohort study included 498 patients with ACS in the Second Medical Center of the Chinese People's Liberation Army General Hospital between January 2016 and March 2019. Univariate and multivariate logistic regression with odds ratios (OR) and two-sided 95% confidence interval (CI) analysis were used to investigate predictors for readmission within 6 months. The cohort was randomly divided into training cohort to develop a prediction model, and the validation cohort to validate the model. The receiver operating characteristic curve (ROC) and the calibration curve was used to assess discriminative power and calibration. RESULTS: Eighty-three ACS patients were readmitted within six months, with a readmission rate of 16.67%. Predictors included ACS type, treatment, hypertension, SUA, length of stay, statins, and adverse events occurred during hospitalization were used to form a six-month readmission prediction model for readmission within 6 months in ACS patients. The area under the curve (AUC) of the model was 0.788 (95%CI: 0.735-0.878) and 0.775 (95%CI: 0.686-0.865) in the training cohort and the validation cohort, respectively. Calibration curves showed the good calibration of the prediction model. Decision-curve analyses and clinical impact curve also demonstrated that it was clinically valuable. CONCLUSION: We used seven readily available predictors to develop a prediction model for readmission within six months after treatment in ACS patients, which could be used to identify high-risk patients for ACS readmission.