Influencing factors of 90-day readmission of patients with heart failure based on the MIMIC-IV database

At this stage, the prognostic analysis of heart failure (HF) patients mainly focused on death and 30-day readmission, and fewer studies focused on 90-day readmission. Therefore, this study was to explore the risk factors for 90-day readmission in HF patients. Least absolute shrinkage and selection operator (LASSO) regression was used to perform variable screening. Multivariate logistic regression was used to analyze the influencing factors of 90-day readmission of patients with HF. Logistic classification, eXtreme gradient boosting (XGBoost) regression, and random forest were used to rank the influencing factors, and the Wayne diagram extracted the common factors of the top five influencing factors obtained by the three methods. Trend regression analysis and restricted cubic spline analysis were used to analyze the correlation between these factors and the 90-day readmission of patients with HF. Charlson Comorbidity Index (CCI), red blood cell count (RBC), red cell distribution width (RDW), and age were the main factors influencing the 90-day readmission of patients with HF. With the increase of CCI, the risk of 90-day readmission for patients with HF increased, the same results were found in RDW and age, while with the increase of RBC value, the risk of 90-day readmission decreased. RBC and RDW were linearly correlated with the 90-day readmission of patients with HF, while CCI and age were nonlinearly correlated with the 90-day readmission of patients with HF. CCI, RBC, RDW, and age were the main factors influencing the 90-day readmission of patients with HF. RBC and RDW were linearly correlated with the 90-day readmission of patients with HF, while CCI and age were nonlinearly correlated with the 90-day readmission of patients with HF.