Construction and evaluation of a 180-day readmission prediction model for chronic heart failure patients based on sCD40L

Background The high readmission rate of patients with chronic heart failure can cause waste of medical resources and economic losses. Establishing an effective heart failure readmission model can effectively alleviate medical pressure and improve the quality of treatment. Methods In this study, we conducted a comprehensive analysis of clinical and laboratory data from 248 patients with chronic heart failure who received treatment at our medical center between January 2021 to January 2023. We also measured sCD40L levels to determine their association with readmission due to heart failure during follow-up. To analyze the data, we employed various statistical methods including independent sample t-test, correlation analysis, univariate COX regression, and LASSO COX regression. Using these techniques, we organized the data and constructed a predictive model that was both trained and validated. Results We developed a nomogram to assess the likelihood of readmission within 180 days for patients with chronic heart failure. Our findings revealed that monocytes, creatinine, sCD40L, and hypertension history were all independent risk factors for 180-day heart failure readmissions. Additionally, our model's area under the curve was 0.731 in the training dataset and 0.704 in the validation dataset. Conclusion This study provides new insights for predicting readmission within 180 days for patients with chronic heart failure. And sCD40L is an important predictive indicator for readmission of heart failure patients within 180 days, and clinical doctors can develop appropriate treatment plans based on sCD40L.