Many CABG are performed annually around the world. In 2015, CABG was included in the US Hospital Readmission Reduction Program, since then, many scholars have studied the risk factors associated with postoperative readmission in patients after CABG, such as obstructive pulmonary disease, anemia, cirrhosis, and infection at the surgical site[1, 16]. There are also studies showed that readmission after cardiac surgery are related to the additional cost of surgery[17–18]. In 2017, Aleksander and other scholars used American patient data to establish a RAC risk assessment scale, which included 6 items: age, gender, race, insurance, type of admission, and complications. This scale showed that patients over 80 years of age have a higher risk of readmission, however, in China, only a few readmission patients are older than 80, and the high risk of readmission is mainly for patients over 65 years old. Furthermore, the scale said that African American are at higher risk than white and Hispanic, obviously this item is not applicable in China. The model built by Benuzillo et al. used the data available soon after admission for isolated CABG surgery as the research cohort, and the scholars such as Derrick Y constructed the Clinical Risk Scoring Tool based on Canadian population data, but the C-statistic of both models are less than 0.7[9–10]. Some Chinese scholars have studied the application value of foreign risk prediction models in the Chinese population, due to various factors such as ethnicity and national conditions, the application effect was poor.
The prediction model constructed in this study includes 6 indicators: gender (female), age ≥ 65 years, insurance type ༈private༉, diabetes, hypertension ༈level 2,3༉and congenital heart disease. It can be noted that these 6 variables are all preoperative variables, which implied that patient's own physical condition, lifestyle and economic status may determine to a greater extent whether the patient will be admitted to the hospital again. Moreover, basic diseases, poor diet and exercise habits, lack of health awareness and knowledge would not only lead to the occurrence of diseases, but also affect the prognosis of patients. Research by Bates and Lin showed that health education and interventions can reduce the rate of postoperative readmission and improve the quality of life in CABG patients[19–20]. Therefore, it is necessary to educate patients on health and intervene according to their respective conditions. The CABG readmission risk prediction model can help medical staff identify the high-risk population who will be readmitted after CABG, and provide a basis for stratified management and intervention of patients with different degrees of risk, which will help to maximize the effectiveness of treatment and reduce medical costs. The scoring system constructed in this study can quickly measure the patients’ score before the first discharge, assess the patients’ risk of readmission, and guide medical workers to complete individualized nursing and medical interventions.
Although the risk prediction model can predict the patients’ readmission risk to a certain extent, it does not mean that the prediction result can be absolutely matched with each clinical patient. This study also included inevitable limitations of the single-center retrospective research, since China has only started to develop electronic medical record systems in recent years, there is currently no database like Nationwide Readmissions Database, which makes the data we collected incomplete, so there are some predictive factors that have not been included in the study. And for a patient who has undergone surgical treatment in our hospital, if he/she went to another hospital for treatment for the second time, it means that the patient has not been readmitted. Therefore, the readmission rate in this study may be lower than the actual readmission rate. Despite the limitations, the discrimination and calibration of our risk prediction model were excellent with the available variables.