The Charlson Comorbidity Index (CCI) was initially proposed by Charlson et al. [5]. in 1987 and revised in 1992. It scores various chronic diseases and uses the total score to predict long-term mortality. ACCI is a variant of CCI, incorporating age as a risk factor on top of the original comorbidity index, making it more suitable for risk assessment in elderly patients. Similarly, the Elixhauser Comorbidity Index (ECI) predicts in-hospital mortality based on 30 acute and chronic comorbidities [6]. ECI-vw is a modified index based on ECI. In 2009, Van Walraven et al. [7] transformed the ECI scoring into a weighted scoring system to simplify its use.

Compared to other scoring methods such as the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Sequential Organ Failure Assessment (SOFA), ACCI and ECI-vw can be calculated at the time of patient admission without the need for interpretation of laboratory and bedside clinical data. Therefore, they can be easily extracted from management databases, and their use has been increasing in critical care-related literature. Both CCI and ECI-vw have been widely used to predict survival rates of patients in ICUs [8]. Multiple studies [9,10,11,12,13,14] have demonstrated the effectiveness of these indices in predicting in-hospital mortality among ICU patients. Recent evidence suggests that ECI-vw may slightly outperform CCI in predicting mortality among hospitalized patients [15,16,17,18,19,20] but is inferior to ACCI [21].

Although ACCI and ECI-vw have been proven to have predictive value in other types of surgeries, their relevance and predictive capability in the prognosis of patients after cardiac valve surgery have not been fully explored. Moreover, patients with heart valve disease often have multiple comorbidities, such as hypertension, diabetes, and chronic kidney disease, which could affect surgical outcomes and long-term prognosis. Therefore, accurately assessing postoperative in-hospital all-cause mortality risk using ACCI and ECI-vw scores is crucial for improving patient management and enhancing surgical safety.

Our results indicate that both ACCI and ECI-vw could predict the risk of postoperative in-hospital all-cause mortality in patients undergoing cardiac valve surgery. Notably, ACCI shows good predictive performance for all-cause mortality within 28 days post-surgery. The application of these two scoring tools provides important reference information for clinicians in perioperative management. Especially today, with the advent of an aging society and the increasing average age of patients with heart valve disease, along with more complex comorbidity conditions, more precise assessment tools are needed to guide clinical decision-making. The advantage of ACCI lies in its integration of patients’ age with comorbid conditions, offering a more comprehensive perspective for assessing postoperative risks in elderly patients with heart valve disease. In this study, patients undergoing heart valve surgery with an ACCI score greater than 3.5 exhibited a higher risk of mortality within 28 days postoperatively compared to those with an ACCI score less than 3.5, further confirming its applicability in the postoperative prognosis assessment of patients with heart valve disease.

ACCI, as a comprehensive assessment tool that combines the traditional CCI with age factors, is more suitable for assessing the risk in elderly patients. This was validated in our study, where patient groups with higher ACCI scores showed significantly increased risk of postoperative in-hospital all-cause mortality. This may be because elderly patients often have multiple chronic diseases, such as hypertension, diabetes, and chronic kidney disease, which not only affect their overall health condition but may also increase the risk of surgical complications and postoperative complications. Therefore, ACCI holds significant value in assessing risk in elderly patients with heart valve disease.

ECI-vw predicts perioperative risk by assessing the overall health condition of patients. The advantage of ECI-vw lies in its comprehensive consideration of the impact of more chronic diseases and complications on patients, allowing for a more accurate assessment of postoperative risk. Additionally, the universality of ECI-vw enables its application across different types of surgeries, including cardiac valve surgery.

This study offers a new perspective on the postoperative prognosis assessment of patients with heart valve disease, contributing to the advancement of personalized medicine in the field of cardiac surgery. With further research, we hope to improve the accuracy of perioperative risk assessments, provide better medical services to patients, and improve their long-term health outcomes.

Despite the promising predictive capabilities of ACCI and ECI-vw shown in this study, they still have limitations. First, these scoring tools mainly focus on patients’ comorbidities and overall health conditions, without considering specific risk factors related to the surgery itself, such as the duration of surgery, technical difficulty, and intraoperative complications. Therefore, relying solely on ACCI and ECI-vw may not fully assess postoperative risk. In clinical practice, ACCI and ECI-vw should be considered alongside other clinical information for a comprehensive assessment of postoperative risk.

However, we need to be cautious in interpreting our findings, with some limitations. First, the limitations of our study is the inability to compare the Age-Adjusted Charlson Comorbidity Index (ACCI) and the Elixhauser Comorbidity Index-Van Walraven (ECI-VW) with established risk prediction models such as the EuroSCORE and the Society of Thoracic Surgeons (STS) score. This limitation arises due to the lack of comprehensive echocardiographic data, specifically the ejection fraction (EF) values, which are essential for calculating the EuroSCORE and STS score. Consequently, our analysis focuses solely on the predictive utility of ACCI and ECI-VW in the context of our available data.Second, our study is based on the extraction of data from INSPIRE database and has not yet been validated in a clinical setting. Third, although multivariate analysis has controlled for potential confounders as much as possible, there may still be other influencing factors not included in the model. For example, patients’ lifestyle habits, nutritional status, and psychological state may also impact postoperative prognosis, but these factors were not considered in this study. Additionally, due to the retrospective nature of this analysis, there is potential for information bias and selection bias.

Future research could consider integrating more surgery-related risk factors, such as the degree of surgical trauma, intraoperative blood management, and perioperative care level, to construct a more comprehensive risk assessment model. Prospective multicenter studies could help validate the results of this study and further explore the applicability of ACCI and ECI-vw among different populations and regions.