Predictive value of machine learning for mortality risk in aortic dissection: a systematic review and meta-analysis

Aortic dissection (AD) is a critical cardiovascular disorder with substantial risks of short-term mortality. Some researchers have endeavored to utilize machine learning (ML) approaches to develop predictive models for the risk of mortality in AD. However, systematic evidence about the accuracy of these models remains scarce, which poses challenges to the development and enhancement of risk assessment tools. Therefore, this study seeks to systematically review the reliability of ML in forecasting the risk of mortality in AD.

A search was implemented through PubMed, Cochrane, Embase, and Web of Science up to September 11, 2025. The prediction model risk of bias (RoB) assessment tool (PROBAST) was leveraged to estimate the RoB of the included studies. Subgroup analyses were implemented based upon types of AD and time of death.

In total, 35 studies were included, covering 19,838 patients with AD. The results showed that, within the training datasets, ML models demonstrated a sensitivity (SEN) of 0.75 (95% CI: 0.72-0.78) and specificity (SPE) of 0.77 (95% CI: 0.74-0.80) for predicting mortality in AD. Within the validation set, which mainly focused on TAAD, the SEN was 0.79 (95% CI: 0.74-0.84) and the SPE was 0.78 (95% CI: 0.68-0.85). For in-hospital mortality, the SEN was 0.78 (95% CI: 0.72-0.83) and the SPE was 0.77 (95% CI: 0.65-0.86); for out-of-hospital mortality, the SEN and SPE were 0.81-0.84 and 0.74-0.86.

ML models demonstrate remarkable accuracy in forecasting the risk of mortality in AD and show superior performance relative to existing scoring systems to some extent. Future research should incorporate more multi-center, multi-ethnic, and geographically varied cases to develop a more broadly applicable risk prediction tool and offer insights for the tailored prevention strategies.