Zaka Ammar, Mustafiz Cecil, Mutahar Daud, Sinhal Shreyans, Gorcilov James, Muston Benjamin, Evans Shaun, Gupta Aashray, Stretton Brandon, Kovoor Joshua, Mridha Naim, Sivagangabalan Gopal, Thiagalingam Aravinda, Ramponi Fabio, Chan Justin, Bennetts Jayme, Murdoch Dale J, Zaman Sarah, Chow Clara K, Jayasinghe Rohan, Kovoor Pramesh, Bacchi Stephen
Gold Coast Hospital and Health Service, Southport, Queensland, Australia
Griffith University, Brisbane, Queensland, Australia.
Open Heart. 2025 Jan 21;12(1):e002779. doi: 10.1136/openhrt-2024-002779.
Accurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making and periprocedural planning. Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy.
PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. The primary outcome was comparative discrimination measured by C-statistics with 95% CIs between ML models and traditional methods in estimating the risk of all-cause mortality at 30 days and 1 year.
Nine studies were included (29 608 patients). The summary C-statistic of the top performing ML models was 0.79 (95% CI 0.71 to 0.86), compared with traditional methods 0.68 (95% CI 0.61 to 0.76). The difference in C-statistic between all ML models and traditional methods was 0.11 (p<0.00001). Of the nine studies, two studies provided externally validated models and three studies reported calibration. Prediction Model Risk of Bias Assessment Tool tool demonstrated high risk of bias for all studies.
ML models outperformed traditional risk scores in the discrimination of all-cause mortality following TAVI. While integration of ML algorithms into electronic healthcare systems may improve periprocedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.
经导管主动脉瓣植入术（TAVI）后准确的死亡率预测对于降低风险、共同决策和围手术期规划至关重要。手术风险模型对接受TAVI的患者显示出适度的判别价值，且通常校准不佳，而TAVI特异性模型有渐进性改善。机器学习（ML）模型提供了一种替代的风险分层方法，可能会提高预测准确性。
检索了PubMed、EMBASE、科学网和Cochrane数据库，直至2023年12月16日，以查找比较ML模型与传统统计方法用于TAVI后事件预测的研究。主要结局是通过C统计量衡量的比较判别能力，95%置信区间表示ML模型与传统方法在估计30天和1年全因死亡率风险方面的差异。
纳入9项研究（29608例患者）。表现最佳的ML模型的汇总C统计量为0.79（95%置信区间0.71至0.86），而传统方法为0.68（95%置信区间0.61至0.76）。所有ML模型与传统方法之间C统计量的差异为0.11（p<0.00001）。在这9项研究中，2项研究提供了外部验证模型，3项研究报告了校准情况。预测模型偏倚风险评估工具显示所有研究均存在高偏倚风险。
在TAVI后全因死亡率的判别方面，ML模型优于传统风险评分。虽然将ML算法整合到电子医疗系统中可能会改善围手术期风险分层，但在临床环境中立即实施仍不确定。需要进一步研究以克服方法学和验证方面的局限性。
