Development of machine learning models for mortality risk prediction after cardiac surgery

Enhancing mortality prediction after coronary artery bypass graft: a machine learning approach utilizing EuroScore

Aim: We developed a machine learning model using EuroScore assumptions and preoperative and intraoperative risk factors to predict mortality after coronary artery bypass graft (CABG). Materials & methods: We retrospectively examined data from 108 CABG patients at King Abdullah University Hospital, classifying them into risk groups via EuroScore and predicting mortality through random forest classification. Results: High-risk patients displayed longer surgical times and significant factors such as age and surgery choice. The median EuroScore was 0.95 (0.5-6.4). The model yielded high AUC scores (0.98, 0.95) indicating strong predictive accuracy. Conclusion: Our findings showed that the machine learning models combined with the EuroScore significantly improve post-CABG mortality prediction. For further validation, larger datasets are needed.

A review of evaluation approaches for explainable AI with applications in cardiology

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-024-10852-w.

The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery

Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery utilizing immense amount of data generated in the course of their care. When deployed, AI can be used to analyze this information at the patient's bedside more expediently and accurately, all while providing new insights. This review summarizes the current applications of AI in cardiac surgery, from the vantage point of a patient's journey. Applications of AI include pre-operative risk assessment, intraoperative planning, post-operative patient care and out-patient telemonitoring, encompassing the spectrum of cardiac surgical care. Offloading of administrative processes and enhanced experience with information gathering also represent a unique and underrepresented avenue for future utilization of AI. As clinicians, understanding the nomenclature and applications of AI is important to contextualize problems, to ensure problem-driven solutions and for clinical benefit. Precision medicine, and thus clinically relevant AI, remains dependent on data curation and warehousing to gather insights from large multicenter repositories while treating privacy with the utmost importance. AI tasks should not be siloed but rather holistically integrated into clinical workflow to retain context and relevance. As cardiac surgeons, AI allows us to look forward to a bright future of more efficient utilization of our clinical expertise toward high-level decision making and technical prowess.

Artificial intelligence in cardiac surgery: A systematic review

BACKGROUND

Artificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery.

METHODS

A systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized.

RESULTS

A total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest (n = 48), followed by support vector machine (n = 33), logistic regression (n = 32), and eXtreme Gradient Boosting (n = 31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes.

CONCLUSION

AI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision-making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.

Predicting post-discharge complications in cardiothoracic surgery: A clinical decision support system to optimize remote patient monitoring resources

Cardiac surgery patients are highly prone to severe complications post-discharge. Close follow-up through remote patient monitoring can help detect adverse outcomes earlier or prevent them, closing the gap between hospital and home care. However, equipment is limited due to economic and human resource constraints. This issue raises the need for efficient risk estimation to provide clinicians with insights into the potential benefit of remote monitoring for each patient. Standard models, such as the EuroSCORE, predict the mortality risk before the surgery. While these are used and validated in real settings, the models lack information collected during or following the surgery, determinant to predict adverse outcomes occurring further in the future. This paper proposes a Clinical Decision Support System based on Machine Learning to estimate the risk of severe complications within 90 days following cardiothoracic surgery discharge, an innovative objective underexplored in the literature. Health records from a cardiothoracic surgery department regarding 5 045 patients (60.8% male) collected throughout ten years were used to train predictive models. Clinicians' insights contributed to improving data preparation and extending traditional pipeline optimization techniques, addressing medical Artificial Intelligence requirements. Two separate test sets were used to evaluate the generalizability, one derived from a patient-grouped 70/30 split and another including all surgeries from the last available year. The achieved Area Under the Receiver Operating Characteristic curve on these test sets was 69.5% and 65.3%, respectively. Also, additional testing was implemented to simulate a real-world use case considering the weekly distribution of remote patient monitoring resources post-discharge. Compared to the random resource allocation, the selection of patients with respect to the outputs of the proposed model was proven beneficial, as it led to a higher number of high-risk patients receiving remote monitoring equipment.