State‐of‐the‐art machine learning improves predictive accuracy of 1‐year survival after heart transplantation

Abstract

Heart transplantation (HT) remains the treatment of choice for patients with medically refractory end-stage heart failure given its improved long-term outcomes and quality of life. 1 Despite the effectiveness of the treatment, only about 3000 HTs are performed annually in the USA, with a small rise attributed to the opioid epidemic and the expanded use of donors with hepatitis C. 2 Optimization of outcomes and risk stratification is recognized as a critically important issue in HT today. 3 Although several risk scores have been developed for the prediction of outcomes after HT, their accuracy remains modest. Machine learning (ML) algorithms have shown promising results in predicting outcomes and discovering phenotypes in patients with heart failure 4 and have even been applied in patients undergoing HT. 5 However, further work is needed to determine the role of ML in the prediction of outcomes and risk stratification of HT patients. Herein, we present our work on prediction of 1-year survival after HT using state-of-the-art ML algorithms.We queried the United Network for Organ Sharing (UNOS) database for patients that were enlisted and underwent heart transplant between 2010 and 2018. Extensive baseline clinical and laboratory data as well as follow-up survival were collected. We excluded patients with multiple transplants during the index hospitalization, non-adult patients, and patients with >10% missing values or less than 1-year follow-up. The study dataset was then randomly split into training and validation cohorts with a 3:1 ratio. A random oversampling technique was applied to artificially equalize the number of patients that were alive with the number of patients that did not survive at 1-year in the training cohort. This is common practice for many ML algorithms. Next, a feature selection method based on Support Vector Machines (SVM) and Fuzzy Logic, namely, SVM-FuzCoC, 6 was used to (i) identify the most informative variables, (ii) reduce the complexity, and (iii) improve the performance of the ML models. The selected features were then used to train five state-of-the-art supervised ML algorithms: Adaptive boosting (Adaboost), SVM, Decision Trees, K-nearest neighbour, and Logistic Regression. 7 Training of the models was controlled by hyperparameter optimization. After training, the validation cohort was used to internally test the ML algorithms.

Machine learning‐based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis

“…The overall study design, involving data pre-processing, splitting into training and validation cohorts, feature selection, creation of ML models and explainability, follows a standard ML methodology that has been used by our group in prior studies. 4,20 In this study, we used the CatBoost for 1-year and 3-year mortality prediction, as this ML algorithm minimizes errors introduced by categorical variables and has been previously used multiple fields, including cardiology. 21 The Cat-Boost algorithm has also been specifically used in the field of organ transplantation to predict bleeding after liver transplantation.…”