Key result
A random forest machine learning model with a reject option predicted 1-year survival in TAVI patients with 96% negative predictive value, 92% positive predictive value, and 96% accuracy.
Why the study?
Predicting 1-year survival in patients undergoing TAVI is indispensable for managing safe early discharge strategies and resource optimization.
Can machine learning models accurately predict 1-year survival in patients undergoing transfemoral TAVI?
Cohort (n=629)
Can machine learning models accurately predict 1-year survival in patients undergoing transfemoral TAVI?
A machine learning model using routinely acquired data can accurately predict 1-year survival in TAVI patients, which may help guide early discharge strategies and resource optimization.
ML models may predict 1-year TAVI survival with high accuracy in select patients; hypothesis-generating and requires prospective validation before guiding discharge.
BACKGROUND: predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization. METHODS: Routinely acquired data (134 variables) were used from 629 patients, who underwent transfemoral TAVI from 2012 up to 2018. Support vector machines, neuronal networks, random forests, nearest neighbour and Bayes models were used with new, previously unseen patients to predict 1-year mortality in TAVI patients. A genetic variable selection algorithm identified a set of predictor variables with high predictive power. RESULTS: Univariate analyses revealed 19 variables (clinical, laboratory, echocardiographic, computed tomographic and ECG) that significantly influence 1-year survival. Before applying the reject option, the model performances in terms of negative predictive value (NPV) and positive predictive value (PPV) were similar between all models. After applying the reject option, the random forest model identified a subcohort showing a negative predictive value of 96% (positive predictive value = 92%, accuracy = 96%). CONCLUSIONS: Our model can predict the 1-year survival with very high negative and sufficiently high positive predictive value, with very high accuracy. The "reject option" allows a high performance and harmonic integration of machine learning in the clinical decision process.
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Pollari et al. (2023) conducted a cohort in Transcatheter aortic valve implantation (TAVI) (n=629). Machine learning models (random forest) was evaluated on 1-year survival. A random forest machine learning model with a reject option predicted 1-year survival in TAVI patients with 96% negative predictive value, 92% positive predictive value, and 96% accuracy.
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