A machine learning approach to high‐risk cardiac surgery risk scoring

Abstract

Introduction In patients undergoing high‐risk cardiac surgery, the uncertainty of outcome may complicate the decision process to intervene. To augment decision‐making, a machine learning approach was used to determine weighted personalized factors contributing to mortality. Methods American College of Surgeons National Surgical Quality Improvement Program was queried for cardiac surgery patients with predicted mortality ≥10% between 2012 and 2019. Multiple machine learning models were investigated, with significant predictors ultimately used in gradient boosting machine (GBM) modeling. GBM‐trained data were then used for local interpretable model‐agnostic explanations (LIME) modeling to provide individual patient‐specific mortality prediction. Results A total of 194 patient deaths among 1291 high‐risk cardiac surgeries were included. GBM performance was superior to other model approaches. The top five factors contributing to mortality in LIME modeling were preoperative dialysis, emergent cases, Hispanic ethnicity, steroid use, and ventilator dependence. LIME results individualized patient factors with model probability and explanation of fit. Conclusions The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high‐risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality.

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“…Artificial intelligence (AI) refers to the technology that aims to develop algorithms and computer systems capable of performing tasks that typically require human intelligence [ 1 ]. Therefore, the remit of AI is multi-faceted, reaching from language understanding through image and pattern recognition to decision making and problem solving [ 2 , 3 ]. AI is based on machine learning, whereby computers are generally taught to learn from data, and deep learning, which leverages neural networks to facilitate pattern recognition and decision-making [ 4 ].…”

Clinical Applications of Machine Learning

“…A case study in high-risk surgery utilized ML to enhance risk calculator predictions, and elucidate individual features and their contributions to mortality prediction. 18 After using a series of ML methods, including gradient boosting machine models, generalized linear models, random forest, and deep neural networks, the resultant modeling features were explored using a LIME approach. 19 LIME is a method that uses an interpretable model to explain the predictions of a regressor by approximating it locally.…”

“…As a result, the LIME approach allowed the identification of patient-specific factors influencing mortality and determining their weight in favor of or against patient survival, besides providing insights into personalized features and their impact on survival probabilities and model accuracy. 18 …”