Machine learning model predicts mortality risk for individual cardiac surgery patients

The new data-driven algorithm, built on a stockpile of electronic health records (EHR), is said to be the first institution-specific model for assessing a cardiac patient’s risk prior to surgery, which will allow health care providers to pursue the best course of action for individual patients. The team’s work is detailed in The Journal of Thoracic and Cardiovascular Surgery (JTCVS) Open.

“The standard-of-care risk models used today are limited by their applicability to specific types of surgeries, leaving out significant numbers of patients undergoing complex or combination procedures for which no models exist,” senior author Ravi Iyengar, PhD, said in a statement. “Our team rigorously combined electronic health record data and machine learning methods to demonstrate for the first time how individual institutions can build their own risk models for post-cardiac surgery mortality.”

Prediction models based on machine learning algorithms have been generated across diverse fields of medicine, and some have shown improved results over their standard-of-care counterparts.

In cardiac surgery, the Society of Thoracic Surgeons (STS) risk scores are considered the gold standard and are routinely used to assess a cardiac surgery patient’s procedural risk. While they continue to provide important benchmarks for hospitals to evaluate and improve their performance, they are derived from population-level data and may fail to accurately predict risk for specific patients with complicated pathologies who require tailored preoperative evaluations and complex surgeries.

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Cardiovascular surgeons and data science specialists at The Mount Sinai Hospital, supervised by co-senior author Gaurav Pandey, PhD, Associate Professor of Genetics and Genomic Sciences at Icahn Mount Sinai, hypothesised that machine learning-based models using EHR data from their own institution could offer an effective solution.

To this end, they created a rigorous machine learning framework using routinely collected EHR data to develop a risk prediction model for postsurgical mortality that is personalised to the patient and specific to the hospital, incorporating important information about Mount Sinai’s patient population, including demographics, socioeconomic factors, and health characteristics.

According to Mount Sinai, this contrasts with population-derived models like STS, which are based on data from diverse health systems in different parts of the US. Further driving the performance of this methodology was an open-source prediction algorithm dubbed XGBoost that builds a set of decision trees by progressively focusing on harder-to-predict subsets of training data.

The research team used XGBoost to model 6,392 cardiac surgeries performed at The Mount Sinai Hospital from 2011 to 2016, including heart valve procedures; coronary artery bypass graft; aortic resection, replacement, or anastomosis; and reoperative cardiac surgeries. The team then compared the performance of its model to STS models for the same patient sets.

The study showed that the XGBoost model outperformed STS risk scores for mortality in all commonly conducted categories of cardiac surgery for which STS scores were designed. Prediction performance of the XGBoost model across all surgery types was also high, demonstrating the potential of machine learning and EHR data for building effective institution-specific models.

“Accurate prediction of postsurgical mortality is critical to ensure the best outcomes for cardiac surgery patients, and our study shows that institution-specific models may be preferable to the clinical standard based on population data,” said Dr Pandey. “Just as importantly, we’ve demonstrated that it’s practical for health care institutions to develop their own predictive models through sophisticated machine learning algorithms to replace or complement the established STS template.”