In the United States, where more than 20 million surgeries occur annually, current methods for assessing risks before surgery, especially predicting outcomes such as mortality, prove to be ineffective.
Despite ongoing advancements in guidelines and tools, such as biomarkers and other data, accurately predicting postoperative risks remains difficult. This underscores the need for simpler methods to assess risks before surgery, aiming to identify high-risk patients earlier and improve predictions for a diverse group undergoing surgery.
Deep-learning analyses offer a new opportunity to identify hidden risk markers and understand complex relationships using available clinical resources for risk prediction. One valuable resource is the 12-lead electrocardiogram (ECG), a cost-effective, non-invasive diagnostic test routinely done in preoperative settings.
Prior research has shown that using deep-learning algorithms on ECG waveforms can reveal clinical traits and outcomes not identified by standard ECG measures or expert human interpretations.
The idea is that using deep-learning algorithms on a single preoperative ECG can effectively differentiate postoperative mortality outcomes and outperform established clinical preoperative assessment methods.
The Lancet Digital Health recently published a groundbreaking study by Cedars-Sinai Medical Center researchers, introducing a powerful deep-learning algorithm. This algorithm enhances postoperative mortality prediction by analyzing preoperative electrocardiograms (ECGs).
To test this concept, researchers conducted a comprehensive study using an artificial intelligence (AI) algorithm trained on perioperative ECGs. They evaluated the performance of the resulting model on cohorts from three independent health-care systems.
Methods and results
The study involved 45,969 patients undergoing medical procedures who needed a full ECG within 30 days before the procedure.
Trained on Cedars-Sinai Medical Center data, the algorithm showed impressive discriminatory capabilities. It achieved an AUC of 0.83 in the internal test cohort, outperforming the RCRI score’s AUC of 0.67.
Researchers divided patients into training, internal validation, and final algorithm test cohorts using a diverse dataset that included 59,975 inpatient procedures and 112,794 ECGs.
They also tested the algorithm’s performance in two external hospital cohorts, showcasing its consistent and robust predictive power.
Key findings show the algorithm’s superiority over the RCRI score in predicting postoperative mortality. High-risk patients identified by the algorithm had a significantly higher unadjusted odds ratio (8.83) for postoperative mortality compared to those with RCRI scores over 2 (2.08).
The algorithm proved effective across various medical procedures, including cardiac surgeries (AUC 0.85), non-cardiac surgeries (AUC 0.83), and catheterization or endoscopy suite procedures (AUC 0.76).
Conclusions
This deep-learning algorithm is a breakthrough in improving postoperative mortality risk stratification. Its ability to interpret preoperative ECGs could revolutionize medical procedure decision-making. Researchers validated the algorithm’s robustness in three healthcare systems, emphasizing its broad applicability.
The National Heart, Lung, and Blood Institute funded the study, indicating a significant stride towards AI-based, precise, and personalized patient care in perioperative settings.
