Alapfogalmak
AI deep-learning algorithm predicts postoperative mortality accurately.
Kivonat
The study focused on an AI deep-learning algorithm, PreOpNet, trained on preoperative ECGs to predict postoperative mortality in patients undergoing various surgeries and procedures. The algorithm outperformed the Revised Cardiac Risk Index (RCRI) in identifying high-risk patients. Key highlights include:
PreOpNet showed an AUC of 0.83 in discriminating mortality, compared to RCRI's AUC of 0.67.
Patients identified as high risk by PreOpNet had a significantly higher odds ratio for postoperative mortality.
The algorithm performed well in discriminating mortality in both cardiac and noncardiac surgery patients.
External validation in different healthcare systems showed consistent performance.
The AI model could potentially improve clinical risk prediction tools.
Statisztikák
The algorithm discriminated mortality with an AUC of 0.83 compared to RCRI's AUC of 0.67.
Patients identified as high risk by the deep-learning model had an unadjusted odds ratio for postoperative mortality of 9.17.
PreOpNet showed an AUC of 0.85 for patients undergoing cardiovascular surgery and 0.83 for noncardiac surgery.
Idézetek
"Current clinical risk prediction tools are insufficient." - David Ouyang