AI- atria oracle - an artificial intelligence sentinel for precision prognostication of atrial fibrillation risks in post-cardiac surgery care- pilot study

Introduction:

Embarking on the forefront of cardiac care, this study pioneers an AI-driven scoring system for predicting atrial fibrillation risks post cardiac surgery. Drawing insights from established methodologies, ChatGPT crafts a sophisticated tool, demonstrating robust performance in a diverse cohort. This innovative approach holds the promise of revolutionizing preoperative risk assessment and subsequent management strategies in cardiac surgical interventions.

Objective:

This study endeavours to revolutionize the prediction of atrial fibrillation (AF) risk following cardiac surgery through the development of an innovative AI-driven scoring system. Drawing insights from five globally recognized articles on Scoring system for predicting the Post-Operative Atrial Fibrillation (POAF) in post-cardiac surgery, ChatGPT, an advanced language model, crafted a sophisticated tool for predicting complications before thoracic surgery.

Methods:

The research involves a comprehensive examination and synthesis of existing scoring methodologies related to AF complications post cardiac surgery. Employing state-of-the-art machine learning techniques, ChatGPT engineers a robust scoring system designed to enhance the accuracy of preoperative assessments, enabling the early identification of individuals at higher risk for postoperative atrial fibrillation.

Validation:

In a retrospective cohort study spanning from January 2023 to July 2023, this study evaluates 100 consecutive patients, 18 years and above, undergoing various cardiac surgical procedures. Focus is placed on the occurrence of atrial fibrillation within 30 days post-surgery. The AI-derived scoring system demonstrates compelling performance metrics, with an Area Under the Curve (AUC) of 0.85 in derivation and 0.77 in validation, indicating its efficacy in predicting atrial fibrillation risks.

Results:

Out of the 100 patients studied over a 7-month period, 18.5% developed postoperative atrial fibrillation. Key predictors identified include Age, Transischemic attack/stroke, Ejection fraction, Left Atrial Size and Surgical Procedure Type.

Limitation:

The study acknowledges potential limitations related to sample size, impacting generalizability. Additionally, inherent biases associated with retrospective study designs are duly recognized.

Conclusion:

 This study presents a cutting-edge AI model-based scoring system for the proactive assessment of atrial fibrillation risk following cardiac surgery. With a strong predictive accuracy and calibration, the developed system holds promise for optimizing preoperative risk stratification and subsequent management strategies in the context of cardiac surgical interventions.

To be updated shortly..