The Predictability of the Necessity for Cardiology Consultation in Patients Scheduled for Non-Cardiac Surgery Using Artificial Intelligence Models in Preoperative Anesthesia Assessment

PREOPERATIVE CARDIOLOGY CONSULTATION REQUIREMENT, USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA

THE ROLE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA, ARTIFICIAL INTELLIGENCE IN CONSULTATION NEEDS

Structured Summary Title Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models Background Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays. Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations. Study Design Prospective, observational, comparative study. Ethical Approval The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki. Sample Size Sample size was calculated using G\*Power software based on anticipated effect size and statistical power requirements. Participants Inclusion Criteria: Adults aged 18 years or older ASA physical status I-IV Scheduled for non-cardiac surgery Evaluated by anesthesia residents with less than two years of clinical experience Exclusion Criteria: Pediatric patients Patients declining participation Incomplete clinical data Data Collection The following patient data will be recorded: Demographics (age, sex, BMI) Medical history (comorbidities, medication use, allergies, substance use) Functional capacity (METs score) ECG findings Chest radiography findings Planned surgical procedure characteristics AI Model Evaluation Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats: Prompted format: "You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation." Non-prompted format: "Evaluate whether this patient requires cardiology consultation." AI recommendations will not influence clinical decision-making. Outcome Measures Primary and secondary analyses will include: Agreement between AI recommendations and expert anesthesiologist evaluations Readability of AI-generated responses Quality assessment of responses Classification performance comparisons across models Statistical Analysis Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p \< 0.05 will be applied. Study Objective The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.

Structured Summary Title Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models Background Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays. Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations. Study Design Prospective, observational, comparative study. Ethical Approval The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki. Sample Size Sample size was calculated using G\*Power software based on anticipated effect size and statistical power requirements. Participants Inclusion Criteria: Adults aged 18 years or older ASA physical status I-IV Scheduled for non-cardiac surgery Evaluated by anesthesia residents with less than two years of clinical experience Exclusion Criteria: Pediatric patients Patients declining participation Incomplete clinical data Data Collection The following patient data will be recorded: Demographics (age, sex, BMI) Medical history (comorbidities, medication use, allergies, substance use) Functional capacity (METs score) ECG findings Chest radiography findings Planned surgical procedure characteristics AI Model Evaluation Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats: Prompted format: "You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation." Non-prompted format: "Evaluate whether this patient requires cardiology consultation." AI recommendations will not influence clinical decision-making. Outcome Measures Primary and secondary analyses will include: Agreement between AI recommendations and expert anesthesiologist evaluations Readability of AI-generated responses Quality assessment of responses Classification performance comparisons across models Statistical Analysis Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p \< 0.05 will be applied. Study Objective The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.

No related papers found yet.

Turkey (Türkiye)