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.

Inclusion Criteria:

Adults aged 18 years or older

ASA physical status classification I-IV

Scheduled for non-cardiac surgery

Patients evaluated preoperatively by anesthesia residents with less than two years of clinical experience

Availability of complete clinical data including medical history, ECG findings, and chest radiography

Ability to provide informed consent

Exclusion Criteria:

Patients younger than 18 years of age

Patients undergoing cardiac surgery

Patients with incomplete clinical data

Patients who declined participation

Emergency surgery cases

Patients unable to undergo standard preoperative evaluation

This study will be conducted with the participation of patients who will be referred to cardiology following an examination at the anesthesia clinic prior to undergoing non-cardiac surgery.

Due to our commitment to patient rights, data privacy, and the consent form