Preoperative Risk AssessmentAI-Based ASA CNCT07407998
AI-Based ASA Classification in Preoperative Patients
An observational study of Preoperative Risk Assessment and ASA Physical Status Classification, run by Bursa City Hospital at 1 site in Turkey (Türkiye).
Trial summary
This prospective observational study aims to evaluate the performance of multiple artificial intelligence-based large language models in assigning American Society of Anesthesiologists Physical Status (ASA-PS) classifications in adult preoperative patients. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Preoperative Risk Assessment, ASA Physical Status Classification
- How many people
- 128 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Dec 2024to 15 Jan 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population consists of adult patients presenting for routine preoperative anesthesia assessment at Bursa City Hospital, including individuals with varying comorbidities and surgical risk profiles.
Who may be able to join
- Adult patients aged 18 years or older
- Undergoing routine preoperative anesthesia evaluation
- Classified as ASA Physical Status I-IV
- Availability of complete clinical data required for AI assessment
Who cannot join
- Patients younger than 18 years
- Refusal to participate
- Incomplete or missing clinical information
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Evaluation of Artificial Intelligence Models in Assigning American Society of Anesthesiologists Physical Status Classification in Preoperative Patients: A Prospective Observational Study
This prospective observational study aims to evaluate the performance of multiple artificial intelligence-based large language models in assigning American Society of Anesthesiologists Physical Status (ASA-PS) classifications in adult preoperative patients. AI-generated ASA scores obtained using both prompted and unprompted clinical scenario inputs will be compared with assessments performed by experienced anesthesiologists. The agreement, accuracy, readability, and overall quality of AI outputs will be analyzed to determine the potential role of artificial intelligence in supporting preoperative risk stratification.
Read the detailed description
The American Society of Anesthesiologists Physical Status (ASA-PS) classification is widely used for perioperative risk stratification but is subject to interobserver variability. Recent advances in artificial intelligence and large language models have introduced new opportunities for clinical decision support.
This prospective observational study includes adult patients undergoing routine preoperative anesthesia evaluation at Bursa City Hospital. Demographic data, medical history, comorbidities, functional capacity, laboratory findings, electrocardiography, chest imaging results, and planned surgical procedures are recorded to construct standardized clinical scenarios.
Multiple artificial intelligence models, including large language model-based systems, are provided with patient scenarios using both structured prompts and unstructured inputs. Each model assigns an ASA-PS classification and provides explanatory text. AI-generated classifications are compared with assessments performed independently by experienced anesthesiologists.
Primary outcomes include agreement and accuracy between AI-generated and clinician-assigned ASA classifications using Cohen's Kappa statistics. Secondary outcomes include readability assessment using the Ateşman Turkish Readability Index and response quality evaluation using the Global Quality Scale.
The study aims to explore whether artificial intelligence can improve standardization, objectivity, and efficiency in preoperative risk assessment while highlighting the strengths and limitations of current AI technologies in clinical anesthesia practice.
- Keywords
- Artificial Intelligence, Large Language Models, Preoperative Evaluation, ASA Classification
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement Between AI-Generated and Clinician-Assigned ASA Physical Status Classification
Level of agreement between artificial intelligence models and anesthesiologists in assigning ASA Physical Status classification measured using Cohen's Kappa coefficient
Measured: Preprocedural/Perioperative
Secondary outcomes (2)
Accuracy of AI Models in ASA Classification
Proportion of correct ASA Physical Status classifications generated by artificial intelligence models compared with anesthesiologist assessments
Measured: Preprocedural/Perioperative
Readability of AI-Generated Clinical Responses
Readability scores of artificial intelligence-generated clinical responses assessed using the Ateşman Turkish Readability Index (range: 0-100), where higher scores indicate better readability.
Measured: Preprocedural/Perioperative
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Feb 2026
- Study start15 Dec 2024
- Primary completion15 Dec 2024
- Study completion15 Jan 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Preoperative Risk AssessmentStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 128 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bursa City Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, eralp çevikkalp, Bursa City Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07407998
- Sponsor's protocol ID
- Bursa City Hospital 004
- Acronym
- AI-Based ASA C
- Status last verified
- Feb 2026
- First submitted
- 4 Feb 2026
- Last updated
- 17 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to patient confidentiality and institutional data protection policies.
Study at a glance
From the registry record, last updated by the sponsor on 17 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Artificial Intelligence Models in Assigning American Society of Anesthesiologists Physical Status Classification in Preoperative Patients: A Prospective Observational Study
- ClinicalTrials.gov ID
- NCT07407998
- Acronym / sponsor ID
- AI-Based ASA C / Bursa City Hospital 004
- Keywords
- Artificial Intelligence, Large Language Models, Preoperative Evaluation, ASA Classification
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 128 participants (actual)
- Lead sponsor
- Bursa City HospitalOther government
- Study start
- 15 Dec 2024 (actual)
- Primary completion
- 15 Dec 2024 (actual)
- Study completion
- 15 Jan 2026 (actual)
- First posted
- 12 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This prospective observational study aims to evaluate the performance of multiple artificial intelligence-based large language models in assigning American Society of Anesthesiologists Physical Status (ASA-PS) classifications in adult preoperative patients. AI-generated ASA scores obtained using both prompted and unprompted clinical scenario inputs will be compared with assessments performed by experienced anesthesiologists. The agreement, accuracy, readability, and overall quality of AI outputs will be analyzed to determine the potential role of artificial intelligence in supporting preoperative risk stratification.
Detailed description
The American Society of Anesthesiologists Physical Status (ASA-PS) classification is widely used for perioperative risk stratification but is subject to interobserver variability. Recent advances in artificial intelligence and large language models have introduced new opportunities for clinical decision support.
This prospective observational study includes adult patients undergoing routine preoperative anesthesia evaluation at Bursa City Hospital. Demographic data, medical history, comorbidities, functional capacity, laboratory findings, electrocardiography, chest imaging results, and planned surgical procedures are recorded to construct standardized clinical scenarios.
Multiple artificial intelligence models, including large language model-based systems, are provided with patient scenarios using both structured prompts and unstructured inputs. Each model assigns an ASA-PS classification and provides explanatory text. AI-generated classifications are compared with assessments performed independently by experienced anesthesiologists.
Primary outcomes include agreement and accuracy between AI-generated and clinician-assigned ASA classifications using Cohen's Kappa statistics. Secondary outcomes include readability assessment using the Ateşman Turkish Readability Index and response quality evaluation using the Global Quality Scale.
The study aims to explore whether artificial intelligence can improve standardization, objectivity, and efficiency in preoperative risk assessment while highlighting the strengths and limitations of current AI technologies in clinical anesthesia practice.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients presenting for routine preoperative anesthesia assessment at Bursa City Hospital, including individuals with varying comorbidities and surgical risk profiles.
Key inclusion criteria (4 listed)
- Adult patients aged 18 years or older
- Undergoing routine preoperative anesthesia evaluation
- Classified as ASA Physical Status I-IV
- Availability of complete clinical data required for AI assessment
Key exclusion criteria (3 listed)
- Patients younger than 18 years
- Refusal to participate
- Incomplete or missing clinical information
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Agreement Between AI-Generated and Clinician-Assigned ASA Physical Status Classification
Time frame: Preprocedural/Perioperative
Level of agreement between artificial intelligence models and anesthesiologists in assigning ASA Physical Status classification measured using Cohen's Kappa coefficient
2 secondary outcomes
Accuracy of AI Models in ASA Classification
Time frame: Preprocedural/Perioperative
Readability of AI-Generated Clinical Responses
Time frame: Preprocedural/Perioperative
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Jun 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to patient confidentiality and institutional data protection policies.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Preoperative Risk Assessment and ASA Physical Status Classification, run by Bursa City Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between AI-Generated and Clinician-Assigned ASA Physical Status Classification (time frame: Preprocedural/Perioperative). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Jun 2026.