ASA-PS ClassificationNCT06629350
ASA Prediction Using Health Data and Medication Use
An observational study of ASA-PS Classification, run by Erasmus Medical Center at 1 site in Netherlands.
Trial summary
The development of a machine learning algorithm that predicts American Society of Anesthesiologist-Physical Status (ASA-PS) based on preoperative variables would not only improve clinical decision-making in patient risk stratification but also offer a more reliable tool for administrative and regulatory uses. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- ASA-PS Classification
- How many people
- 149,422 participants
- Where
- 1 siteNetherlands
- Study dates
- 25 Jun 2024to 27 Jun 2024
- 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
- No healthy volunteers
- Adult, Older adult
Study population: All patients who underwent a surgical, diagnostic or therapeutic procedure within the surgical suite of the Erasmus MC since 2018 (introduction new digital Hospital Information System) and who had a ASA-PS class recorded.
Who may be able to join
- Underwent a surgical, diagnostic or therapeutic procedure within the surgical suite of the Erasmus MC, and
- ASA-PS score recorded in electronic medical record (EMR), and
- A verified medication list in EMR, or a filled out preoperative anesthesiological health questionnaire registered in EMR
Who cannot join
- Age <18 at moment of surgery, or
- ASA-PS V-VI, or
- Opt-out registered in EMR
Trial locations and contacts
1 site in 1 country.
About this study
The development of a machine learning algorithm that predicts American Society of Anesthesiologist-Physical Status (ASA-PS) based on preoperative variables would not only improve clinical decision-making in patient risk stratification but also offer a more reliable tool for administrative and regulatory uses. Therefore, the development of such a machine learning tool presents a significant opportunity to advance both the science and practice of perioperative care. Incorporating medication use into the algorithm could further enhance its predictive power, as it is closely linked to systemic disease. This addition could help refine the ASA-PS classification, making it an even more valuable tool in the clinical setting.
Read the detailed description
The American Society of Anesthesiologists Physical Status (ASA-PS) classification system is a widely used tool for assessing surgical fitness and other clinical contexts. However, its inherent subjectivity and heavy reliance on clinician judgment can lead to inconsistencies in patient risk stratification, a critical component of perioperative care. Furthermore, the ASA-PS system has been adopted for various administrative and regulatory purposes beyond its original intent, such as quality assessment by the Dutch Health and Youth Care Inspectorate (IGJ), compensation decisions by private payers in the USA, patient triage, and determining suitability for certain types of surgery.
Given the broad and critical applications of the ASA-PS system, enhancing its precision and objectivity is of paramount importance. One way to achieve this is through the development of a machine learning algorithm that predicts ASA-PS based on preoperative variables. Anesthesiologists base the ASA-PS score on the presence of systemic diseases, which can be inferred from medication use. By leveraging data such as Anatomical Therapeutic Chemical (ATC) codes, BMI, sex, age, routinely collected preoperative health data, and medication use, this algorithm could provide a more consistent and objective measure of ASA-PS.
This would not only improve clinical decision-making in patient risk stratification but also offer a more reliable tool for administrative and regulatory uses. Therefore, the development of such a machine learning tool presents a significant opportunity to advance both the science and practice of perioperative care. Incorporating medication use into the algorithm could further enhance its predictive power, as it is closely linked to systemic disease. This addition could help refine the ASA-PS classification, making it an even more valuable tool in the clinical setting.
- Conditions
- ASA-PS Classification
- Keywords
- machine learning, ASA-PS, Medication, Health questionnaire
What is being measured
The primary outcome is the main question the study is designed to answer.
The American Society of Anesthesiologists physical status (ASA-PS) class
The dependent response variable will be the ASA-PS class, both as a four-level variable (ASA-PS I, II, III and IV) and a two-level variable (ASA-PS I and II versus ASA-PS III and IV). The ASA-PS class was assigned to the patient and recorded in the patients file in the EMR by an anesthesiologist of resident anesthesiology as a part of the routinely performed preoperative anesthesiological screening in preparation for a procedure.
Measured: Day 0
Secondary outcomes (10)
Performance metrics: accuracy
The correct classification of the ASA-PS score will be evaluated using performance metrics of the machine learning algorithms. Common performance metrics include: Accuracy (the proportion of correctly predicted instances).
Measured: day 0
Performance metrics: precision
The correct classification of the ASA-PS score will be evaluated using performance metrics of the machine learning algorithms. Common performance metrics include: precision (the ratio of true positive predictions to the total positive predictions)
Measured: day 0
Performance metrics:recall
The correct classification of the ASA-PS score will be evaluated using performance metrics of the machine learning algorithms. Common performance metrics include:ecall (sensitivity or the ratio of true positive predictions to the actual positive instances)
Measured: day 0
Performance metrics: F1-score
The correct classification of the ASA-PS score will be evaluated using performance metrics of the machine learning algorithms. Common performance metrics include: F1-score (the harmonic mean of precision and recall).
Measured: day 0
Performance metrics: Area Under the Receiver Operating Characteristic Curve
The correct classification of the ASA-PS score will be evaluated using performance metrics of the machine learning algorithms. Common performance metrics include:the Area Under the Receiver Operating Characteristic Curve (AUC-ROC, Measures the model's ability to discriminate between positive and negative instances).
Measured: day 0
Calibration
Calibration plots will be used to assess the agreement between predictions and the event rate (i.e. correct classification).
Measured: Day 0
Misclassification of the ASA-PS score
A manual review of a selection of misclassifications will be performed by two anesthesiologists to qualitatively assess the cause of the misclassification.
Measured: Day 0
Explainability of the prediction model:Shapley additive explanations (SHAP)
Shapley additive explanations (SHAP) if applicable, as it can offer insights into the contribution of each feature to the prediction of individual instances.
Measured: day 0
Explainability of the prediction model:Local interpretable model-agnostic explanations (LIME)
Local interpretable model-agnostic explanations (LIME) can offer insights into the contribution of each feature to the prediction of individual instances.
Measured: day 0
Optimal sample size
Analysis of the learning curves to determine if additional data would likely improve the model's performance or if the current dataset is sufficient.
Measured: day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Oct 2024
- Study start25 Jun 2024
- Primary completion27 Jun 2024
- Study completion27 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ASA-PS ClassificationStudies of the same condition, open and under way.
- More trials run by Erasmus Medical CenterThe sponsor's other registered studies.
- 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
- Retrospective
- Enrolment
- 149,422 participants (actual)
Sponsor and investigators
- Lead sponsor
- Erasmus Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Health Holland
- Responsible party
- Principal investigator, Jan-Wiebe Korstanje, Erasmus Medical Center
- Investigators
- Jan-Wiebe Korstanje, MD MSc PhD, Principal investigator, Erasmus Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06629350
- Sponsor's protocol ID
- MEC-2020-0051/MEC-2024-0181
- Status last verified
- May 2025
- First submitted
- 1 Oct 2024
- Last updated
- 18 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All (Underlying) pseudonymised data will be made available alongside with the publication to execute the training and validation of the models. Data will be uploaded in dataverse.
Study at a glance
From the registry record, last updated by the sponsor on 18 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ASA Prediction Using Health Data and Medication Use
- ClinicalTrials.gov ID
- NCT06629350
- Acronym / sponsor ID
- MEC-2020-0051/MEC-2024-0181
- Conditions
- ASA-PS Classification
- Keywords
- machine learning, ASA-PS, Medication, Health questionnaire
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 149,422 participants (actual)
- Lead sponsor
- Erasmus Medical CenterUniversity, hospital or other
- Collaborators
- Health Holland
- Study start
- 25 Jun 2024 (actual)
- Primary completion
- 27 Jun 2024 (actual)
- Study completion
- 27 Jun 2024 (actual)
- First posted
- 8 Oct 2024 (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.
The development of a machine learning algorithm that predicts American Society of Anesthesiologist-Physical Status (ASA-PS) based on preoperative variables would not only improve clinical decision-making in patient risk stratification but also offer a more reliable tool for administrative and regulatory uses. Therefore, the development of such a machine learning tool presents a significant opportunity to advance both the science and practice of perioperative care. Incorporating medication use into the algorithm could further enhance its predictive power, as it is closely linked to systemic disease. This addition could help refine the ASA-PS classification, making it an even more valuable tool in the clinical setting.
Detailed description
The American Society of Anesthesiologists Physical Status (ASA-PS) classification system is a widely used tool for assessing surgical fitness and other clinical contexts. However, its inherent subjectivity and heavy reliance on clinician judgment can lead to inconsistencies in patient risk stratification, a critical component of perioperative care. Furthermore, the ASA-PS system has been adopted for various administrative and regulatory purposes beyond its original intent, such as quality assessment by the Dutch Health and Youth Care Inspectorate (IGJ), compensation decisions by private payers in the USA, patient triage, and determining suitability for certain types of surgery.
Given the broad and critical applications of the ASA-PS system, enhancing its precision and objectivity is of paramount importance. One way to achieve this is through the development of a machine learning algorithm that predicts ASA-PS based on preoperative variables. Anesthesiologists base the ASA-PS score on the presence of systemic diseases, which can be inferred from medication use. By leveraging data such as Anatomical Therapeutic Chemical (ATC) codes, BMI, sex, age, routinely collected preoperative health data, and medication use, this algorithm could provide a more consistent and objective measure of ASA-PS.
This would not only improve clinical decision-making in patient risk stratification but also offer a more reliable tool for administrative and regulatory uses. Therefore, the development of such a machine learning tool presents a significant opportunity to advance both the science and practice of perioperative care. Incorporating medication use into the algorithm could further enhance its predictive power, as it is closely linked to systemic disease. This addition could help refine the ASA-PS classification, making it an even more valuable tool in the clinical setting.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All patients who underwent a surgical, diagnostic or therapeutic procedure within the surgical suite of the Erasmus MC since 2018 (introduction new digital Hospital Information System) and who had a ASA-PS class recorded.
Key inclusion criteria (3 listed)
- Underwent a surgical, diagnostic or therapeutic procedure within the surgical suite of the Erasmus MC, and
- ASA-PS score recorded in electronic medical record (EMR), and
- A verified medication list in EMR, or a filled out preoperative anesthesiological health questionnaire registered in EMR
Key exclusion criteria (3 listed)
- Age <18 at moment of surgery, or
- ASA-PS V-VI, or
- Opt-out registered in EMR
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
The American Society of Anesthesiologists physical status (ASA-PS) class
Time frame: Day 0
The dependent response variable will be the ASA-PS class, both as a four-level variable (ASA-PS I, II, III and IV) and a two-level variable (ASA-PS I and II versus ASA-PS III and IV). The ASA-PS class was assigned to the patient and recorded in the patients file in the EMR by an anesthesiologist of resident anesthesiology as a part of the routinely performed preoperative anesthesiological screening in preparation for a procedure.
10 secondary outcomes
Performance metrics: accuracy
Time frame: day 0
Performance metrics: precision
Time frame: day 0
Performance metrics:recall
Time frame: day 0
Performance metrics: F1-score
Time frame: day 0
Performance metrics: Area Under the Receiver Operating Characteristic Curve
Time frame: day 0
Calibration
Time frame: Day 0
Misclassification of the ASA-PS score
Time frame: Day 0
Explainability of the prediction model:Shapley additive explanations (SHAP)
Time frame: day 0
Explainability of the prediction model:Local interpretable model-agnostic explanations (LIME)
Time frame: day 0
Optimal sample size
Time frame: day 0
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 18 May 2025.
- Individual participant dataSharing planned: Yes. All (Underlying) pseudonymised data will be made available alongside with the publication to execute the training and validation of the models. Data will be uploaded in dataverse.
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.
Jan-Wiebe Korstanje, MD MSc PhD principal investigator
Erasmus Medical Center
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 ASA-PS Classification, run by Erasmus Medical Center at 1 site in Netherlands.
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 3 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 18 May 2025. 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 Netherlands. 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?
The American Society of Anesthesiologists physical status (ASA-PS) class (time frame: Day 0). 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 18 May 2025.