ASA Prediction Using Health Data and Medication Use

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.

Study Overview

• Status: Completed
• Conditions: ASA-PS Classification

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 Type: Observational
• Enrollment (Actual): 149422

Contacts and Locations

Study Locations

• Netherlands
  • Zuid-Holland
    • Rotterdam, Zuid-Holland, Netherlands, 3015GD
      • Erasmus MC

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

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.

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• Age <18 at moment of surgery, or
• ASA-PS V-VI, or
• Opt-out registered in EMR

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Day 0

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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).	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)	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)	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).	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).	day 0
Calibration	Calibration plots will be used to assess the agreement between predictions and the event rate (i.e. correct classification).	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.	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.	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.	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.	day 0

Collaborators and Investigators

• Sponsor: Erasmus Medical Center
• Collaborators: Health Holland
• Investigators: Principal Investigator: Jan-Wiebe Korstanje, MD MSc PhD, Erasmus Medical Center

Study record dates

Study Major Dates

• Study Start (Actual): June 25, 2024
• Primary Completion (Actual): June 27, 2024
• Study Completion (Actual): June 27, 2024

Study Registration Dates

• First Submitted: October 1, 2024
• First Submitted That Met QC Criteria: October 3, 2024
• First Posted (Actual): October 8, 2024

Study Record Updates

• Last Update Posted (Actual): May 18, 2025
• Last Update Submitted That Met QC Criteria: May 14, 2025
• Last Verified: May 1, 2025

More Information

Terms related to this study

• Keywords: machine learning; Medication; Health questionnaire; ASA-PS
• Other Study ID Numbers: MEC-2020-0051/MEC-2024-0181

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: 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.
• IPD Sharing Time Frame: to be determined, based on Dutch Law
• IPD Sharing Access Criteria: Only data requests in line with the Terms of Use will be taken into consideration. A Data Transfer Agreement (DTA) in line with European Union General Data Protection Regulation (EU-GDPR) regulations and/or the Research Collaboration Agreement (RCA) should be signed before data is shared. If a data request is approved, the data will be delivered in a safe and secure manner. By signing the DTA and/or RCA and accessing the Materials, the recipient represents his/her acceptance of the Terms of Use.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No