Clinical trial NCT06629350: ASA Prediction Using Health Data and Medication Use
Official title: ASA Prediction Using Health Data and Medication Use
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: ASA-PS Classification
Keywords: machine learning
ASA-PS
Medication
Health questionnaire
Lead sponsor: Erasmus Medical Center
Sponsor class: OTHER
Collaborators: name: Health Holland
First posted: 2024-10-08
Enrollment: 149422
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

Brief 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. 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.

Eligibility criteria: 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

Primary outcomes:
measure: The American Society of Anesthesiologists physical status (ASA-PS) class
timeFrame: Day 0
Secondary outcomes:
measure: Performance metrics: accuracy
timeFrame: day 0
measure: Performance metrics: precision
timeFrame: day 0
measure: Performance metrics:recall
timeFrame: day 0
measure: Performance metrics: F1-score
timeFrame: day 0
measure: Performance metrics: Area Under the Receiver Operating Characteristic Curve
timeFrame: day 0
measure: Calibration
timeFrame: Day 0
measure: Misclassification of the ASA-PS score
timeFrame: Day 0
measure: Explainability of the prediction model:Shapley additive explanations (SHAP)
timeFrame: day 0
measure: Explainability of the prediction model:Local interpretable model-agnostic explanations (LIME)
timeFrame: day 0
measure: Optimal sample size
timeFrame: day 0
Other outcomes:
Locations:
city: Rotterdam
country: Netherlands
facility: Erasmus MC
state: Zuid-Holland
Study officials:
affiliation: Erasmus Medical Center
name: Jan-Wiebe Korstanje, MD MSc PhD
role: PRINCIPAL_INVESTIGATOR
References: