ASA-PS Classification
Conditions
Keywords
machine learning, ASA-PS, Medication, Health questionnaire
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.
Interventions
None listed
Sponsors
Health Holland
Erasmus Medical Center
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The American Society of Anesthesiologists physical status (ASA-PS) class | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Performance metrics: precision | day 0 | 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) |
| Performance metrics:recall | day 0 | 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) |
| Performance metrics: F1-score | day 0 | 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). |
| Performance metrics: Area Under the Receiver Operating Characteristic Curve | day 0 | 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). |
| Performance metrics: accuracy | day 0 | 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). |
| Misclassification of the ASA-PS score | Day 0 | A manual review of a selection of misclassifications will be performed by two anesthesiologists to qualitatively assess the cause of the misclassification. |
| Explainability of the prediction model:Shapley additive explanations (SHAP) | day 0 | Shapley additive explanations (SHAP) if applicable, as it can offer insights into the contribution of each feature to the prediction of individual instances. |
| Explainability of the prediction model:Local interpretable model-agnostic explanations (LIME) | day 0 | Local interpretable model-agnostic explanations (LIME) can offer insights into the contribution of each feature to the prediction of individual instances. |
| Optimal sample size | day 0 | Analysis of the learning curves to determine if additional data would likely improve the model's performance or if the current dataset is sufficient. |
| Calibration | Day 0 | Calibration plots will be used to assess the agreement between predictions and the event rate (i.e. correct classification). |
Countries
Netherlands
Outcome results
None listed