Ignite Creation Date:
2025-12-24 @ 5:09 PM
Ignite Modification Date:
2025-12-25 @ 2:47 PM
Study NCT ID:
NCT06629350
Status:
COMPLETED
Last Update Posted:
2025-05-18
First Post:
2024-10-01
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
ASA Prediction Using Health Data and Medication Use
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2024-06-25', 'size': 643003, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2024-04-23T10:39', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 149422}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-06-25', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2024-06-27', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-05-14', 'studyFirstSubmitDate': '2024-10-01', 'studyFirstSubmitQcDate': '2024-10-03', 'lastUpdatePostDateStruct': {'date': '2025-05-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-10-08', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-06-27', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The American Society of Anesthesiologists physical status (ASA-PS) class', 'timeFrame': 'Day 0', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Performance metrics: accuracy', 'timeFrame': 'day 0', 'description': '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).'}, {'measure': 'Performance metrics: precision', 'timeFrame': 'day 0', 'description': '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)'}, {'measure': 'Performance metrics:recall', 'timeFrame': 'day 0', 'description': '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)'}, {'measure': 'Performance metrics: F1-score', 'timeFrame': 'day 0', 'description': '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).'}, {'measure': 'Performance metrics: Area Under the Receiver Operating Characteristic Curve', 'timeFrame': 'day 0', 'description': "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)."}, {'measure': 'Calibration', 'timeFrame': 'Day 0', 'description': 'Calibration plots will be used to assess the agreement between predictions and the event rate (i.e. correct classification).'}, {'measure': 'Misclassification of the ASA-PS score', 'timeFrame': 'Day 0', 'description': 'A manual review of a selection of misclassifications will be performed by two anesthesiologists to qualitatively assess the cause of the misclassification.'}, {'measure': 'Explainability of the prediction model:Shapley additive explanations (SHAP)', 'timeFrame': 'day 0', 'description': 'Shapley additive explanations (SHAP) if applicable, as it can offer insights into the contribution of each feature to the prediction of individual instances.'}, {'measure': 'Explainability of the prediction model:Local interpretable model-agnostic explanations (LIME)', 'timeFrame': 'day 0', 'description': 'Local interpretable model-agnostic explanations (LIME) can offer insights into the contribution of each feature to the prediction of individual instances.'}, {'measure': 'Optimal sample size', 'timeFrame': 'day 0', 'description': "Analysis of the learning curves to determine if additional data would likely improve the model's performance or if the current dataset is sufficient."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['machine learning', 'ASA-PS', 'Medication', 'Health questionnaire'], 'conditions': ['ASA-PS Classification']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.\n\nGiven 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.\n\nThis 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Underwent a surgical, diagnostic or therapeutic procedure within the surgical suite of the Erasmus MC, and\n* ASA-PS score recorded in electronic medical record (EMR), and\n* A verified medication list in EMR, or a filled out preoperative anesthesiological health questionnaire registered in EMR\n\nExclusion Criteria:\n\n* Age \\<18 at moment of surgery, or\n* ASA-PS V-VI, or\n* Opt-out registered in EMR'}, 'identificationModule': {'nctId': 'NCT06629350', 'briefTitle': 'ASA Prediction Using Health Data and Medication Use', 'organization': {'class': 'OTHER', 'fullName': 'Erasmus Medical Center'}, 'officialTitle': 'ASA Prediction Using Health Data and Medication Use', 'orgStudyIdInfo': {'id': 'MEC-2020-0051/MEC-2024-0181'}}, 'contactsLocationsModule': {'locations': [{'zip': '3015GD', 'city': 'Rotterdam', 'state': 'South Holland', 'country': 'Netherlands', 'facility': 'Erasmus MC', 'geoPoint': {'lat': 51.9225, 'lon': 4.47917}}], 'overallOfficials': [{'name': 'Jan-Wiebe Korstanje, MD MSc PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Erasmus Medical Center'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'CSR'], 'timeFrame': 'to be determined, based on Dutch Law', 'ipdSharing': 'YES', '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.', 'accessCriteria': '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.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Erasmus Medical Center', 'class': 'OTHER'}, 'collaborators': [{'name': 'Health Holland', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'principal investigator', 'investigatorFullName': 'Jan-Wiebe Korstanje', 'investigatorAffiliation': 'Erasmus Medical Center'}}}}