Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06629350
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-10-01
Brief Title:
ASA Prediction Using Health Data and Medication Use
Sponsor:
None
Organization:
None
Study Overview
Official Title:
ASA Prediction Using Health Data and Medication Use
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None