American Association for Thoracic Surgery Quality Gateway: A surgeon case study of its application in adult cardiac surgery for quality assurance.

Autor:	Svensson LG; Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio., Blackstone EH; Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio; Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio. Electronic address: blackse@ccf.org., DiPaola L; Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio., Kramer BP; Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio., Ishwaran H; Division of Biostatistics, University of Miami, Miami, Fla.
Jazyk:	angličtina
Zdroj:	The Journal of thoracic and cardiovascular surgery [J Thorac Cardiovasc Surg] 2024 Aug 05. Date of Electronic Publication: 2024 Aug 05.
DOI:	10.1016/j.jtcvs.2024.07.056
Abstrakt:	Objective: To demonstrate the application of American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case Study of quality assurance in adult cardiac surgery. Methods: The case study includes 6989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by a single surgeon between 2001 and 2023. AQG models were used to predict expected probabilities for operative mortality and major morbidity and to compare hospital outcomes, surgery type, risk profile, and individual risk factor levels using virtual (digital) twin causal inference. These models were based on postoperative procedural outcomes after 52,792 cardiac operations performed in 19 hospitals of 3 high-performing hospital systems with overall hospital mortality of 2.0%, analyzed by advanced machine learning for rare events. Results: For individual surgeons, their patients, hospitals, and hospital systems, the Surgeon Case Study demonstrated that AQG provides expected outcomes across the entire spectrum of cardiac surgery, from single-component primary operations to complex multicomponent reoperations. Actionable opportunities for quality improvement based on virtual twins are illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals. Conclusions: Using minimal data collection and models developed using advanced machine learning, this case study shows that probabilities can be generated for operative mortality and major morbidity after virtually all adult cardiac operations. It demonstrates the utility of 21st century causal inference (virtual [digital] twin) tools for assessing quality for surgeons asking "how am I doing?," their patients asking "what are my chances?," and the profession asking "how can we get better?" Competing Interests: Conflict of Interest Statement Drs Blackstone, Ishwaran, and Ms DiPaola are involved in developing the AATS Quality Gateway. All other authors reported no conflicts of interest. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest. (Copyright © 2024 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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