Related Experiment Video
Updated: Mar 8, 2026

Anesthesia and Intubation of Preadolescent Mouse Pups for Cardiothoracic Surgery
Published on: June 2, 2022
Anesthesia and Databases: Pediatric Cardiac Disease as a Role Model
David F Vener1, Sara K Pasquali, Emad B Mossad
1From the *Departments of Anesthesiology and Pediatrics, Division of Pediatric Cardiovascular Anesthesia, Baylor College of Medicine, Houston, Texas; and †Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, C.S. Mott Children's Hospital, Ann Arbor, Michigan.
Insights
Large datasets in clinical medicine, especially for congenital heart disease, enable multi-center collaboration. Merging administrative and clinical data improves understanding of care patterns and outcomes, driving quality improvement initiatives.
Area of Science:
- Clinical Medicine
- Health Informatics
- Cardiovascular Research
Background:
- Large datasets are increasingly vital in clinical medicine, particularly for rare conditions like congenital heart disease.
- Data sources include administrative (hospital admissions, charges) and clinical (disease-specific) information.
- Over a dozen registries exist for congenital cardiac diseases, focusing on various patient care aspects.
Purpose of the Study:
- To explore the utility of merging administrative and clinical data for analyzing care patterns, outcomes, and costs in congenital heart disease.
- To highlight the potential of multi-institutional data collaboration for quality improvement (QI).
- To address the challenges and considerations in using large datasets, including protected health information (PHI) and nomenclature.
Main Methods:
- Utilizing probabilistic statistical matching to integrate disparate administrative and clinical datasets post hoc.
- Leveraging common data elements for data linkage and analysis.
- Adhering to federal guidelines for handling protected health information (PHI).
Main Results:
- Probabilistic matching allows for the combination of administrative and clinical data to reveal insights into care patterns, outcomes, and costs.
- Collaborative data use between institutions can effectively drive quality improvement (QI) initiatives.
- Standardized nomenclature and risk adjustment are crucial for accurate analysis of large datasets.
Conclusions:
- Merging administrative and clinical data offers valuable insights into patient care and outcomes, especially in complex conditions like congenital heart disease.
- Collaborative learning using large datasets can mitigate variability in mortality, morbidity, and costs across institutions.
- Accurate outcomes measurement is fundamental for hospital-based QI and requires appropriate oversight.
Abstract:
Large data sets have now become ubiquitous in clinical medicine; they are particularly useful in high-acuity, low-volume conditions such as congenital heart disease where data must be collected from many centers. These data fall into 2 categories: administrative data arising from hospital admissions and charges and clinical data relating to specific diseases or procedures. In congenital cardiac diseases, there are now over a dozen of these data sets or registries focusing on various elements of patient care. Using probabilistic statistic matching, it is possible to marry administrative and clinical data post hoc using common elements to determine valuable information about care patterns, outcomes, and costs. These data sets can also be used in a collaborative fashion between institutions to drive quality improvement (QI). Because these data may include protected health information (PHI), care must be taken to adhere to federal guidelines on their use. A fundamental principle of large data management is the use of a common language and definition (nomenclature) to be effective. In addition, research derived from these information sources must be appropriately balanced to ensure that risk adjustments for preoperative and surgical factors are taken into consideration during the analysis. Care of patients with cardiac disease both in the United States and abroad consistently shows wide variability in mortality, morbidity, and costs, and there has been a tremendous amount of discussion about the benefits of regionalization of care based on center volume and outcome measurements. In the absence of regionalization, collaborative learning techniques have consistently been shown to minimize this variability and improve care at all centers, but before changes can be made it is necessary to accurately measure accurately current patient outcomes. Outcomes measurement generally falls under hospital-based QI initiatives, but more detailed analysis and research require Institutional Review Board and administrative oversight. Cardiac anesthesia providers for these patients have partnered with the Society of Thoracic Surgeons Congenital Heart surgeons to include anesthesia elements to help in this process.
Related Concept Videos
Parenteral Anesthetics: Overview
General Anesthesia: Overview
General anesthesia induces unconsciousness in the whole body, while the others target specific areas or sensations. It is administered to minimize adverse effects, maintain...
Cardiomyopathy VII: Pre and Post Operative Nursing Management
Local Anesthetics: Clinical Application as Epidural Anesthesia
Since epidural anesthetics can be infused through an epidural catheter, all types of drugs, including short-acting ones, can be administered. Chloroprocaine and lidocaine are examples of short and long-duration anesthetics, respectively. Bupivacaine...
Inhalational Anesthetics: Overview
Local Anesthetics: Clinical Application as Spinal Anesthesia

