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Zhang, Xiaohan Tanner

https://orcid.org/0000-0002-4843-9077

Mayo Clinic

Johns Hopkins University

Citations 2

Guiding Principles for Data Sharing and Harmonization: Results of a Systematic Review and Modified Delphi From the Society of Critical Care Medicine Data Science Campaign
Armaignac, Donna Lee , Heavner, Smith F. , Rausen, Michelle , Zhang, Xiaohan Tanner , Al-Hakim, Tamara, Strekalova, Yulia Levites , Shah, Neel , Remy, Kenneth E. , Manion, Sean T. , Scruth, Elizabeth , Rincon, Teresa , Park, Soojin , Evans, Laura , Ozrazgat‐Baslanti, Tezcan , Murphy, David J. , Engel, Heidi J., Cobb, J. Perren , Clermont, Gilles , Park, Soojin , Laudański, Krzysztof , Haendel, Melissa , Kramer, Andrew A. , Herasevich, Vitaly , Sikora, Andrea , Khanna, Ashish K. , Zimmerman, Jerry J. , Reuter‐Rice, Karin

Critical Care Medicine | February 21, 2025 | Volume: 53 | Issue: 3 | Pages: e619-e631

10.1097/ccm.0000000000006578 Get It

OBJECTIVES: This study aimed to establish a set of guiding principles for data sharing and harmonization in critical care, focusing on the use of real-world data (RWD) and real-world evidence (RWE) to improve patient outcomes and research efficacy. The principles were developed through a systematic literature review and a modified Delphi process, with the goal of enhancing data accessibility, standardization, and interoperability across critical care settings. DATA SOURCES: Data sources included a comprehensive search of peer-reviewed literature, specifically studies related to the use of RWD and RWE in healthcare, guidelines, best practices, and recommendations on data sharing and harmonization. A total of 8150 articles were initially identified through databases such as MEDLINE and Web of Science, with 257 studies meeting inclusion criteria. STUDY SELECTION: Inclusion criteria focused on publications discussing health-related informatics, recommendations for RWD/RWE usage, data sharing, and harmonization principles. Exclusion criteria ruled out non-human studies, case studies, conference abstracts, and articles published before 2013, as well as those not available in English. DATA EXTRACTION: From the 257 selected studies, 322 statements were extracted. After removing irrelevant definitions and off-topic content, 232 statements underwent content validation and thematic analysis. These statements were then consolidated into 24 candidate guiding principles after rigorous review and consensus-building among the expert panel. DATA SYNTHESIS: A three-phase modified Delphi process was employed, involving a conceptualization group, a review group, and a Delphi group. In phase 1, experts identified key themes and search terms for the systematic review. Phase 2 involved validating and refining the prospective guiding principles, while phase 3 employed a Delphi panel to rate principles on acceptability, importance, and feasibility. This process resulted in 24 guiding principles, with high consensus achieved in rounds 2 and 3 on their relevance and applicability. CONCLUSIONS: The systematic review and Delphi process resulted in 24 guiding principles to improve data sharing and harmonization in critical care. These principles address challenges across the data lifecycle, including generation, storage, access, and usage of RWD and RWE. This framework is designed to promote more effective and equitable data practices, with relevance for the development of artificial intelligence-based decision support tools and clinical research. The principles are intended to guide the responsible use of data science in critical care, with emphasis on ethics and equity, while acknowledging the variability of resources across settings.
Development of a Core Critical Care Data Dictionary With Common Data Elements to Characterize Critical Illness and Injuries Using a Modified Delphi Method
Anderson, Wesley R., Heavner, Smith H., Al-Hakim, Tamara, Cruz‐Cano, Raul , Engel, Heidi J., Lamb, Keith, MacLaren, Robert , Rincon, Teresa , Sikora, Andrea, Tanner, Donna, Teeter, William , Wong, An-Kwok Ian , Zhang, Xiaohan Tanner , Zimmerman, Jerry J. , Kumar, Vishakha K. , Cobb, J. Perren, Laudański, Krzysztof , Murphy, David J., Anderson, Wesley, Heavner, Smith H., Kamaleswaran, Rishikesan , Badawi, Omar , Grunwell, Jocelyn R. , Herasevich, Vitaly , Khanna, Ashish K. , Sanchez-Pinto, Lazaro Nelson , Sikora, Andrea , Stevens, Robert David , Wynn, James Lawrence , Reuter‐Rice, Karin

Critical Care Medicine | February 21, 2025 | Volume: 53 | Issue: 5 | Pages: e1045-e1054

10.1097/ccm.0000000000006595 Get It

OBJECTIVES: To develop the first core Critical Care Data Dictionary (C2D2) with common data elements (CDEs) to characterize critical illness and injuries. DESIGN: Group consensus process using modified Delphi approach. SETTING: Electronic surveys and in-person meetings. SUBJECTS: A multidisciplinary workgroup of clinicians and researchers with expertise in the care of the critically ill and injured. INTERVENTIONS: The Delphi process was divided into domain and CDE portions with each composed of two item generation rounds and one item reduction/refinement rounds. Two in-person meetings augmented this process to facilitate review and consideration of the domains and by panel members. The final set of domains and CDEs was then reviewed by the group to meet the competing criteria of utility and feasibility, resulting in the core dataset. MEASUREMENTS AND MAIN RESULTS: The 23-member Delphi panel was provided 1833 candidate variables for potential dataset inclusion. The final dataset includes 226 patient-level CDCs in nine domains, which include anthropometrics and demographics (8), chronic comorbid illnesses (18), advanced directives (1), ICU diagnoses (61), diagnostic tests (42), interventions (27), medications (38), objective assessments (26), and hospital course and outcomes (5). Upon final review, 91% of the panel endorsed the CDCs as meeting criteria for a minimum viable data dictionary. Data elements cross the lifespan of neonate through adult patients. CONCLUSIONS: The resulting C2D2 provides a foundation to facilitate rapid collection, analyses, and dissemination of information necessary for research, quality improvement, and clinical practice to optimize critical care outcomes. Further work is needed to validate the effectiveness of the dataset in a variety of critical care settings.