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Common data elements for predictors of pediatric sepsis: A framework to standardize data collection
Alishah Mawji1,2, Edmond Li3, Arjun Chandna4,5
1Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada.
Insights
Standardizing pediatric sepsis predictors improves data sharing and algorithm development. This framework aids clinical decisions in resource-limited settings, enhancing sepsis prediction and care.
Area of Science:
- Pediatric critical care
- Epidemiology
- Health informatics
Background:
- Pediatric sepsis prediction requires standardized data collection for research compatibility.
- Resource-limited settings face challenges in collecting reliable sepsis predictor data.
Purpose of the Study:
- To define common data elements for pediatric sepsis predictors at triage.
- To establish a standardized framework for data collection in resource-limited settings.
Main Methods:
- Systematic literature review and guideline examination to identify potential predictors.
- A 5-round modified Delphi process involving voting and discussion to select, standardize, and prioritize predictors.
- Consideration of predictive value, reliability, and resource requirements for data collection.
Main Results:
- 116 common data elements were generated, including standardized prompts, response values, and prioritization tiers.
- Branching logic was incorporated for efficient data collection, supporting mobile applications.
- The data elements are publicly available, with a feedback mechanism for future updates.
Conclusions:
- Routine use of these elements will enable data sharing and the development of robust risk prediction algorithms.
- These algorithms can support clinical decision-making at triage in resource-limited areas.
- Ongoing community feedback is crucial for maintaining the applicability of these elements across diverse settings.
Background:
Standardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings.
Methods:
A preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings.
Results:
We generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information.
Conclusion:
Routine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.
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