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Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol
Matthew O Wiens1, Niranjan Kissoon2, Elias Kumbakumba3
1School of Population and Public Health, University of British Columbia, Vancouver, Canada.
Insights
Identifying high-risk children after hospital discharge is crucial in resource-limited settings. This study identified key variables for predicting pediatric post-discharge mortality, aiding in targeted interventions.
Area of Science:
- Pediatric Health
- Global Health
- Epidemiology
Background:
- Post-discharge mortality significantly contributes to child mortality in resource-limited countries.
- Identifying at-risk children post-discharge is essential for intervention.
Purpose of the Study:
- To determine variables associated with post-discharge mortality.
- To inform a prediction modeling study for pediatric post-discharge mortality.
Main Methods:
- A two-round modified Delphi process involving experts was used.
- Variables were evaluated based on prediction relevance, availability, cost, and measurement time.
- A systematic approach was employed to select candidate predictor variables.
Main Results:
- 23 experts evaluated 17 initial variables in the first round.
- An additional 40 variables were suggested and reviewed in the second round.
- Thirty unique variables were compiled for the prediction modeling study.
Conclusions:
- A systematic Delphi process successfully generated an optimal list of predictor variables.
- This list is intended for a study predicting pediatric post-discharge mortality in resource-poor settings.
Background:
Post-discharge mortality is a frequent but poorly recognized contributor to child mortality in resource limited countries. The identification of children at high risk for post-discharge mortality is a critically important first step in addressing this problem.
Objectives:
The objective of this project was to determine the variables most likely to be associated with post-discharge mortality which are to be included in a prediction modelling study.
Methods:
A two-round modified Delphi process was completed for the review of a priori selected variables and selection of new variables. Variables were evaluated on relevance according to (1) prediction (2) availability (3) cost and (4) time required for measurement. Participants included experts in a variety of relevant fields.
Results:
During the first round of the modified Delphi process, 23 experts evaluated 17 variables. Forty further variables were suggested and were reviewed during the second round by 12 experts. During the second round 16 additional variables were evaluated. Thirty unique variables were compiled for use in the prediction modelling study.
Conclusion:
A systematic approach was utilized to generate an optimal list of candidate predictor variables for the incorporation into a study on prediction of pediatric post-discharge mortality in a resource poor setting.
