Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project.

Matthew O. Wiens,Elias Kumbakumba,Andrew Ndamira,Peter P. Moschovis,Joel Singer,J. Mark Ansermino,Niranjan Kissoon,Charles P. Larson,Julius Kiwanuka

doi:10.4314/ahs.v16i1.22

Abstract

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. 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. 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. 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. 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.

Highlights

Acute infectious diseases account for most childhood deaths in resource limited countries, on the African continent.[1]
Sepsis, according to the Pediatric Consensus Conference definition, is the systemic response to any infectious disease which can progress from a mild inflammatory derangement to multi-organ failure, shock and death[2], and can be broadly considered to be the primary cause of acute infectious disease death.[3]
A summary of each round is reviewed by the experts providing opportunity to modify previously selected answers, converging, in theory, towards the most correct answer. This method has been used successfully in prediction of modelling research as a means to generate a comprehensive set of candidate predictor variables to be used in statistical modeling.[8]

Summary

Introduction

Acute infectious diseases account for most childhood deaths in resource limited countries, on the African continent.[1]. A summary of each round is reviewed by the experts providing opportunity to modify previously selected answers, converging, in theory, towards the most correct answer. This method has been used successfully in prediction of modelling research as a means to generate a comprehensive set of candidate predictor variables to be used in statistical modeling.[8]. 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. Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project.

Objectives

Methods

Conclusion