Wiens Matthew O, Kissoon Niranjan, Kumbakumba Elias, Singer Joel, Moschovis Peter P, Ansermino J Mark, Ndamira Andrew, Kiwanuka Julius, Larson Charles P
School of Population and Public Health, University of British Columbia, Vancouver, Canada.
Department of Pediatrics, University of British Columbia, Vancouver, Canada.
Afr Health Sci. 2016 Mar;16(1):162-9. doi: 10.4314/ahs.v16i1.22.
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.
在资源有限的国家，出院后死亡率是儿童死亡的一个常见但却未得到充分认识的因素。识别出院后死亡风险高的儿童是解决这一问题至关重要的第一步。
本项目的目的是确定最有可能与出院后死亡率相关的变量，以便纳入一项预测模型研究。
完成了两轮改进的德尔菲法，用于审查预先选定的变量并选择新变量。根据以下方面评估变量的相关性：（1）预测性；（2）可获得性；（3）成本；（4）测量所需时间。参与者包括各个相关领域的专家。
在第一轮改进的德尔菲法中，23位专家评估了17个变量。另外提出了40个变量，并在第二轮由12位专家进行审查。在第二轮中又评估了16个变量。共整理出30个独特变量用于预测模型研究。
采用了一种系统方法来生成一份最佳候选预测变量清单，以便纳入一项关于资源匮乏地区儿科出院后死亡率预测的研究。
