Nemetchek Brooklyn R, Liang Li Danny, Kissoon Niranjan, Ansermino J Mark, Kabakyenga Jerome, Lavoie Pascal M, Fowler-Kerry Susan, Wiens Matthew O
College of Nursing, University of Saskatchewan, Saskatoon, Canada.
Faculty of Medicine, University of Toronto, Toronto, Canada.
Afr Health Sci. 2018 Dec;18(4):1214-1225. doi: 10.4314/ahs.v18i4.43.
Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs.
To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world.
A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings.
In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables were retained.
A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting.
在每年500万5岁以下儿童死亡病例中，超过三分之二发生在婴儿期，且大多发生在发展中国家，许多是在出院后。然而，基于早期临床预测指标，对于哪些儿童风险更高尚缺乏了解。早期识别出院后有高死亡风险的脆弱婴儿对于制定干预计划很重要。
确定在发展中国家从卫生机构出院后可能死亡的1岁以下婴儿出院后死亡率的潜在预测变量。
进行两轮改良德尔菲法，由一组专家对从系统文献综述中选出的变量进行评估。根据（1）预测价值、（2）测量可靠性、（3）可获得性以及（4）在低资源环境中的适用性对变量进行评估。
第一轮中，18位专家评估了37个候选变量，并提出了另外26个变量。第二轮中，17位专家对第一轮提出的27个变量进行了评估。最终总共保留了55个候选变量。
一种系统方法得出了55个候选预测变量，可用于在低资源环境中设计婴儿出院后死亡率的预测模型。
