Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique.
Hospital Clínic de Barcelona, Barcelona Institute for Global Health and Universitat de Barcelona, Barcelona, Spain.
Pediatrics. 2019 Jan;143(1). doi: 10.1542/peds.2018-0606. Epub 2018 Dec 14.
Although the burden of postdischarge mortality (PDM) in low-income settings appears to be significant, no clear recommendations have been proposed in relation to follow-up care after hospitalization. We aimed to determine the burden of pediatric PDM and develop predictive models to identify children who are at risk for dying after discharge.
Deaths after hospital discharge among children aged <15 years in the last 17 years were reviewed in an area under demographic and morbidity surveillance in Southern Mozambique. We determined PDM over time (up to 90 days) and derived predictive models of PDM using easily collected variables on admission.
Overall PDM was high (3.6%), with half of the deaths occurring in the first 30 days. One primary predictive model for all ages included young age, moderate or severe malnutrition, a history of diarrhea, clinical pneumonia symptoms, prostration, bacteremia, having a positive HIV status, the rainy season, and transfer or absconding, with an area under the curve of 0.79 (0.75-0.82) at day 90 after discharge. Alternative models for all ages including simplified clinical predictors had a similar performance. A model specific to infants <3 months old was used to identify as predictors being a neonate, having a low weight-for-age score, having breathing difficulties, having hypothermia or fever, having oral candidiasis, and having a history of absconding or transfer to another hospital, with an area under the curve of 0.76 (0.72-0.91) at day 90 of follow-up.
Death after discharge is an important although poorly recognized contributor to child mortality. A simple predictive algorithm based on easily recognizable variables could readily be used to identify most infants and children who are at a high risk of dying after discharge.
尽管在低收入环境中，出院后死亡率（PDM）的负担似乎很大，但尚未针对住院后随访护理提出明确建议。我们旨在确定儿科 PDM 的负担，并开发预测模型以识别出院后有死亡风险的儿童。
在莫桑比克南部的人口和发病监测地区，回顾了过去 17 年中<15 岁儿童出院后的死亡情况。我们确定了随时间推移（最长 90 天）的 PDM，并使用入院时可收集的变量来推导 PDM 的预测模型。
总体 PDM 很高（3.6%），其中一半的死亡发生在出院后的前 30 天。所有年龄段的一个主要预测模型包括年龄较小、中重度营养不良、腹泻史、临床肺炎症状、虚脱、菌血症、HIV 阳性、雨季以及转院或逃跑，出院后第 90 天的曲线下面积为 0.79（0.75-0.82）。适用于所有年龄段的简化临床预测因子的替代模型具有类似的性能。针对<3 个月大的婴儿的特定模型用于识别以下预测因子：新生儿、体重不足评分低、呼吸困难、体温过低或发热、口腔念珠菌病以及逃跑或转院史，出院后第 90 天的曲线下面积为 0.76（0.72-0.91）。
出院后死亡是儿童死亡的一个重要但未被充分认识的原因。一种基于易于识别的变量的简单预测算法，可以很容易地用于识别大多数有高出院后死亡风险的婴儿和儿童。
