Wiens M O, Kumbakumba E, Larson C P, Ansermino J M, Singer J, Kissoon N, Wong H, Ndamira A, Kabakyenga J, Kiwanuka J, Zhou G
School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.
Department of Pediatrics, Mbarara University of Science and Technology, Mbarara, Uganda.
BMJ Open. 2015 Nov 25;5(11):e009449. doi: 10.1136/bmjopen-2015-009449.
To derive a model of paediatric postdischarge mortality following acute infectious illness.
Prospective cohort study.
2 hospitals in South-western Uganda.
1307 children of 6 months to 5 years of age were admitted with a proven or suspected infection. 1242 children were discharged alive and followed up 6 months following discharge. The 6-month follow-up rate was 98.3%.
None.
The primary outcome was postdischarge mortality within 6 months following the initial hospital discharge.
64 children died during admission (5.0%) and 61 died within 6 months of discharge (4.9%). Of those who died following discharge, 31 (51%) occurred within the first 30 days. The final adjusted model for the prediction of postdischarge mortality included the variables mid-upper arm circumference (OR 0.95, 95% CI 0.94 to 0.97, per 1 mm increase), time since last hospitalisation (OR 0.76, 95% CI 0.61 to 0.93, for each increased period of no hospitalisation), oxygen saturation (OR 0.96, 95% CI 0.93 to 0·99, per 1% increase), abnormal Blantyre Coma Scale score (OR 2.39, 95% CI 1·18 to 4.83), and HIV-positive status (OR 2.98, 95% CI 1.36 to 6.53). This model produced a receiver operating characteristic curve with an area under the curve of 0.82. With sensitivity of 80%, our model had a specificity of 66%. Approximately 35% of children would be identified as high risk (11.1% mortality risk) and the remaining would be classified as low risk (1.4% mortality risk), in a similar cohort.
Mortality following discharge is a poorly recognised contributor to child mortality. Identification of at-risk children is critical in developing postdischarge interventions. A simple prediction tool that uses 5 easily collected variables can be used to identify children at high risk of death after discharge. Improved discharge planning and care could be provided for high-risk children.
建立急性感染性疾病后儿童出院后死亡率模型。
前瞻性队列研究。
乌干达西南部的两家医院。
1307名6个月至5岁的儿童因确诊或疑似感染入院。1242名儿童存活出院,并在出院后6个月进行随访。6个月的随访率为98.3%。
无。
主要结局为首次出院后6个月内的出院后死亡率。
64名儿童在住院期间死亡(5.0%),61名儿童在出院后6个月内死亡(4.9%)。在出院后死亡的儿童中,31名(51%)发生在出院后的前30天内。预测出院后死亡率的最终校正模型包括以下变量:上臂中围(每增加1毫米,比值比为0.95,95%置信区间为0.94至0.97)、上次住院后的时间(每增加一个未住院期,比值比为0.76,95%置信区间为0.61至0.93)、血氧饱和度(每增加1%,比值比为0.96,95%置信区间为0.93至0.99)、异常的布兰太尔昏迷量表评分(比值比为2.39,95%置信区间为1.18至4.83)以及HIV阳性状态(比值比为2.98,95%置信区间为1.36至6.53)。该模型生成的受试者工作特征曲线下面积为0.82。在敏感性为80%时,我们的模型特异性为百分之66。在类似队列中,约35%的儿童将被确定为高危(死亡风险为11.1%),其余儿童将被归类为低危(死亡风险为1.4%)。
出院后死亡率是儿童死亡率中一个未得到充分认识的因素。识别高危儿童对于制定出院后干预措施至关重要。一个使用5个易于收集的变量的简单预测工具可用于识别出院后死亡风险高的儿童。可为高危儿童提供更好的出院计划和护理。