Raihana Shahreen, Dunsmuir Dustin, Huda Tanvir, Zhou Guohai, Rahman Qazi Sadeq-Ur, Garde Ainara, Moinuddin Md, Karlen Walter, Dumont Guy A, Kissoon Niranjan, El Arifeen Shams, Larson Charles, Ansermino J Mark
Centre for Child and Adolescent Health, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
Centre for International Child Health, British Columbia Children's Hospital, Vancouver, British Columbia, Canada.
PLoS One. 2015 Nov 18;10(11):e0143213. doi: 10.1371/journal.pone.0143213. eCollection 2015.
The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course.
We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested.
Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42-2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%).
A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice.
要减少数百万死于传染病的儿童死亡人数,需要尽早开始治疗并改善在医疗机构获得护理的机会。一个主要挑战是缺乏客观证据来指导社区一线卫生工作者在病程早期识别儿童的危重病况。
我们对2012年10月至2013年4月期间在一家农村三级护理医院的门诊或急诊科就诊的5岁以下儿童进行了一项前瞻性观察研究。研究医生从医疗机构记录中收集临床体征和症状,并使用移动应用程序记录血氧饱和度、心率和呼吸频率。医疗机构的医生在不知道血氧饱和度的情况下决定是否需要住院。测试了多个逻辑预测模型。
在3374名接受评估的儿童中,25%被收住院,年龄中位数(四分位间距)为1.02(0.42 - 2.24)岁。在他们就诊后,我们无法联系到20%的受试者。一个逻辑回归模型使用连续的血氧饱和度、呼吸频率、体温和年龄,再结合胸凹陷、嗜睡、易激惹以及咳嗽、腹泻和呼吸急促或困难等症状的二分体征,预测住院的受试者工作特征曲线下面积为0.89(95%置信区间 - CI:0.87至0.90)。在住院风险阈值为25%时,敏感性为77%(95% CI:74%至80%),特异性为87%(95% CI:86%至88%),阳性预测值为70%(95% CI:67%至73%),阴性预测值为91%(95% CI:90%至92%)。
一个结合血氧饱和度、呼吸频率和体温以及容易获得的临床体征和症状的模型可以预测危重病童的住院需求。在将该模型应用于临床实践之前,需要在社区环境中进行外部验证。