Empirically Derived Dehydration Scoring and Decision Tree Models for Children With Diarrhea: Assessment and Internal Validation in a Prospective Cohort Study in Dhaka, Bangladesh.
Empirically Derived Dehydration Scoring and Decision Tree Models for Children With Diarrhea: Assessment and Internal Validation in a Prospective Cohort Study in Dhaka, Bangladesh.
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DOI：
10.9745/ghsp-d-15-00097
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发表时间：
2015-08-18
期刊：
影响因子：
--
通讯作者：
Alam NH
中科院分区：
文献类型：
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作者：
Levine AC;Glavis-Bloom J;Modi P;Nasrin S;Rege S;Chu C;Schmid CH;Alam NH
达卡脱水评分和达卡脱水树是第一个基于经验推导和内部验证的诊断模型，用于评估急性腹泻儿童的脱水情况，供全科护士在资源有限的环境中使用。一线提供者可以使用这些新工具来更好地分类和管理儿童脱水。达卡脱水评分和达卡脱水树是第一个基于经验推导和内部验证的诊断模型，用于评估急性腹泻儿童的脱水情况，供全科护士在资源有限的环境中使用。一线提供者可以使用这些新工具来更好地分类和管理儿童脱水。腹泻仍然是影响全世界儿童的最常见和最致命的疾病之一。准确评估脱水状态对于确定治疗过程至关重要，但没有经验性地推导出脱水的临床诊断模型并验证其在资源有限的环境中使用。在脱水中：准确评估儿童（达卡）前瞻性队列研究，2014年2月至6月在孟加拉国随机抽取5岁以下急性腹泻儿童样本。当地护士在到达时评估儿童脱水的临床体征，然后在受试者再水合时获得系列体重。对于每个儿童，使用补液后的体重变化百分比将受试者分为严重脱水（>9%体重变化），部分脱水（3-9%）或无脱水（<3%）。然后将临床变量输入逻辑回归和递归划分模型，以分别开发达卡脱水评分和达卡脱水树。使用受试者工作特征曲线下面积（AUC）评估模型的准确性，并通过重复临床检查评估其可靠性。Bootstrapping用于内部验证模型。共入组850名儿童，最终分析纳入771名儿童。在纳入分析的771名儿童中，11%被归类为严重脱水，45%有一些脱水，44%没有脱水。对于重度脱水的诊断，达卡脱水评分和达卡脱水树的显著AUC分别为0.79（95% CI = 0.74，0.84）和0.76（95% CI =0.71，0.80）。 此外，对于重度脱水的诊断，达卡脱水评分和达卡脱水树的显著阳性似然比分别为2.0（95% CI = 1.8，2.3）和2.5（95% CI = 2.1，2.8），显著阴性似然比分别为0.23（95% CI = 0.13，0.40）和0.28（95% CI = 0.18，0.44）。 这两个模型都证明了独立评分者之间90%的一致性和良好的再现性，使用自举。这项研究是第一个在资源有限的环境中经验性地推导和内部验证准确可靠的脱水临床诊断模型的研究。经过外部验证后，一线提供者可以使用这些新工具来更好地管理儿童急性腹泻。
The DHAKA Dehydration Score and the DHAKA Dehydration Tree are the first empirically derived and internally validated diagnostic models for assessing dehydration in children with acute diarrhea for use by general practice nurses in a resource-limited setting. Frontline providers can use these new tools to better classify and manage dehydration in children. The DHAKA Dehydration Score and the DHAKA Dehydration Tree are the first empirically derived and internally validated diagnostic models for assessing dehydration in children with acute diarrhea for use by general practice nurses in a resource-limited setting. Frontline providers can use these new tools to better classify and manage dehydration in children. Diarrhea remains one of the most common and most deadly conditions affecting children worldwide. Accurately assessing dehydration status is critical to determining treatment course, yet no clinical diagnostic models for dehydration have been empirically derived and validated for use in resource-limited settings. In the Dehydration: Assessing Kids Accurately (DHAKA) prospective cohort study, a random sample of children under 5 with acute diarrhea was enrolled between February and June 2014 in Bangladesh. Local nurses assessed children for clinical signs of dehydration on arrival, and then serial weights were obtained as subjects were rehydrated. For each child, the percent weight change with rehydration was used to classify subjects with severe dehydration (>9% weight change), some dehydration (3–9%), or no dehydration (<3%). Clinical variables were then entered into logistic regression and recursive partitioning models to develop the DHAKA Dehydration Score and DHAKA Dehydration Tree, respectively. Models were assessed for their accuracy using the area under their receiver operating characteristic curve (AUC) and for their reliability through repeat clinical exams. Bootstrapping was used to internally validate the models. A total of 850 children were enrolled, with 771 included in the final analysis. Of the 771 children included in the analysis, 11% were classified with severe dehydration, 45% with some dehydration, and 44% with no dehydration. Both the DHAKA Dehydration Score and DHAKA Dehydration Tree had significant AUCs of 0.79 (95% CI = 0.74, 0.84) and 0.76 (95% CI = 0.71, 0.80), respectively, for the diagnosis of severe dehydration. Additionally, the DHAKA Dehydration Score and DHAKA Dehydration Tree had significant positive likelihood ratios of 2.0 (95% CI = 1.8, 2.3) and 2.5 (95% CI = 2.1, 2.8), respectively, and significant negative likelihood ratios of 0.23 (95% CI = 0.13, 0.40) and 0.28 (95% CI = 0.18, 0.44), respectively, for the diagnosis of severe dehydration. Both models demonstrated 90% agreement between independent raters and good reproducibility using bootstrapping. This study is the first to empirically derive and internally validate accurate and reliable clinical diagnostic models for dehydration in a resource-limited setting. After external validation, frontline providers may use these new tools to better manage acute diarrhea in children.