BACKGROUND Several prediction models have been reported to identify patients with radiographic pneumonia, but none have been validated or broadly implemented into practice. We evaluated 5 prediction models for radiographic pneumonia in children. METHODS We evaluated 5 previously published prediction models for radiographic pneumonia (Neuman, Oostenbrink, Lynch, Mahabee-Gittens, and Lipsett) using data from a single-center prospective study of patients 3 months to 18 years with signs of lower respiratory tract infection. Our outcome was radiographic pneumonia. We compared each model's area under the receiver operating characteristic curve (AUROC) and evaluated their diagnostic accuracy at statistically-derived cutpoints. RESULTS Radiographic pneumonia was identified in 253 (22.2%) of 1142 patients. When using model coefficients derived from the study dataset, AUROC ranged from 0.58 (95% confidence interval, 0.52-0.64) to 0.79 (95% confidence interval, 0.75-0.82). When using coefficients derived from original study models, 2 studies demonstrated an AUROC >0.70 (Neuman and Lipsett); this increased to 3 after deriving regression coefficients from the study cohort (Neuman, Lipsett, and Oostenbrink). Two models required historical and clinical data (Neuman and Lipsett), and the third additionally required C-reactive protein (Oostenbrink). At a statistically derived cutpoint of predicted risk from each model, sensitivity ranged from 51.2% to 70.4%, specificity 49.9% to 87.5%, positive predictive value 16.1% to 54.4%, and negative predictive value 83.9% to 90.7%. CONCLUSIONS Prediction models for radiographic pneumonia had varying performance. The 3 models with higher performance may facilitate clinical management by predicting the risk of radiographic pneumonia among children with lower respiratory tract infection.

中文翻译：

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急诊科儿童肺炎预测模型的验证。


背景技术据报道，有几种预测模型可以识别放射学肺炎患者，但没有一个模型得到验证或广泛应用于实践。我们评估了 5 个儿童放射学肺炎预测模型。方法 我们使用来自 3 个月至 18 岁有下呼吸道感染迹象患者的单中心前瞻性研究的数据，评估了 5 个先前发表的放射学肺炎预测模型（Neuman、Oostenbrink、Lynch、Mahabee-Gittens 和 Lipsett）。我们的结果是放射学肺炎。我们比较了每个模型的受试者工作特征曲线 (AUROC) 下的面积，并在统计得出的切点上评估了它们的诊断准确性。结果 1142 名患者中有 253 名 (22.2%) 被确诊为放射学肺炎。当使用从研究数据集导出的模型系数时，AUROC 的范围为 0.58（95% 置信区间，0.52-0.64）到 0.79（95% 置信区间，0.75-0.82）。当使用源自原始研究模型的系数时，2 项研究表明 AUROC >0.70（Neuman 和 Lipsett）；从研究队列（Neuman、Lipsett 和 Oostenbrink）推导出回归系数后，这一数字增加到 3。两个模型需要历史和临床数据（Neuman 和 Lipsett），第三个模型还需要 C 反应蛋白（Oostenbrink）。在每个模型预测风险的统计得出的切点处，敏感性范围为 51.2% 至 70.4%，特异性范围为 49.9% 至 87.5%，阳性预测值 16.1% 至 54.4%，阴性预测值 83.9% 至 90.7%。结论 放射学肺炎的预测模型具有不同的性能。 性能较高的 3 个模型可以通过预测下呼吸道感染儿童放射学肺炎的风险来促进临床管理。