Roukema Jolt, van Loenhout Rhiannon B, Steyerberg Ewout W, Moons Karel G M, Bleeker Sacha E, Moll Henriëtte A
Department of Pediatrics, Sophia Children's Hospital, Erasmus MC, P.O. Box 2060, 3000 CB Rotterdam, The Netherlands.
J Clin Epidemiol. 2008 Feb;61(2):135-41. doi: 10.1016/j.jclinepi.2007.07.005.
To compare polytomous and dichotomous logistic regression analyses in diagnosing serious bacterial infections (SBIs) in children with fever without apparent source (FWS).
We analyzed data of 595 children aged 1-36 months, who attended the emergency department with fever without source. Outcome categories were SBI, subdivided in pneumonia and other-SBI (OSBI), and non-SBI. Potential predictors were selected based on previous studies and literature. Four models were developed: a polytomous model, estimating probabilities for three diagnostic categories simultaneously; two sequential dichotomous models, which differed in variable selection, discriminating SBI and non-SBI in step 1, and pneumonia and OSBI in step 2; and model 4, where each outcome category was opposed to the other two. The models were compared with respect to the area under the receiver-operating characteristic curve (AUC) for each of the three outcome categories and to the variable selection.
Small differences were found in the variables that were selected in the polytomous and dichotomous models. The AUCs of the three outcome categories were similar for each modeling strategy.
A polytomous logistic regression analysis did not outperform sequential and single application of dichotomous logistic regression analyses in diagnosing SBIs in children with FWS.
比较多分类逻辑回归分析和二分类逻辑回归分析在诊断无明显病因发热（FWS）儿童严重细菌感染（SBI）中的应用。
我们分析了595名1至36个月大因无明显病因发热而到急诊科就诊儿童的数据。结局分类为SBI，细分为肺炎和其他SBI（OSBI）以及非SBI。根据先前的研究和文献选择潜在预测因素。构建了四个模型：一个多分类模型，同时估计三种诊断类别的概率；两个序贯二分类模型，在变量选择上有所不同，第一步区分SBI和非SBI，第二步区分肺炎和OSBI；模型4，其中每个结局类别与其他两个类别相对。比较了这三个结局类别各自的受试者工作特征曲线下面积（AUC）以及变量选择情况。
多分类模型和二分类模型选择的变量存在细微差异。每种建模策略下三个结局类别的AUC相似。
在诊断FWS儿童的SBI方面，多分类逻辑回归分析并不优于序贯和单次应用的二分类逻辑回归分析。
