Data_Sheet_1_Integrating Clinical Signs at Presentation and Clinician's Non-analytical Reasoning in Prediction Models for Serious Bacterial Infection in Febrile Children Presenting to Emergency Department.PDF

ObjectiveDevelopment and validation of clinical prediction model (CPM) for serious bacterial infections (SBIs) in children presenting to the emergency department (ED) with febrile illness, based on clinical variables, clinician's “gut feeling,” and “sense of reassurance.Materials and MethodsFebrile children presenting to the ED of Children's Clinical University Hospital (CCUH) between April 1, 2017 and December 31, 2018 were enrolled in a prospective observational study. Data on clinical signs and symptoms at presentation, together with clinician's “gut feeling” of something wrong and “sense of reassurance” were collected as candidate variables for CPM. Variable selection for the CPM was performed using stepwise logistic regression (forward, backward, and bidirectional); Akaike information criterion was used to limit the number of parameters and simplify the model. Bootstrapping was applied for internal validation. For external validation, the model was tested in a separate dataset of patients presenting to six regional hospitals between January 1 and March 31, 2019.ResultsThe derivation cohort consisted of 517; 54% (n = 279) were boys, and the median age was 58 months. SBI was diagnosed in 26.7% (n = 138). Validation cohort included 188 patients; the median age was 28 months, and 26.6% (n = 50) developed SBI. Two CPMs were created, namely, CPM1 consisting of six clinical variables and CPM2 with four clinical variables plus “gut feeling” and “sense of reassurance.” The area under the curve (AUC) for receiver operating characteristics (ROC) curve of CPM1 was 0.744 (95% CI, 0.683–0.805) in the derivation cohort and 0.692 (95% CI, 0.604–0.780) in the validation cohort. AUC for CPM2 was 0.783 (0.727–0.839) and 0.752 (0.674–0.830) in derivation and validation cohorts, respectively. AUC of CPM2 in validation population was significantly higher than that of CPM1 [p = 0.037, 95% CI (−0.129; −0.004)]. A clinical evaluation score was derived from CPM2 to stratify patients in “low risk,” “gray area,” and “high risk” for SBI.ConclusionBoth CPMs had moderate ability to predict SBI and acceptable performance in the validation cohort. Adding variables “gut feeling” and “sense of reassurance” in CPM2 improved its ability to predict SBI. More validation studies are needed for the assessment of applicability to all febrile patients presenting to ED.

旨在开发与验证针对儿童急诊发热疾病患者严重细菌感染（SBIs）的临床预测模型（CPM），该模型基于临床变量、医师的直觉判断以及安心感。研究材料与方法：纳入2017年4月1日至2018年12月31日期间就诊于儿童临床大学医院（CCUH）的发热儿童，进行前瞻性观察研究。收集患者就诊时的临床体征和症状，以及医师对病情的直觉判断和安心感，作为CPM的候选变量。采用逐步逻辑回归（正向、反向和双向）进行CPM的变量选择；使用赤池信息准则限制参数数量并简化模型。采用自助法进行内部验证。对于外部验证，该模型在2019年1月1日至3月31日期间六个区域医院的独立患者数据集中进行测试。结果：构建队列包括517名患者；其中54%（n = 279）为男性，中位年龄为58个月。26.7%（n = 138）的患者被诊断为SBIs。验证队列包括188名患者；中位年龄为28个月，其中26.6%（n = 50）的患者发生了SBIs。创建了两个CPM，即CPM1，包含六个临床变量，以及CPM2，包含四个临床变量加上“直觉判断”和“安心感”。在构建队列中，CPM1的接受者操作特征（ROC）曲线下面积（AUC）为0.744（95% CI，0.683–0.805），在验证队列中为0.692（95% CI，0.604–0.780）。CPM2的AUC在构建和验证队列中分别为0.783（0.727–0.839）和0.752（0.674–0.830）。在验证人群中，CPM2的AUC显著高于CPM1 [p = 0.037，95% CI（−0.129；−0.004）]。从CPM2中衍生出一个临床评估分数，以将患者分层为“低风险”、“灰色区域”和“高风险”SBIs组。结论：两个CPM均具有中等的预测SBIs的能力，在验证队列中表现可接受。在CPM2中添加“直觉判断”和“安心感”变量提高了其预测SBIs的能力。需要更多的验证研究来评估其适用于所有急诊发热患者的适用性。