Department of Pediatrics, CH Douai, Douai, France.
Department of Pediatrics, Children's Hospital, CH Roubaix, Roubaix, France.
Acta Paediatr. 2023 Oct;112(10):2218-2227. doi: 10.1111/apa.16911. Epub 2023 Jul 24.
To develop a model to discriminate non-specific abdominal pain (NSAP) from organic pain in the paediatric emergency department (PED) and evaluate the added value of laboratory markers.
Prospective cohort study in an urban French PED including all patients aged ≥4 years with abdominal pain between November 2020 and May 2021. The outcome was the discrimination between NSAP (patients coded to have only "pain" or "constipation") and organic pain (all other diagnoses) using stepwise backward multivariate logistic regression method with bootstrap resampling.
The study enrolled 246 patients. Overall, 163 patients (66.2%) had NSAP. Four variables associated with organic pain: pain in the epigastric region (OR 0.48 [0.23-0.99]), worsening pain (0.57 [0.32-0.99]), pain migration (0.42 [0.17-0.99]) and vomiting (0.47 [0.26-0.84]) were integrated in a clinical model. To discriminate NSAP with a probability of 65%, model sensitivity was 71.8% (64.9-78.7), specificity was 53.0% (42.3-63.7), and the Net Benefit (NB) was 15.4%. White Blood Count and C-reactive protein results improved discriminative capacity of the model (AUC 0.708 [0.643-0.773] vs. 0.654 [0.585-0.723], p = 0.01) with a supplementary NB of 12%. Patient follow-up showed 95% diagnostic accuracy.
This study reveals a four-clinical predictor model with a NB of 15% in predicting NSAP. Validation studies are necessary.
建立一种模型,以区分儿科急诊(PED)中非特异性腹痛(NSAP)与器质性腹痛,并评估实验室标志物的附加价值。
这是一项在法国城市 PED 进行的前瞻性队列研究,纳入 2020 年 11 月至 2021 年 5 月间所有年龄≥4 岁、腹痛患者。采用逐步后退多元逻辑回归法,结合 bootstrap 重采样,以 NSAP(编码为仅“疼痛”或“便秘”的患者)和器质性腹痛(所有其他诊断)为结局。
研究共纳入 246 例患者,其中 163 例(66.2%)为 NSAP。与器质性腹痛相关的 4 个变量:上腹痛(OR 0.48 [0.23-0.99])、腹痛加重(0.57 [0.32-0.99])、腹痛转移(0.42 [0.17-0.99])和呕吐(0.47 [0.26-0.84]),整合到一个临床模型中。该模型对 NSAP 的预测概率为 65%,敏感度为 71.8%(64.9%-78.7%),特异性为 53.0%(42.3%-63.7%),净获益(NB)为 15.4%。白细胞计数和 C 反应蛋白结果提高了模型的判别能力(AUC 为 0.708 [0.643-0.773] vs. 0.654 [0.585-0.723],p=0.01),并额外增加了 12%的 NB。患者随访显示,该模型的诊断准确率为 95%。
本研究建立了一种包含 4 个临床预测因子的模型,其 NB 为 15%,可预测 NSAP。还需要进一步的验证研究。