Section for Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Rama VI Road, Rajathevi, Bangkok 10400, Thailand.
BMC Pediatr. 2014 Feb 28;14:60. doi: 10.1186/1471-2431-14-60.
Injury prediction scores facilitate the development of clinical management protocols to decrease mortality. However, most of the previously developed scores are limited in scope and are non-specific for use in children. We aimed to develop and validate a risk prediction model of death for injured and Traumatised Thai children.
Our cross-sectional study included 43,516 injured children from 34 emergency services. A risk prediction model was derived using a logistic regression analysis that included 15 predictors. Model performance was assessed using the concordance statistic (C-statistic) and the observed per expected (O/E) ratio. Internal validation of the model was performed using a 200-repetition bootstrap analysis.
Death occurred in 1.7% of the injured children (95% confidence interval [95% CI]: 1.57-1.82). Ten predictors (i.e., age, airway intervention, physical injury mechanism, three injured body regions, the Glasgow Coma Scale, and three vital signs) were significantly associated with death. The C-statistic and the O/E ratio were 0.938 (95% CI: 0.929-0.947) and 0.86 (95% CI: 0.70-1.02), respectively. The scoring scheme classified three risk stratifications with respective likelihood ratios of 1.26 (95% CI: 1.25-1.27), 2.45 (95% CI: 2.42-2.52), and 4.72 (95% CI: 4.57-4.88) for low, intermediate, and high risks of death. Internal validation showed good model performance (C-statistic = 0.938, 95% CI: 0.926-0.952) and a small calibration bias of 0.002 (95% CI: 0.0005-0.003).
We developed a simplified Thai pediatric injury death prediction score with satisfactory calibrated and discriminative performance in emergency room settings.
伤害预测评分有助于制定临床管理方案以降低死亡率。然而,以前开发的大多数评分范围有限,并且不适用于儿童。我们旨在开发和验证一种用于受伤和创伤泰国儿童死亡风险的预测模型。
我们的横断面研究包括来自 34 个急救服务的 43516 名受伤儿童。使用逻辑回归分析得出风险预测模型,该模型包含 15 个预测因子。使用一致性统计量(C 统计量)和观察到的预期比(O/E 比)评估模型性能。使用 200 次重复自举分析对模型进行内部验证。
受伤儿童中有 1.7%(95%置信区间 [95%CI]:1.57-1.82)死亡。10 个预测因子(即年龄、气道干预、物理损伤机制、三个受伤身体部位、格拉斯哥昏迷量表和三个生命体征)与死亡显着相关。C 统计量和 O/E 比分别为 0.938(95%CI:0.929-0.947)和 0.86(95%CI:0.70-1.02)。评分方案将三种风险分层分类,相应的似然比分别为 1.26(95%CI:1.25-1.27)、2.45(95%CI:2.42-2.52)和 4.72(95%CI:4.57-4.88),用于低、中、高死亡风险。内部验证显示模型性能良好(C 统计量=0.938,95%CI:0.926-0.952),校准偏差较小为 0.002(95%CI:0.0005-0.003)。
我们开发了一种简化的泰国儿科伤害死亡预测评分,在急诊环境中具有令人满意的校准和区分性能。