Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USA.
Br J Anaesth. 2019 Feb;122(2):215-223. doi: 10.1016/j.bja.2018.10.061. Epub 2018 Dec 19.
The current incidence of major complications in paediatric craniofacial surgery in North America has not been accurately defined. In this report, the Pediatric Craniofacial Collaborative Group evaluates the incidence and determines the independent predictors of major perioperative complications using a multicentre database.
The Pediatric Craniofacial Surgery Perioperative Registry was queried for subjects undergoing complex cranial vault reconstruction surgery over a 5-year period. Major perioperative complications were identified through a structured a priori consensus process. Logistic regression was applied to identify predictors of a major perioperative complication with bootstrapping to evaluate discrimination accuracy and provide internal validity of the multivariable model.
A total of 1814 patients from 33 institutions in the US and Canada were analysed; 15% were reported to have a major perioperative complication. Multivariable predictors included ASA physical status 3 or 4 (P=0.005), craniofacial syndrome (P=0.008), antifibrinolytic administered (P=0.003), blood product transfusion >50 ml kg (P<0.001), and surgery duration over 5 h (P<0.001). Bootstrapping indicated that the predictive algorithm had good internal validity and excellent discrimination and model performance. A perioperative complication was estimated to increase the hospital length of stay by an average of 3 days (P<0.001).
The predictive algorithm can be used as a prognostic tool to risk stratify patients and thereby potentially reduce morbidity and mortality. Craniofacial teams can utilise these predictors of complications to identify high-risk patients. Based on this information, further prospective quality improvement initiatives may decrease complications, and reduce morbidity and mortality.
目前北美小儿颅面外科主要并发症的发生率尚未得到准确界定。在本报告中,小儿颅面协作组利用多中心数据库评估了发生率,并确定了主要围手术期并发症的独立预测因素。
在 5 年期间,通过小儿颅面外科围手术期登记处对接受复杂颅穹窿重建手术的患者进行了查询。主要围手术期并发症通过结构化的先验共识过程确定。应用逻辑回归确定主要围手术期并发症的预测因素,并通过引导法评估多变量模型的判别准确性和内部有效性。
分析了来自美国和加拿大 33 个机构的 1814 名患者;15%的患者报告发生了主要围手术期并发症。多变量预测因素包括 ASA 身体状况 3 或 4 级(P=0.005)、颅面综合征(P=0.008)、使用抗纤维蛋白溶解剂(P=0.003)、输血量>50ml/kg(P<0.001)和手术时间超过 5 小时(P<0.001)。引导法表明,预测算法具有良好的内部有效性和出色的判别力和模型性能。围手术期并发症估计会使平均住院时间延长 3 天(P<0.001)。
该预测算法可用作风险分层患者的预后工具,从而有可能降低发病率和死亡率。颅面团队可以利用这些并发症预测因素来识别高危患者。基于这些信息,进一步的前瞻性质量改进举措可能会减少并发症,降低发病率和死亡率。