National Emergency Laparotomy Audit, Royal College of Anaesthetists, London, UK; Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK.
National Emergency Laparotomy Audit, Royal College of Anaesthetists, London, UK; Division of Surgery and Interventional Science, University College London, London, UK; UCLH Surgical Outcomes Research Centre, Department of Anaesthesia and Perioperative Medicine, University College London Hospitals NHS Foundation Trust, London, UK.
Br J Anaesth. 2018 Oct;121(4):739-748. doi: 10.1016/j.bja.2018.06.026. Epub 2018 Aug 23.
Among patients undergoing emergency laparotomy, 30-day postoperative mortality is around 10-15%. The risk of death among these patients, however, varies greatly because of their clinical characteristics. We developed a risk prediction model for 30-day postoperative mortality to enable better comparison of outcomes between hospitals.
We analysed data from the National Emergency Laparotomy Audit (NELA) on patients having an emergency laparotomy between December 2013 and November 2015. A prediction model was developed using multivariable logistic regression, with potential risk factors identified from existing prediction models, national guidelines, and clinical experts. Continuous risk factors were transformed if necessary to reflect their non-linear relationship with 30-day mortality. The performance of the model was assessed in terms of its calibration and discrimination. Interval validation was conducted using bootstrap resampling.
There were 4458 (11.5%) deaths within 30-days among the 38 830 patients undergoing emergency laparotomy. Variables associated with death included (among others): age, blood pressure, heart rate, physiological variables, malignancy, and ASA physical status classification. The predicted risk of death among patients ranged from 1% to 50%. The model demonstrated excellent calibration and discrimination, with a C-statistic of 0.863 (95% confidence interval, 0.858-0.867). The model retained its high discrimination during internal validation, with a bootstrap derived C-statistic of 0.861.
The NELA risk prediction model for emergency laparotomies discriminates well between low- and high-risk patients and is suitable for producing risk-adjusted provider mortality statistics.
在接受急诊剖腹手术的患者中，术后 30 天死亡率约为 10-15%。然而，由于这些患者的临床特征，其死亡风险差异很大。我们开发了一种 30 天术后死亡率的风险预测模型，以便更好地比较医院之间的结果。
我们分析了 2013 年 12 月至 2015 年 11 月期间进行急诊剖腹手术的国家紧急剖腹手术审计（NELA）的数据。使用多变量逻辑回归开发预测模型，从现有预测模型、国家指南和临床专家确定潜在风险因素。如果需要，连续风险因素会进行转换，以反映其与 30 天死亡率的非线性关系。通过校准和区分来评估模型的性能。使用 bootstrap 重新抽样进行间隔验证。
在接受急诊剖腹手术的 38830 例患者中，术后 30 天内有 4458 例（11.5%）死亡。与死亡相关的变量包括（除其他外）：年龄、血压、心率、生理变量、恶性肿瘤和 ASA 身体状况分类。患者的死亡预测风险范围从 1%到 50%。该模型表现出优异的校准和区分能力，C 统计量为 0.863（95%置信区间，0.858-0.867）。该模型在内部验证期间保留了其高区分能力，bootstrap 衍生的 C 统计量为 0.861。
NELA 急诊剖腹手术风险预测模型能够很好地区分低风险和高风险患者，适合生成风险调整后的提供者死亡率统计数据。
