From the Department of General Surgery (S.K., K.P., G.-A.K., V.M., A.K., M.P., E.C., K.L.), University Hospital of Heraklion, University of Crete, School of Medicine; Laboratory of Biostatistics, University of Crete, School of Medicine (E.I.K.); Department of Surgical Oncology, University Hospital of Heraklion, University of Crete, School of Medicine (C.S.A., O.Z.), Heraklion; Department of Surgery, University General Hospital of Patras, School of Medicine (N.D., I.K., D.K.), University of Patras, Patras, Greece; Department of Surgery, General Hospital of Nicosia, School of Medicine (N.G., G.K., I.P., P.P., K.F.), University of Cyprus, Nicosia, Cyprus; First Department of Surgery (D.S., A.S.) and Second Propaedeutic Department of Surgery (I.M.P.), Laikon General Hospital, National and Kapodistrian University of Athens; Department of Surgery, University General Hospital Attikon, School of Medicine (K.N., M.P., N.V.M., I.M.), University of Athens, Athens; Department of Surgery (E.L., G.D.), General Hospital of Volos, Volos, Greece; Department of Surgery (D.P., V.N.), General Hospital of Trikala, Trikala; Department of Surgery (G.K.G., G.P.-G., K.T.), University Hospital of Ioannina, Ioannina, Greece; Department of Surgery, Ippokrateion General Hospital of Thessaloniki, School of Medicine (G.Z., S.T., I.P.), Aristotle University of Thessaloniki, Thessaloniki; Second Department of Surgery (G.S., G.G.), Evangelismos General Hospital, Athens; and Department of Surgery, University General Hospital of Alexandroupolis, School of Medicine (M.K., K.K., M.M.), University of Thrace, Alexandroupolis, Greece.
J Trauma Acute Care Surg. 2023 Jun 1;94(6):847-856. doi: 10.1097/TA.0000000000003904. Epub 2023 Feb 2.
Accurate preoperative risk assessment in emergency laparotomy (EL) is valuable for informed decision making and rational use of resources. Available risk prediction tools have not been validated adequately across diverse health care settings. Herein, we report a comparative external validation of four widely cited prognostic models.
A multicenter cohort was prospectively composed of consecutive patients undergoing EL in 11 Greek hospitals from January 2020 to May 2021 using the National Emergency Laparotomy Audit (NELA) inclusion criteria. Thirty-day mortality risk predictions were calculated using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), NELA, Portsmouth Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (P-POSSUM), and Predictive Optimal Trees in Emergency Surgery Risk tools. Surgeons' assessment of postoperative mortality using predefined cutoffs was recorded, and a surgeon-adjusted ACS-NSQIP prediction was calculated when the original model's prediction was relatively low. Predictive performances were compared using scaled Brier scores, discrimination and calibration measures and plots, and decision curve analysis. Heterogeneity across hospitals was assessed by random-effects meta-analysis.
A total of 631 patients were included, and 30-day mortality was 16.3%. The ACS-NSQIP and its surgeon-adjusted version had the highest scaled Brier scores. All models presented high discriminative ability, with concordance statistics ranging from 0.79 for P-POSSUM to 0.85 for NELA. However, except the surgeon-adjusted ACS-NSQIP (Hosmer-Lemeshow test, p = 0.742), all other models were poorly calibrated ( p < 0.001). Decision curve analysis revealed superior clinical utility of the ACS-NSQIP. Following recalibrations, predictive accuracy improved for all models, but ACS-NSQIP retained the lead. Between-hospital heterogeneity was minimum for the ACS-NSQIP model and maximum for P-POSSUM.
The ACS-NSQIP tool was most accurate for mortality predictions after EL in a broad external validation cohort, demonstrating utility for facilitating preoperative risk management in the Greek health care system. Subjective surgeon assessments of patient prognosis may optimize ACS-NSQIP predictions.
Diagnostic Test/Criteria; Level II.
在急诊剖腹手术（EL）中进行准确的术前风险评估对于知情决策和合理利用资源非常重要。现有的风险预测工具在不同的医疗保健环境中尚未得到充分验证。在此，我们报告了四个广泛引用的预后模型的比较性外部验证。
使用国家急诊剖腹术审计（NELA）纳入标准，从 2020 年 1 月至 2021 年 5 月，在希腊的 11 家医院连续纳入符合条件的接受 EL 的患者，组成多中心队列。使用美国外科医师学院国家外科质量改进计划（ACS-NSQIP）、NELA、朴茨茅斯生理和手术严重程度评分用于死亡率和发病率的枚举（P-POSSUM）以及预测最优树在急诊手术风险工具计算 30 天死亡率风险预测。记录外科医生使用预设截止值对术后死亡率的评估，并且当原始模型的预测值相对较低时，计算外科医生调整后的 ACS-NSQIP 预测值。使用缩放的 Brier 分数、判别和校准措施和图以及决策曲线分析比较预测性能。通过随机效应荟萃分析评估医院间的异质性。
共纳入 631 例患者，30 天死亡率为 16.3%。ACS-NSQIP 及其外科医生调整版本的缩放 Brier 评分最高。所有模型均具有较高的判别能力，一致性统计量范围从 P-POSSUM 的 0.79 到 NELA 的 0.85。然而，除了外科医生调整后的 ACS-NSQIP（Hosmer-Lemeshow 检验，p = 0.742），所有其他模型的校准均较差（p <0.001）。决策曲线分析显示 ACS-NSQIP 的临床实用性更高。在重新校准后，所有模型的预测准确性均有所提高，但 ACS-NSQIP 仍保持领先地位。ACS-NSQIP 模型的医院间异质性最小，P-POSSUM 的最大。
在广泛的外部验证队列中，ACS-NSQIP 工具对 EL 后死亡率的预测最为准确，为希腊医疗保健系统中术前风险管理提供了实用性。外科医生对患者预后的主观评估可能会优化 ACS-NSQIP 的预测。
诊断测试/标准；二级。
