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邀请函名单的确定:严重复杂性腹腔内感染和随机临床试验纳入标准中脓毒症严重程度评分系统的讨论。

Getting the invite list right: a discussion of sepsis severity scoring systems in severe complicated intra-abdominal sepsis and randomized trial inclusion criteria.

机构信息

1Department of Abdominal Surgery, Abdominal Center, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.

2Emergency and Trauma Surgery Department, Bufalini Hospital, Cesena, Italy.

出版信息

World J Emerg Surg. 2018 Apr 6;13:17. doi: 10.1186/s13017-018-0177-2. eCollection 2018.

Abstract

BACKGROUND

Severe complicated intra-abdominal sepsis (SCIAS) is a worldwide challenge with increasing incidence. Open abdomen management with enhanced clearance of fluid and biomediators from the peritoneum is a potential therapy requiring prospective evaluation. Given the complexity of powering multi-center trials, it is essential to recruit an inception cohort sick enough to benefit from the intervention; otherwise, no effect of a potentially beneficial therapy may be apparent. An evaluation of abilities of recognized predictive systems to recognize SCIAS patients was conducted using an existing intra-abdominal sepsis (IAS) database.

METHODS

All consecutive adult patients with a diffuse secondary peritonitis between 2012 and 2013 were collected from a quaternary care hospital in Finland, excluding appendicitis/cholecystitis. From this retrospectively collected database, a target population (93) of those with either ICU admission or mortality were selected. The performance metrics of the Third Consensus Definitions for Sepsis and Septic Shock based on both SOFA and quick SOFA, the World Society of Emergency Surgery Sepsis Severity Score (WSESSSS), the APACHE II score, Manheim Peritonitis Index (MPI), and the Calgary Predisposition, Infection, Response, and Organ dysfunction (CPIRO) score were all tested for their discriminant ability to identify this subgroup with SCIAS and to predict mortality.

RESULTS

Predictive systems with an area under-the-receiving-operating characteristic (AUC) curve > 0.8 included SOFA, Sepsis-3 definitions, APACHE II, WSESSSS, and CPIRO scores with the overall best for CPIRO. The highest identification rates were SOFA score ≥ 2 (78.4%), followed by the WSESSSS score ≥ 8 (73.1%), SOFA ≥ 3 (75.2%), and APACHE II ≥ 14 (68.8%) identification. Combining the Sepsis-3 septic-shock definition and WSESSS ≥ 8 increased detection to 80%. Including CPIRO score ≥ 3 increased this to 82.8% (Sensitivity-SN; 83% Specificity-SP; 74%. Comparatively, SOFA ≥ 4 and WSESSSS ≥ 8 with or without septic-shock had 83.9% detection (SN; 84%, SP; 75%, 25% mortality).

CONCLUSIONS

No one scoring system behaves perfectly, and all are largely dominated by organ dysfunction. Utilizing combinations of SOFA, CPIRO, and WSESSSS scores in addition to the Sepsis-3 septic shock definition appears to offer the widest "inclusion-criteria" to recognize patients with a high chance of mortality and ICU admission.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT03163095; Registered on May 22, 2017.

摘要

背景

严重复杂的腹腔内脓毒症(SCIAS)是一个全球性的挑战,其发病率正在上升。通过开放性腹部管理,从腹膜中清除更多的液体和生物介质,可能是一种潜在的治疗方法,需要前瞻性评估。鉴于多中心试验的复杂性,招募足够严重的纳入患者至关重要,这些患者能够从干预中受益;否则,潜在有益治疗的效果可能不明显。本研究使用现有的腹腔内脓毒症(IAS)数据库,评估了公认的预测系统识别 SCIAS 患者的能力。

方法

从芬兰一家四级护理医院回顾性收集了 2012 年至 2013 年间所有患有弥漫性继发性腹膜炎但无阑尾炎/胆囊炎的成年患者。从这个回顾性数据库中,选择了 ICU 入院或死亡的目标人群(93 人)。基于 SOFA 和快速 SOFA 的第三次脓毒症和脓毒症休克共识定义、世界急诊外科学会脓毒症严重程度评分(WSESSSS)、APACHE II 评分、曼海姆腹膜炎指数(MPI)和卡尔加里易感性、感染、反应和器官功能障碍(CPIRO)评分的性能指标,均用于识别具有 SCIAS 亚组和预测死亡率的鉴别能力。

结果

AUC 曲线>0.8 的预测系统包括 SOFA、Sepsis-3 定义、APACHE II、WSESSSS 和 CPIRO 评分,其中 CPIRO 总体最佳。最高的识别率是 SOFA 评分≥2(78.4%),其次是 WSESSSS 评分≥8(73.1%)、SOFA≥3(75.2%)和 APACHE II≥14(68.8%)。将 Sepsis-3 感染性休克定义与 WSESSS≥8 相结合,检测率提高到 80%。将 CPIRO 评分≥3 纳入其中,这一比例增加到 82.8%(敏感性-SN;83%特异性-SP;74%)。相比之下,SOFA≥4 和 WSESSSS≥8 合并或不合并感染性休克的检测率为 83.9%(SN;84%,SP;75%,25%死亡率)。

结论

没有一个评分系统表现完美,所有系统都主要由器官功能障碍主导。除了 Sepsis-3 感染性休克定义外,联合使用 SOFA、CPIRO 和 WSESSSS 评分,似乎可以提供最广泛的“纳入标准”,以识别具有高死亡率和 ICU 入院风险的患者。

试验注册

https://clinicaltrials.gov/ct2/show/NCT03163095;注册于 2017 年 5 月 22 日。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ae/5889572/9aad03d1a2c3/13017_2018_177_Fig1_HTML.jpg

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