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通过专家共识对医院管理数据中识别脓毒症的编码进行判定

Adjudication of Codes for Identifying Sepsis in Hospital Administrative Data by Expert Consensus.

作者信息

Garland Allan, Li Na, Sligl Wendy, Lane Alana, Thavorn Kednapa, Wilcox M Elizabeth, Rochwerg Bram, Keenan Sean, Marrie Thomas J, Kumar Anand, Curley Emily, Ziegler Jennifer, Dodek Peter, Loubani Osama, Gervais Alain, Murthy Srinivas, Neto Gina, Prescott Hallie C

机构信息

Department of Medicine, University of Manitoba, Winnipeg, MB, Canada.

Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.

出版信息

Crit Care Med. 2024 Dec 1;52(12):1845-1855. doi: 10.1097/CCM.0000000000006432. Epub 2024 Oct 10.

Abstract

OBJECTIVES

Refine the administrative data definition of sepsis in hospitalized patients, including less severe cases.

DESIGN AND SETTING

For each of 1928 infection and 108 organ dysfunction codes used in Canadian hospital abstracts, experts reached consensus on the likelihood that it could relate to sepsis. We developed a new algorithm, called AlgorithmL, that requires at least one infection and one organ dysfunction code adjudicated as likely or very likely to be related to sepsis. AlgorithmL was compared with four previously described algorithms, regarding included codes, population-based incidence, and hospital mortality rates-separately for ICU and non-ICU cohorts in a large Canadian city. We also compared sepsis identification from these code-based algorithms with the Centers for Disease Control's Adult Sepsis Event (ASE) definition.

SUBJECTS

Among Calgary's adult population of 1.033 million there were 61,632 eligible hospitalizations.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

AlgorithmL includes 720 infection codes and 50 organ dysfunction codes. Comparison algorithms varied from 42-941 infection codes to 2-36 organ codes. There was substantial nonoverlap of codes in AlgorithmL vs. the comparators. Annual sepsis incidence rates (per 100,000 population) based on AlgorithmL were 91 in the ICU and 291 in the non-ICU cohort. Incidences based on comparators ranged from 28-77 for ICU to 11-266 for non-ICU cohorts. Hospital sepsis mortality rates based on AlgorithmL were 24% in ICU and 17% in non-ICU cohorts; based on comparators, they ranged 27-38% in the ICU cohort and 18-47% for the non-ICU cohort. Of AlgorithmL-identified cases, 41% met the ASE criteria, compared with 42-82% for the comparator algorithms.

CONCLUSIONS

Compared with other code-based algorithms, AlgorithmL includes more infection and organ dysfunction codes. AlgorithmL incidence rates are higher; hospital mortality rates are lower. AlgorithmL may more fully encompass the full range of sepsis severity.

摘要

目的

完善住院患者脓毒症的行政数据定义,包括病情较轻的病例。

设计与背景

针对加拿大医院摘要中使用的1928个感染代码和108个器官功能障碍代码,专家们就其与脓毒症相关的可能性达成了共识。我们开发了一种名为算法L的新算法,该算法要求至少有一个感染代码和一个经判定可能或极有可能与脓毒症相关的器官功能障碍代码。在加拿大一个大城市,分别针对重症监护病房(ICU)和非ICU队列,将算法L与之前描述的四种算法在纳入代码、基于人群的发病率和医院死亡率方面进行了比较。我们还将这些基于代码的算法识别出的脓毒症与美国疾病控制中心的成人脓毒症事件(ASE)定义进行了比较。

研究对象

在卡尔加里的103.3万成年人口中,有61632例符合条件的住院病例。

干预措施

无。

测量指标与主要结果

算法L包括720个感染代码和50个器官功能障碍代码。比较算法的感染代码从42 - 941个到器官代码从2 - 36个不等。算法L与比较算法的代码存在大量不重叠。基于算法L的年度脓毒症发病率(每10万人)在ICU队列中为91例,在非ICU队列中为291例。基于比较算法的发病率在ICU队列中为28 - 77例,在非ICU队列中为11 - 266例。基于算法L的医院脓毒症死亡率在ICU队列中为24%,在非ICU队列中为17%;基于比较算法的死亡率在ICU队列中为27 - 38%,在非ICU队列中为18 - 47%。在算法L识别出的病例中,41%符合ASE标准,而比较算法的这一比例为42 - 82%。

结论

与其他基于代码的算法相比,算法L纳入了更多的感染和器官功能障碍代码。算法L的发病率更高;医院死亡率更低。算法L可能更全面地涵盖了脓毒症严重程度的全范围。

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