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发展和验证医院医学安全脓毒症倡议死亡率模型。

Development and Validation of the Hospital Medicine Safety Sepsis Initiative Mortality Model.

机构信息

Department of Internal Medicine, University of Michigan, Ann Arbor, MI; VA Center for Clinical Management Research, Ann Arbor, MI.

Department of Internal Medicine, University of Michigan, Ann Arbor, MI.

出版信息

Chest. 2024 Nov;166(5):1035-1045. doi: 10.1016/j.chest.2024.06.3769. Epub 2024 Jul 2.

Abstract

BACKGROUND

When comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis).

RESEARCH QUESTION

Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vs unadjusted data differ?

STUDY DESIGN AND METHODS

Retrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier scores, and comparisons of predicted vs observed mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vs adjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment.

RESULTS

Among 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and ≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9% of hospitals.

INTERPRETATION

The HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model may aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.

摘要

背景

在比较脓毒症的结果时,必须考虑患者的病例组合,以进行公平的比较。我们开发了一种模型来评估密歇根医院医学安全脓毒症倡议(HMS-Sepsis)中的风险调整后 30 天死亡率。

研究问题

HMS-Sepsis 登记处的数据能否充分预测 30 天死亡率的风险?使用调整后和未调整的数据进行绩效评估是否存在差异?

研究设计和方法

这是 HMS-Sepsis 登记处(2022 年 4 月至 2023 年 9 月)中社区获得性脓毒症住院患者的回顾性队列研究,分为 70%的推导队列和 30%的验证队列。我们拟合了一个风险调整模型(HMS-Sepsis 死亡率模型),该模型纳入了急性生理、人口统计学和基线健康数据,并使用一致性(C)统计、Brier 评分以及通过风险的十分位数对预测死亡率与观察死亡率进行比较来评估模型性能。我们通过观察死亡率与调整死亡率比较,了解风险调整对医院绩效评估的影响程度,比较了医院的表现(第一五分位数、五分位数、五分位数)。

结果

在研究期间,来自 66 家医院的 17514 例住院患者中,有 12260 例(70%)用于模型推导,5254 例(30%)用于模型验证。整个队列的 30 天死亡率为 19.4%。最终模型纳入了 13 个生理变量、2 个生理交互作用和 16 个人口统计学和慢性健康变量。最重要的变量是年龄、转移性实体瘤、体温、意识状态改变和血小板计数。该模型在推导队列中的 C 统计量为 0.82,在验证队列中的 C 统计量为 0.81,在所有评估的亚组中均≥0.78。总体校准误差为 0.0%,风险十分位数的平均校准误差为 1.5%。标准化死亡率比为 33.9%的医院提供了与观察死亡率不同的评估结果。

解释

HMS-Sepsis 死亡率模型显示出较强的区分度和足够的校准度,并将三分之一的医院重新分类为与未调整死亡率不同的绩效类别。基于其良好的表现,HMS-Sepsis 死亡率模型可能有助于公平的医院基准测试、时间变化的评估和观察性因果推理分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59b2/11638544/6a4028f2f81e/gr1.jpg

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