背景:在比较脓毒症后的结果时,必须考虑患者病例组合,以进行公平的比较。我们在密歇根医院医学安全脓毒症倡议(HMS-Sepsis)中开发了一个模型来评估风险调整后的30天死亡率。
目的:HMS-Sepsis注册数据能否充分预测30天死亡率的风险?使用调整后与未调整后数据的绩效评估是否有所不同?
方法:HMS-Sepsis注册中社区发作性败血症住院的回顾性队列(4/2022-9/2023),具有拆分推导(70%)和验证(30%)队列。我们拟合了一个纳入急性生理学的风险调整模型(HMS-脓毒症死亡率模型),人口统计学,和基线健康数据,并使用c统计量评估模型性能,Brier的分数,以及按风险分位数比较预测和观察到的死亡率。我们比较了医院的表现(第一五分之一,中间五分之一,第五五分之一)使用观察到的死亡率与调整后的死亡率来了解风险调整对医院绩效评估的影响程度。
结果:在研究期间,来自66家医院的17,514例住院患者中,12,260(70%)用于模型推导,5,254(30%)用于模型验证。总队列的30天死亡率为19.4%。最终模型包括13个生理变量,两种生理相互作用,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,温度,精神状态改变,和血小板计数。推导队列的模型c统计量为0.82,验证队列为0.81,所有评估的亚组≥0.78。总体校准误差为0.0%,风险分位数的平均校准误差为1.5%。对于33.9%的医院,标准化死亡率与观察到的死亡率产生了不同的评估。
结论:HMS-脓毒症死亡率模型具有很强的区分度,充分校准,并将三分之一的医院与未调整死亡率重新分类为不同的绩效类别。基于其强劲的表现,HMS-脓毒症死亡率模型可以帮助公平的医院基准,评估时间变化,和观察性因果推断分析。
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).
OBJECTIVE: Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vs unadjusted data differ?
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\'s 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.
CONCLUSIONS: 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 can aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.