A Severe Sepsis Mortality Prediction Model and Score for Use With Administrative Data.
A Severe Sepsis Mortality Prediction Model and Score for Use With Administrative Data.
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DOI：
10.1097/ccm.0000000000001392
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发表时间：
2016-02
影响因子：
8.8
通讯作者：
Simpson KN
中科院分区：
文献类型：
--
作者：
Ford DW;Goodwin AJ;Simpson AN;Johnson E;Nadig N;Simpson KN
管理数据用于严重脓毒症的研究，质量改进和卫生政策。然而，没有适用于管理数据的脓毒症专用工具来调整疾病严重程度。我们的目标是开发、内部验证和外部验证严重脓毒症死亡率预测模型和相关死亡率预测评分。回顾性队列研究，使用来自美国五个州的2012年管理数据。创建了三个严重脓毒症患者队列：1）严重脓毒症/脓毒性休克的ICD-9-CM代码，2）“Martin”方法和3）“安格斯”方法。该模型在ICD-9-CM队列中开发和内部验证，并在其他队列中进行外部验证。生成每个预测变量的临界点值以创建脓毒症严重程度评分。纽约、马里兰、佛罗里达、密歇根和华盛顿的非联邦医院的急性护理患者，三个严重脓毒症队列之一：1）明确编码（n= 108，448），2）马丁队列（n= 139，094）和3）安格斯队列（n= 523，637）无最大似然估计logistic回归，以开发院内死亡率的预测模型。分别通过Hosmer-Lemeshow拟合优度（GOF）和C-统计量评估模型校准和区分度。将主要队列亚组划分为风险十分位数，并绘制观察到的死亡率与预测死亡率的曲线。GOF对于每个群组证明p>0.05，证明了声音校准。C-统计量范围从0.709（脓毒症严重程度评分）的下限到0.838（安格斯队列）的上限，表明模型区分度为良好至极佳。观察到的死亡率与预期死亡率的比较是稳健的，尽管在最高风险的十分位数中准确性下降。我们的脓毒症严重程度模型和评分是一种为管理数据提供可靠风险调整的工具。
Administrative data is used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score. Retrospective cohort study using 2012 administrative data from five US states. Three cohorts of patients with severe sepsis were created: 1) ICD-9-CM codes for severe sepsis/septic shock, 2) ‘Martin’ approach, and 3) ‘Angus’ approach. The model was developed and internally validated in ICD-9-CM cohort and externally validated in other cohorts. Integer point values for each predictor variable were generated to create a sepsis severity score. Acute care, non-federal hospitals in NY, MD, FL, MI, and WA Patients in one of three severe sepsis cohorts: 1) explicitly coded (n=108,448), 2) Martin cohort (n=139,094), and 3) Angus cohort (n=523,637) None Maximum likelihood estimation logistic regression to develop a predictive model for in-hospital mortality. Model calibration and discrimination assessed via Hosmer-Lemeshow goodness-of-fit (GOF) and C-statistics respectively. Primary cohort subset into risk deciles and observed versus predicted mortality plotted. GOF demonstrated p>0.05 for each cohort demonstrating sound calibration. C-statistic ranged from low of 0.709 (sepsis severity score) to high of 0.838 (Angus cohort) suggesting good to excellent model discrimination. Comparison of observed versus expected mortality was robust although accuracy decreased in highest risk decile. Our sepsis severity model and score is a tool that provides reliable risk adjustment for administrative data.