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重症监护病房死亡风险回顾性计算模型的验证

Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit.

作者信息

Tan Eugene M, Kashyap Rahul, Olson Ian C, O'Horo John C

机构信息

Division of Infectious Diseases, Mayo Clinic, Rochester, MN.

Department of Anesthesia & Perioperative Medicine, Mayo Clinic, Rochester, MN.

出版信息

Mayo Clin Proc Innov Qual Outcomes. 2020 Oct 6;4(5):575-582. doi: 10.1016/j.mayocpiqo.2020.09.001. eCollection 2020 Oct.

Abstract

OBJECTIVE

To compare the predictive performance of Epic Systems Corporation's proprietary intensive care unit (ICU) mortality risk model (IMRM) with that of the Acute Physiology and Chronic Health Evaluation (APACHE) IV score.

METHODS

This is a retrospective cohort study of patients treated from January 1, 2008, through January 1, 2018. This single-center study was performed at Mayo Clinic (Rochester, MN), a tertiary care teaching and referral center. The primary outcome was death in the ICU. Discrimination of each risk model for hospital mortality was assessed by comparing area under the receiver operating characteristic curve (AUROC).

RESULTS

The cohort mostly comprised older patients (median age, 64 years) and men (56.7%). The mortality rate of the cohort was 3.5% (2251 of 63,775 patients). The AUROC for mortality prediction was 89.7% (95% CI, 89.5% to 89.9%) for the IMRM, which was significantly greater than the AUROC of 88.2% (95% CI, 87.9% to 88.4%) for APACHE IV (<.001).

CONCLUSION

The IMRM was superior to the commonly used APACHE IV score and may be easily integrated into electronic health records at any hospital using Epic software.

摘要

目的

比较Epic Systems公司专有的重症监护病房(ICU)死亡风险模型(IMRM)与急性生理学与慢性健康状况评估(APACHE)IV评分的预测性能。

方法

这是一项对2008年1月1日至2018年1月1日期间接受治疗的患者进行的回顾性队列研究。这项单中心研究在梅奥诊所(明尼苏达州罗切斯特)进行,该诊所是一家三级医疗教学和转诊中心。主要结局是ICU内死亡。通过比较受试者工作特征曲线下面积(AUROC)来评估每种医院死亡风险模型的辨别力。

结果

该队列主要由老年患者(中位年龄64岁)和男性(56.7%)组成。该队列的死亡率为3.5%(63775例患者中的2251例)。IMRM的死亡率预测AUROC为89.7%(95%CI,89.5%至89.9%),显著高于APACHE IV的AUROC 88.2%(95%CI,87.9%至88.4%)(P<0.001)。

结论

IMRM优于常用的APACHE IV评分,并且可以轻松集成到任何使用Epic软件的医院的电子健康记录中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c4e/7560567/166c4349a0bc/gr1.jpg

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