Abstract

Objective

To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team.

Materials and Methods

We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.

Results

The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.

Discussion

Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.

Conclusion

We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

中文翻译：

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COVID-19 大流行期间实时电子健康记录死亡率预测：一项前瞻性队列研究

摘要

客观的

快速开发、验证和实施 COVID-19 大流行的新型实时死亡率评分，该评分改进了顺序器官衰竭评估 (SOFA)，为危机护理标准团队提供决策支持。

材料和方法

我们开发、验证和部署了一个叠加的泛化模型，通过结合 5 个先前验证的分数和其他与 COVID-19 特定死亡率相关的新变量，使用电子健康记录 (EHR) 中的可用数据来预测死亡率。我们使用 2020 年 3 月至 2020 年 7 月期间从科罗拉多州 12 家医院前瞻性收集的数据验证了该模型。我们将新模型的受试者操作曲线下面积 (AUROC) 与 SOFA 评分和 Charlson 合并症指数进行了比较。

结果

前瞻性队列包括 27296 次遭遇，其中 1358 人（5.0%）SARS-CoV-2 呈阳性，4494 人（16.5%）需要重症监护病房护理，1480 人（5.4%）需要机械通气，717 人（2.6%）以死亡告终。Charlson 合并症指数和 SOFA 评分预测死亡率，AUROC 分别为 0.72 和 0.90。我们的新评分预测死亡率为 AUROC 0.94。在 COVID-19 患者子集中，堆叠模型预测死亡率为 AUROC 0.90，而 SOFA 的 AUROC 为 0.85。

讨论

堆叠回归提供了一个灵活、可更新、可实时实施、符合伦理道德的预测分析工具，用于决策支持，从经过验证的模型开始，仅包含可改进预测的新信息。

结论

我们开发并验证了实时 EHR 中准确的院内死亡率预测评分，以便使用改进 SOFA 的新型模型进行自动和连续计算。