Background

Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19.

Methods

We conducted a retrospective analysis of resuscitation survivors in the Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 registry between February 2020 and May 2021. The primary outcome was a favorable neurological outcome, indicated by a discharge Cerebral Performance Category score ≤ 2. Pre- and peri-arrest variables were used as predictors. We applied our published logistic regression, neural network, and gradient boosted machine models developed in patients without COVID-19 to the COVID-19 cohort. We also updated the neural network model using transfer learning. Performance was compared between models and the Cardiac Arrest Survival Post-Resuscitation In-Hospital (CASPRI) score.

Results

Among the 4,125 patients with COVID-19 included in the analysis, 484 (12 %) patients survived with favorable neurological outcomes. The gradient boosted machine, trained on non-COVID-19 patients was the best performing model for predicting neurological outcomes in COVID-19 patients, significantly better than the CASPRI score (c-statistic: 0.75 vs 0.67, P < 0.001). While calibration improved for the neural network with transfer learning, it did not surpass the gradient boosted machine in terms of discrimination.

Conclusion

Our gradient boosted machine model developed in non-COVID patients had high discrimination and adequate calibration in COVID-19 resuscitation survivors and may provide clinicians with important information for these patients.

中文翻译：

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预测 2019 年冠状病毒病患者院内心脏骤停后的神经系统结局

背景

机器学习模型比标准工具更准确地预测心脏骤停后复苏患者的神经学结果。但是，它们在 2019 年冠状病毒病 (COVID-19) 患者中的准确性尚不清楚。因此，我们比较了他们在一组患有 COVID-19 的心脏骤停患者中的表现。

方法

我们在 2020 年 2 月至 2021 年 5 月期间对 Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 登记册中的复苏幸存者进行了回顾性分析。主要结果是良好的神经学结果，由出院脑功能类别评分表示≤ 2. 逮捕前和逮捕前变量用作预测变量。我们将在没有 COVID-19 的患者中开发的已发表的逻辑回归、神经网络和梯度提升机器模型应用于 COVID-19 队列。我们还使用迁移学习更新了神经网络模型。比较了模型与心脏骤停后复苏院内存活率 (CASPRI) 评分之间的性能。

结果

在分析中包括的 4,125 名 COVID-19 患者中，484 名 (12%) 患者存活下来，神经系统结果良好。在非 COVID-19 患者身上训练的梯度增强机器是预测 COVID-19 患者神经系统结果的最佳模型，明显优于 CASPRI 评分（c 统计量：0.75 对 0.67，P < 0.001 ）  。虽然使用迁移学习改进了神经网络的校准，但它在辨别方面并没有超过梯度提升机器。

结论

我们在非 COVID 患者中开发的梯度增强机器模型在 COVID-19 复苏幸存者中具有很高的辨别力和足够的校准，可以为临床医生提供有关这些患者的重要信息。