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深度学习预测需要机械通气 7 天的患者的长期死亡率。

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

机构信息

Department of Emergency Medicine, Division of Critical Care, University of New Mexico Health Science Center, Albuquerque, New Mexico, United States of America.

Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.

出版信息

PLoS One. 2021 Jun 29;16(6):e0253443. doi: 10.1371/journal.pone.0253443. eCollection 2021.

Abstract

BACKGROUND

Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.

METHODS

Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.

RESULTS

There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.

DISCUSSION

We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

摘要

背景

在需要长时间机械通气的急性呼吸衰竭患者中,通常在 7 至 10 天后进行气管切开术。然而,在重症监护病房接受气管切开术的患者中,有一半将在一年内死亡,通常在三个月内。现有的 ProVent 评分等用于长时间机械通气的死亡率预测模型,其敏感性较差,并且直到机械通气 14 天后才应用。我们使用深度学习技术开发了一种预测需要机械通气超过 7 天的患者 3 个月死亡率的模型,并将其与现有的死亡率模型进行了比较。

方法

回顾性队列研究。地点:医疗信息集市重症监护 III 数据库。患者:所有需要机械通气≥7 天的成年人。测量:使用过程护理变量(包括人口统计学、生理学和临床数据)创建了一个用于 3 个月死亡率的神经网络模型。比较了接受者操作特征曲线下面积(AUROC)在预测 3 个月和 12 个月死亡率方面与 ProVent 模型的差异。使用 Shapley 值来确定对模型贡献最大的变量。

结果

共有 4334 次就诊,分为开发队列(n=3467)和测试队列(n=867)。最终的深度学习模型包括 250 个变量,在机械通气第 7 天预测 3 个月死亡率的 AUROC 为 0.74,而 ProVent 模型为 0.59。年龄较大和入住重症监护病房时简化急性生理学评分 II(SAPS II)评分较高对预测死亡率的贡献最大。

讨论

我们使用神经网络方法开发了一种深度学习预测模型,用于预测需要机械通气≥7 天的患者的 3 个月死亡率,该模型利用了现成的临床变量。该模型在预测需要机械通气≥7 天的患者死亡率方面优于 ProVent 模型。该模型需要外部验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0aa0/8241081/5272c9bd0fc3/pone.0253443.g001.jpg

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