Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

IF 2.6 3区 医学 Q1 OTORHINOLARYNGOLOGY
Otolaryngology- Head and Neck Surgery Pub Date : 2024-12-01 Epub Date: 2024-07-30 DOI:10.1002/ohn.919
Matthew Nguyen, Ameen Amanian, Meihan Wei, Eitan Prisman, Pedro Alejandro Mendez-Tellez
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引用次数: 0

Abstract

Objective: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and further ventilation-related complications such as subglottic stenosis. In this study, we aimed to develop a machine learning tool to predict which patients need a tracheostomy at the onset of admission to the intensive care unit (ICU).

Study design: Retrospective Cohort Study.

Setting: Multicenter Study of 335 Intensive Care Units between 2014 and 2015.

Methods: The eICU Collaborative Research Database (eICU-CRD) was utilized to obtain the patient cohort. Inclusion criteria included: (1) Age >18 years and (2) ICU admission requiring mechanical ventilation. The primary outcome of interest included tracheostomy assessed via a binary classification model. Models included logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost).

Results: Of 38,508 invasively mechanically ventilated patients, 1605 patients underwent a tracheostomy. The XGBoost, RF, and LR models had fair performances at an AUROC 0.794, 0.780, and 0.775 respectively. Limiting the XGBoost model to 20 features out of 331, a minimal reduction in performance was observed with an AUROC of 0.778. Using Shapley Additive Explanations, the top features were an admission diagnosis of pneumonia or sepsis and comorbidity of chronic respiratory failure.

Conclusions: Our machine learning model accurately predicts the probability that a patient will eventually require a tracheostomy upon ICU admission, and upon prospective validation, we have the potential to institute earlier interventions and reduce the complications of prolonged ventilation.

预测重症监护病房入院时是否需要气管造口术--多中心机器学习分析。
目的:很难预测哪些接受机械通气的患者最终需要进行气管切开术,这将使他们面临不必要的自主呼吸试验、更多的呼吸机使用时间、更高的费用以及更多与通气相关的并发症(如声门下狭窄)。在这项研究中,我们旨在开发一种机器学习工具,用于预测哪些患者在入住重症监护室(ICU)之初就需要进行气管切开术:研究设计:回顾性队列研究:2014年至2015年期间对335个重症监护病房进行的多中心研究:利用 eICU 合作研究数据库(eICU-CRD)获得患者队列。纳入标准包括(1) 年龄大于 18 岁;(2) 入住 ICU 时需要机械通气。主要研究结果包括通过二元分类模型评估的气管切开术。模型包括逻辑回归(LR)、随机森林(RF)和极梯度提升(XGBoost):在 38,508 名有创机械通气患者中,有 1605 名患者接受了气管切开术。XGBoost、RF 和 LR 模型的 AUROC 分别为 0.794、0.780 和 0.775,表现尚可。将 XGBoost 模型限制在 331 个特征中的 20 个特征,观察到的性能降低幅度很小,AUROC 为 0.778。使用 Shapley Additive Explanations,入院诊断为肺炎或败血症以及合并慢性呼吸衰竭的特征排在首位:我们的机器学习模型能准确预测患者在入住 ICU 后最终需要气管切开术的概率,经过前瞻性验证后,我们有可能提前采取干预措施,减少长期通气带来的并发症。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Otolaryngology- Head and Neck Surgery
Otolaryngology- Head and Neck Surgery 医学-耳鼻喉科学
CiteScore
6.70
自引率
2.90%
发文量
250
审稿时长
2-4 weeks
期刊介绍: Otolaryngology–Head and Neck Surgery (OTO-HNS) is the official peer-reviewed publication of the American Academy of Otolaryngology–Head and Neck Surgery Foundation. The mission of Otolaryngology–Head and Neck Surgery is to publish contemporary, ethical, clinically relevant information in otolaryngology, head and neck surgery (ear, nose, throat, head, and neck disorders) that can be used by otolaryngologists, clinicians, scientists, and specialists to improve patient care and public health.
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