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).
METHODS: Retrospective Cohort Study.
METHODS: 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.
METHODS: Retrospective Cohort Study.
METHODS: 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.
摘要：
目的：很难预测哪些机械通气患者最终需要气管切开术，这进一步使他们容易受到不必要的自主呼吸试验的影响。呼吸机上的额外时间，增加成本,和进一步的通气相关并发症，如声门下狭窄。在这项研究中,我们的目标是开发一种机器学习工具来预测哪些患者在入住重症监护病房(ICU)时需要气管造口术.
方法：回顾性队列研究。
方法：2014年至2015年335个重症监护病房的多中心研究。
方法：使用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>18岁和（2）需要机械通气的ICU入院。感兴趣的主要结果包括通过二元分类模型评估的气管造口术。模型包括逻辑回归(LR)，随机森林(RF)，和极端梯度提升（XGBoost）。
结果：在38,508例侵入性机械通气患者中，1605例患者接受了气管造口术。XGBoost,射频,和LR模型的AUROC分别为0.794、0.780和0.775。将XGBoost模型限制为331个中的20个特征，观察到AUROC为0.778的性能降低最小。使用Shapley加法解释，主要特征是入院诊断为肺炎或败血症以及慢性呼吸衰竭的合并症.
结论：我们的机器学习模型准确地预测了患者在入住ICU后最终需要进行气管造口术的概率。经过前瞻性验证，我们有可能采取早期干预措施,减少长时间通气的并发症.
方法：回顾性队列研究。
方法：2014年至2015年335个重症监护病房的多中心研究。
方法：使用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>18岁和（2）需要机械通气的ICU入院。感兴趣的主要结果包括通过二元分类模型评估的气管造口术。模型包括逻辑回归(LR)，随机森林(RF)，和极端梯度提升（XGBoost）。
结果：在38,508例侵入性机械通气患者中，1605例患者接受了气管造口术。XGBoost,射频,和LR模型的AUROC分别为0.794、0.780和0.775。将XGBoost模型限制为331个中的20个特征，观察到AUROC为0.778的性能降低最小。使用Shapley加法解释，主要特征是入院诊断为肺炎或败血症以及慢性呼吸衰竭的合并症.
结论：我们的机器学习模型准确地预测了患者在入住ICU后最终需要进行气管造口术的概率。经过前瞻性验证，我们有可能采取早期干预措施,减少长时间通气的并发症.
