Abstract：
BACKGROUND: Decannulation for people in a persistent vegetative state (PVS) is challenging and relevant predictors of successful decannulation have yet to be identified.
OBJECTIVE: This study aimed to explore the predictors of tracheostomy decannulation outcomes in individuals in PVS and to develop a nomogram.
METHODS: In 2022, 872 people with tracheostomy in PVS were retrospectively enrolled and their data was randomly divided into a training set and a validation set in a 7:3 ratio. Univariate and multivariate regression analyses were performed on the training set to explore the influencing factors for decannulation and nomogram development. Internal validation was performed using 5-fold cross-validation. External validation was performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) on both the training and validation sets.
RESULTS: Data from 610 to 262 individuals were used for the training and validation sets, respectively. The multivariate regression analysis found that duration of tracheostomy tube placement≥30 days (Odds Ratio [OR] 0.216, 95 % CI 0.151-0.310), pulmonary infection (OR 0.528, 95 %CI 0.366-0.761), hypoproteinemia (OR 0.669, 95 % CI 0.463-0.967), no passive standing training (OR 0.372, 95 % CI 0.253-0.547), abnormal swallowing reflex (OR 0.276, 95 % CI 0.116-0.656), mechanical ventilation (OR 0.658, 95 % CI 0.461-0.940), intensive care unit (ICU) duration>4 weeks (OR 0.517, 95 % CI 0.332-0.805), duration of endotracheal tube (OR 0.855, 95 % CI 0.803-0.907), older age (OR 0.981, 95 % CI 0.966-0.996) were risk factors for decannulation failure. Conversely, peroral feeding (OR 1.684, 95 % CI 1.178-2.406), passive standing training≥60 min (OR 1.687, 95 % CI 1.072-2.656), private caregiver (OR 1.944, 95 % CI 1.350-2.799) and ICU duration<2 weeks (OR 1.758, 95 % CI 1.173-2.634) were protective factors conducive to successful decannulation. The 5-fold cross-validation revealed a mean area under the curve of 0.744. The ROC curve C-indexes for the training and validation sets were 0.784 and 0.768, respectively, and the model exhibited good stability and accuracy. The DCA revealed a net benefit when the risk threshold was between 0 and 0.4.
CONCLUSIONS: The nomogram can help adjust the treatment and reduce decannulation failure.
BACKGROUND: Clinical registration is not mandatory for retrospective studies.
OBJECTIVE: This study aimed to explore the predictors of tracheostomy decannulation outcomes in individuals in PVS and to develop a nomogram.
METHODS: In 2022, 872 people with tracheostomy in PVS were retrospectively enrolled and their data was randomly divided into a training set and a validation set in a 7:3 ratio. Univariate and multivariate regression analyses were performed on the training set to explore the influencing factors for decannulation and nomogram development. Internal validation was performed using 5-fold cross-validation. External validation was performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) on both the training and validation sets.
RESULTS: Data from 610 to 262 individuals were used for the training and validation sets, respectively. The multivariate regression analysis found that duration of tracheostomy tube placement≥30 days (Odds Ratio [OR] 0.216, 95 % CI 0.151-0.310), pulmonary infection (OR 0.528, 95 %CI 0.366-0.761), hypoproteinemia (OR 0.669, 95 % CI 0.463-0.967), no passive standing training (OR 0.372, 95 % CI 0.253-0.547), abnormal swallowing reflex (OR 0.276, 95 % CI 0.116-0.656), mechanical ventilation (OR 0.658, 95 % CI 0.461-0.940), intensive care unit (ICU) duration>4 weeks (OR 0.517, 95 % CI 0.332-0.805), duration of endotracheal tube (OR 0.855, 95 % CI 0.803-0.907), older age (OR 0.981, 95 % CI 0.966-0.996) were risk factors for decannulation failure. Conversely, peroral feeding (OR 1.684, 95 % CI 1.178-2.406), passive standing training≥60 min (OR 1.687, 95 % CI 1.072-2.656), private caregiver (OR 1.944, 95 % CI 1.350-2.799) and ICU duration<2 weeks (OR 1.758, 95 % CI 1.173-2.634) were protective factors conducive to successful decannulation. The 5-fold cross-validation revealed a mean area under the curve of 0.744. The ROC curve C-indexes for the training and validation sets were 0.784 and 0.768, respectively, and the model exhibited good stability and accuracy. The DCA revealed a net benefit when the risk threshold was between 0 and 0.4.
CONCLUSIONS: The nomogram can help adjust the treatment and reduce decannulation failure.
BACKGROUND: Clinical registration is not mandatory for retrospective studies.
摘要：
背景：持续植物状态（PVS）的人的拔管具有挑战性，成功拔管的相关预测因素尚未确定。
目的：本研究旨在探讨PVS患者气管切开拔管结果的预测因素，并建立列线图。
方法：2022年，对872例PVS气管造口术患者进行了回顾性研究，他们的数据以7：3的比例随机分为训练集和验证集。对训练集进行单因素和多元回归分析，以探索脱管和列线图发展的影响因素。使用5倍交叉验证进行内部验证。使用受试者工作特征(ROC)曲线进行外部验证，校正曲线,以及对训练集和验证集的决策曲线分析(DCA)。
结果：来自610至262个人的数据用于训练和验证集，分别。多因素回归分析发现气管切开置管时间≥30天(比值比[OR]0.216,95%CI0.151-0.310),肺部感染（OR0.528，95CI0.366-0.761），低蛋白血症（OR0.669，95%CI0.463-0.967），无被动站立训练(OR0.372，95%CI0.253-0.547)，异常吞咽反射（OR0.276，95%CI0.116-0.656），机械通气（OR0.658，95%CI0.461-0.940），重症监护病房（ICU）持续时间>4周（OR0.517，95％CI0.332-0.805），气管内导管的持续时间（OR0.855，95％CI0.803-0.907），高龄(OR0.981,95%CI0.966-0.996)是拔管失败的危险因素.相反，经口喂养（OR1.684，95%CI1.178-2.406），被动站立训练≥60分钟(OR1.687，95%CI1.072-2.656)，私人看护者(OR1.944,95%CI1.350-2.799)和ICU时间<2周(OR1.758,95%CI1.173-2.634)是有利于成功拔管的保护因素.5倍交叉验证显示曲线下平均面积为0.744。训练集和验证集的ROC曲线C指数分别为0.784和0.768,模型具有良好的稳定性和准确性。当风险阈值在0到0.4之间时，DCA显示出净收益。
结论：列线图可以帮助调整治疗方法并减少拔管失败。
背景：临床注册对于回顾性研究不是强制性的。
目的：本研究旨在探讨PVS患者气管切开拔管结果的预测因素，并建立列线图。
方法：2022年，对872例PVS气管造口术患者进行了回顾性研究，他们的数据以7：3的比例随机分为训练集和验证集。对训练集进行单因素和多元回归分析，以探索脱管和列线图发展的影响因素。使用5倍交叉验证进行内部验证。使用受试者工作特征(ROC)曲线进行外部验证，校正曲线,以及对训练集和验证集的决策曲线分析(DCA)。
结果：来自610至262个人的数据用于训练和验证集，分别。多因素回归分析发现气管切开置管时间≥30天(比值比[OR]0.216,95%CI0.151-0.310),肺部感染（OR0.528，95CI0.366-0.761），低蛋白血症（OR0.669，95%CI0.463-0.967），无被动站立训练(OR0.372，95%CI0.253-0.547)，异常吞咽反射（OR0.276，95%CI0.116-0.656），机械通气（OR0.658，95%CI0.461-0.940），重症监护病房（ICU）持续时间>4周（OR0.517，95％CI0.332-0.805），气管内导管的持续时间（OR0.855，95％CI0.803-0.907），高龄(OR0.981,95%CI0.966-0.996)是拔管失败的危险因素.相反，经口喂养（OR1.684，95%CI1.178-2.406），被动站立训练≥60分钟(OR1.687，95%CI1.072-2.656)，私人看护者(OR1.944,95%CI1.350-2.799)和ICU时间<2周(OR1.758,95%CI1.173-2.634)是有利于成功拔管的保护因素.5倍交叉验证显示曲线下平均面积为0.744。训练集和验证集的ROC曲线C指数分别为0.784和0.768,模型具有良好的稳定性和准确性。当风险阈值在0到0.4之间时，DCA显示出净收益。
结论：列线图可以帮助调整治疗方法并减少拔管失败。
背景：临床注册对于回顾性研究不是强制性的。
