预测儿科 ICU 中机械通气的持续时间。

Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU.

机构信息

Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, Indiana.

University of Alabama at Birmingham, Children's of Alabama, Birmingham, Alabama.

出版信息

Respir Care. 2023 Nov 25;68(12):1623-1630. doi: 10.4187/respcare.11015.

Abstract

BACKGROUND

Timely ventilator liberation can prevent morbidities associated with invasive mechanical ventilation in the pediatric ICU (PICU). There currently exists no standard benchmark for duration of invasive mechanical ventilation in the PICU. This study sought to develop and validate a multi-center prediction model of invasive mechanical ventilation duration to determine a standardized duration of invasive mechanical ventilation ratio.

METHODS

This was a retrospective cohort study using registry data from 157 institutions in the Virtual Pediatric Systems database. The study population included encounters in the PICU between 2012-2021 involving endotracheal intubation and invasive mechanical ventilation in the first day of PICU admission who received invasive mechanical ventilation for > 24 h. Subjects were stratified into a training cohort (2012-2017) and 2 validation cohorts (2018-2019/2020-2021). Four models to predict the duration of invasive mechanical ventilation were trained using data from the first 24 h, validated, and compared.

RESULTS

The study included 112,353 unique encounters. All models had observed-to-expected (O/E) ratios close to one but low mean squared error and R values. The random forest model was the best performing model and achieved an O/E ratio of 1.043 (95% CI 1.030-1.056) and 1.004 (95% CI 0.990-1.019) in the validation cohorts and 1.009 (95% CI 1.004-1.016) in the full cohort. There was a high degree of institutional variation, with single-unit O/E ratios ranging between 0.49-1.91. When stratified by time period, there were observable changes in O/E ratios at the individual PICU level over time.

CONCLUSIONS

We derived and validated a model to predict the duration of invasive mechanical ventilation that performed well in aggregated predictions at the PICU and the cohort level. This model could be beneficial in quality improvement and institutional benchmarking initiatives for use at the PICU level and for tracking of performance over time.

摘要

背景

及时的呼吸机撤离可以预防小儿重症监护病房(PICU)中与有创机械通气相关的并发症。目前,PICU 中使用有创机械通气的时间没有标准的基准。本研究旨在开发和验证一种多中心有创机械通气时间预测模型,以确定标准化的有创机械通气时间比。

方法

这是一项使用虚拟儿科系统数据库中 157 个机构的登记数据进行的回顾性队列研究。研究人群包括 2012-2021 年期间在 PICU 中接受气管内插管和有创机械通气的患者,他们在 PICU 入院的第一天接受了超过 24 小时的有创机械通气。受试者被分为训练队列(2012-2017 年)和 2 个验证队列(2018-2019 年/2020-2021 年)。使用前 24 小时的数据训练了 4 种预测有创机械通气持续时间的模型,对这些模型进行了验证和比较。

结果

该研究共纳入了 112353 例独特的患者。所有模型的观察到的预期(O/E)比值都接近 1,但平均平方误差和 R 值较低。随机森林模型是表现最好的模型,在验证队列中获得了 O/E 比值为 1.043(95%CI 1.030-1.056)和 1.004(95%CI 0.990-1.019),在全队列中获得了 1.009(95%CI 1.004-1.016)。机构之间存在很大的差异,单个单位的 O/E 比值在 0.49-1.91 之间。按时间段分层时,在个别 PICU 水平上,O/E 比值随时间发生了可观察到的变化。

结论

我们得出并验证了一种预测有创机械通气时间的模型,该模型在 PICU 和队列水平上的综合预测中表现良好。该模型可用于 PICU 水平的质量改进和机构基准测试,以及随时间跟踪性能。

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