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

Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU

Colin M Rogerson  1 , Samer Abu-Sultaneh  2 , Jeremy M Loberger  3 , Patrick Ross  4 , Robinder G Khemani  4 , L Nelson Sanchez-Pinto  5
Affiliations

Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU

Colin M Rogerson et al. Respir Care. .

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 R2 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.

Keywords: machine learning; mechanical ventilation; pediatric critical care; pediatrics; quality improvement.

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Conflict of interest statement

The authors have disclosed no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Histogram of duration of invasive mechanical ventilation in days with subjects intubated > 14 d (truncated at 14).
Fig. 2.
Fig. 2.
Caterpillar plot of observed-to-expected ratio by institution with 95% CI.
Fig. 3.
Fig. 3.
Institutional observed-to-expected ratios for the top and bottom deciles over time.
See this image and copyright information in PMC

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