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Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU
Colin M Rogerson1, Samer Abu-Sultaneh2, Jeremy M Loberger3
1Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, Indiana. crogerso@iupui.edu.
Insights
This study developed a predictive model for invasive mechanical ventilation duration in pediatric intensive care units (PICUs). The model aids in quality improvement and benchmarking by standardizing ventilation duration ratios.
Area of Science:
- Pediatric critical care medicine
- Health informatics
- Biostatistics
Background:
- Prolonged invasive mechanical ventilation in pediatric intensive care units (PICUs) is linked to increased morbidities.
- A standardized benchmark for mechanical ventilation duration in PICUs is currently lacking.
- This study addresses the need for objective measures to assess and improve ventilation practices.
Purpose of the Study:
- To develop and validate a multi-center prediction model for invasive mechanical ventilation duration in the PICU.
- To establish a standardized ratio for invasive mechanical ventilation duration.
- To provide a tool for quality improvement and benchmarking in pediatric critical care.
Main Methods:
- Retrospective cohort study utilizing Virtual Pediatric Systems registry data (157 institutions, 2012-2021).
- Included 112,353 pediatric encounters requiring invasive mechanical ventilation >24 hours within the first day of PICU admission.
- Four prediction models were trained and validated using data from the first 24 hours of ventilation.
Main Results:
- The random forest model demonstrated strong predictive performance with observed-to-expected (O/E) ratios near one across validation and full cohorts.
- The best model achieved an O/E ratio of 1.043 in validation and 1.009 in the full cohort.
- Significant institutional variation in O/E ratios (0.49-1.91) and temporal changes were observed.
Conclusions:
- A validated model effectively predicts invasive mechanical ventilation duration at aggregated PICU and cohort levels.
- This model can support quality improvement initiatives and institutional benchmarking in PICUs.
- The tool facilitates performance tracking and standardization of ventilation duration over time.
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.
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