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Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU
Daniela Chanci1, Jocelyn R Grunwell2,3, Alireza Rafiei1
1Department of Biomedical Informatics, Emory University, Atlanta, GA.
Insights
A new machine learning model accurately predicts the need for intubation in pediatric intensive care unit (PICU) patients using electronic health record data. This tool can help optimize respiratory care and staff allocation for critically ill children.
Area of Science:
- Pediatric critical care medicine
- Machine learning in healthcare
- Clinical decision support systems
Background:
- Endotracheal intubation is a critical procedure in pediatric intensive care units (PICUs).
- Predicting the need for intubation is essential for timely intervention and resource allocation.
- Existing methods often rely on subjective clinical criteria.
Purpose of the Study:
- To develop and externally validate a machine learning model for predicting intubation in pediatric patients.
- To utilize objective, routinely available electronic medical record (EMR) data for prediction.
- To improve upon conventional clinical criteria for intubation prediction.
Main Methods:
- Retrospective observational cohort study conducted in two PICUs.
- Data from 13,208 PICU stays (derivation) and 17,841 PICU stays (validation) between 2010-2022.
- A Categorical Boosting (CatBoost) model was trained using vital signs, lab tests, demographics, medications, and organ dysfunction scores.
Main Results:
- The CatBoost model achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.88 in the derivation cohort and 0.92 in the validation cohort.
- Intubation events occurred in 8.90% of the derivation cohort and 6.53% of the validation cohort.
- The model demonstrated superior performance compared to extreme gradient boost, random forest, and logistic regression models.
Conclusions:
- An interpretable machine learning model was developed and validated for predicting intubation in PICU children.
- The model effectively uses readily available EMR data, outperforming traditional clinical criteria.
- Implementation can aid clinicians in optimizing intubation timing and resource management for mechanically ventilated children.
Objectives:
To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs).
Design:
Retrospective observational cohort study.
Setting:
Two PICUs within the same healthcare system: an academic, quaternary care center (36 beds) and a community, tertiary care center (56 beds).
Patients:
Children younger than 18 years old admitted to a PICU between 2010 and 2022.
Interventions:
None.
Measurements And Main Results:
Clinical data was extracted from the EMR. PICU stays with at least one mechanical ventilation event (≥ 24 hr) occurring within a window of 1-7 days after hospital admission were included in the study. Of 13,208 PICU stays in the derivation PICU cohort, 1,175 (8.90%) had an intubation event. In the validation cohort, there were 1,165 of 17,841 stays (6.53%) with an intubation event. We trained a Categorical Boosting (CatBoost) model using vital signs, laboratory tests, demographic data, medications, organ dysfunction scores, and other patient characteristics to predict the need of intubation and mechanical ventilation using a 24-hour window of data within their hospital stay. We compared the CatBoost model to an extreme gradient boost, random forest, and a logistic regression model. The area under the receiving operating characteristic curve for the derivation cohort and the validation cohort was 0.88 (95% CI, 0.88-0.89) and 0.92 (95% CI, 0.91-0.92), respectively.
Conclusions:
We developed and externally validated an interpretable machine learning prediction model that improves on conventional clinical criteria to predict the need for intubation in children hospitalized in a PICU using information readily available in the EMR. Implementation of our model may help clinicians optimize the timing of endotracheal intubation and better allocate respiratory and nursing staff to care for mechanically ventilated children.
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