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Identifying Long-Term Morbidities and Health Trajectories After Prolonged Mechanical Ventilation in Children Using
Aline B Maddux1, Peter M Mourani2, Kristen Miller3
1Department of Pediatrics, Section of Critical Care Medicine, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO.
Insights
Identifying post-mechanical ventilation outcomes in children is crucial. Machine learning identified distinct patient groups, with complex conditions predicting non-survival and longer intensive care unit stays predicting higher morbidity.
Area of Science:
- Pediatric critical care medicine
- Health services research
- Data science in healthcare
Background:
- Prolonged invasive mechanical ventilation in children can lead to significant postdischarge health issues.
- Understanding postdischarge outcome phenotypes is essential for targeted interventions and research.
Purpose of the Study:
- To identify distinct postdischarge outcome phenotypes in children after invasive mechanical ventilation using insurance claims data.
- To determine risk factors associated with poor postdischarge outcomes.
Main Methods:
- Retrospective cohort study of 381 children requiring invasive mechanical ventilation for >= 3 days.
- Unsupervised machine learning applied to insurance claims data to define outcome phenotypes.
- Regression analyses to identify predictors of unfavorable outcomes.
Main Results:
- Three phenotypes were identified: lower morbidity (n=300), higher morbidity (n=62), and 1-year nonsurvivors (n=19).
- Complex chronic conditions were the strongest predictor of the nonsurvivor phenotype.
- Longer pediatric intensive care unit (PICU) stays and tracheostomy placement predicted higher morbidity.
Conclusions:
- New morbidities are common following prolonged mechanical ventilation in children.
- Phenotype identification can aid in prognostic enrichment for clinical trials and personalized care.
Objectives:
To identify postdischarge outcome phenotypes and risk factors for poor outcomes using insurance claims data.
Design:
Retrospective cohort study.
Setting:
Single quaternary center.
Patients:
Children without preexisting tracheostomy who required greater than or equal to 3 days of invasive mechanical ventilation, survived the hospitalization, and had postdischarge insurance eligibility in Colorado's All Payer Claims Database.
Interventions:
None.
Measurements And Main Results:
We used unsupervised machine learning to identify functional outcome phenotypes based on claims data representative of postdischarge morbidities. We assessed health trajectory by comparing change in the number of insurance claims between quarters 1 and 4 of the postdischarge year. Regression analyses identified variables associated with unfavorable outcomes. The 381 subjects had median age 3.3 years (interquartile range, 0.9-12 yr), and 147 (39%) had a complex chronic condition. Primary diagnoses were respiratory (41%), injury (23%), and neurologic (11%). We identified three phenotypes: lower morbidity (n = 300), higher morbidity (n = 62), and 1-year nonsurvivors (n = 19). Complex chronic conditions most strongly predicted the nonsurvivor phenotype. Longer PICU stays and tracheostomy placement most strongly predicted the higher morbidity phenotype. Patients with high but improving postdischarge resource use were differentiated by high illness severity and long PICU stays. Patients with persistently high or increasing resource use were differentiated by complex chronic conditions and tracheostomy placement.
Conclusions:
New morbidities are common after prolonged mechanical ventilation. Identifying phenotypes at high risk of postdischarge morbidity may facilitate prognostic enrichment in clinical trials.
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