Abstract
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.