Structural equation modeling of findings in pediatric tele-neuroradiology: a 2-year nationwide study of turnaround time predictors and clinical decision support

Clinical Snapshot

Key Findings

• P Value: Statistical significance achieved with >99% power

• Effect Size: CT modality (β = -0.164), trauma score (β = 0.125), structural score (β = 0.142)

• Primary Outcome: Turnaround time variance explained by SEM model: 7.8%

• Nnt Or Sensitivity: Emergency prediction model: AUC = 0.94, NPV = 0.996; 89.4% of emergencies met 60-min benchmark

• Confidence Interval: Not explicitly reported, but bootstrap validation with 5,000 samples performed

Clinical Application

Requires established teleradiology infrastructure and standardised reporting protocols Relevant for rural/remote Australian hospitals using teleradiology, but regulatory differences (TGA vs FDA) and workforce models may limit direct application Pediatric patients requiring urgent neuroradiology interpretation in resource-limited settings

Abstract

BACKGROUND: Pediatric neuroradiology faces significant workforce shortages, with teleradiology emerging as a vital solution. However, studies investigating findings and their operational impact from teleradiology centers remain limited. OBJECTIVES: To develop and validate structural equation models for identifying predictors of turnaround time and formulate recommendations for workflow optimization in pediatric tele-neuroradiology services. DESIGN: A retrospective cohort study following STROBE guidelines. SETTINGS: 107 hospitals across 17 states in the United States (US) via a teleradiology platform providing interpretation services through US board-certified radiologists. PATIENTS AND METHODS: We analyzed 9985 pediatric neuroradiology scans from 7958 patients (January 2023-December 2024). We utilized confirmatory factor analysis to validate findings structures, followed by structural equation modeling to predict turnaround times. Binary logistic regression models were developed with area-under-the-curve (AUC) estimation for performance assessment. Bootstrap validation with 5000 samples ensured model stability. MAIN OUTCOME MEASURES: Primary outcome was turnaround time. Secondary outcomes included requirements for multiple imaging studies, follow-up recommendations, and consultations. SAMPLE SIZE: A total of 9985 studies providing over 99% statistical power for detecting significant relationships. RESULTS: Factor analysis demonstrated a two-factor structure (trauma: α = 0.742, structural: α = 0.685). The structural model explained 7.8% of turnaround time variance, with computed tomography (CT) modality (β = -0.164), trauma score (β = 0.125), and structural score (β = 0.142) as significant predictors. Among immediate neurosurgical emergencies (n = 180, 1.8%), 89.4% achieved turnaround time within the 60-min benchmark for time-sensitive consultations. Prediction models demonstrated excellent discrimination: traumatic findings (AUC = 0.91), structural findings (AUC = 0.92), critical findings (AUC = 0.95), and a dedicated neurosurgical emergency model (AUC = 0.94, NPV = 0.996). A severity classification system showed strong validation against imaging needs (AUC = 0.76) and consultations (AUC = 0.89). CONCLUSIONS: Our study establishes a validated SEM framework for pediatric tele-neuroradiology with excellent predictive performance (AUC = 0.91-0.95). Among immediate neurosurgical emergencies (n = 180, 1.8%), 89.4% met the 60-min benchmark, and a dedicated emergency prediction model achieved AUC = 0.94. However, translation to improved neurosurgical care delivery and patient outcomes remains unvalidated, representing the next investigational priorities. LIMITATIONS: Retrospective design limits causal inference; a single platform may limit generalizability; the CT majority (96.8%) limits magnetic resonance imaging conclusions. Critically, post-diagnostic clinical outcomes, including neurosurgical consultations, interventions performed, and patient outcomes, were not tracked, precluding conclusions about whether documented operational efficiency translated to improved neurosurgical care delivery.