A system for grouping presenting complaints: The pediatric emergency reason for visit clusters

Objectives: To develop a set of chief complaint groupings for pediatric emergency department (ED) visits that is comprehensive, parsimonious, clinically sensible, and evidence-based. Methods: Investigators derived candidate chief complaint clusters and ranked them a priori into three perceived severity categories. Pediatric visits were extracted from the National Hospital Ambulatory Medical Care Survey (NHAMCS); data for years 1998 and 2000 (n = 13,186) were used for derivation and data for year 1999 (n = 5,365) were used for validation. Visits were assigned to clusters based on the recorded complaints; clusters were combined to ensure adequate numbers for analysis (minimum n = 20), and the clusters were reviewed for clinical sensibility. Resource utilization was categorized in three levels: routine (examination only), ED treatment (tests or therapy in the ED but not admitted), and admission. Area under the receiver-operating characteristic (ROC) curve (AUC) was used to demonstrate the discriminative ability of the clusters in predicting resource use. Results: There were 463 unique complaints in the derivation database; 95 (20%) had a single associated visit. Fifty-two clusters were generated; only 2.4% of complaints were classified as other. The eight most common clusters encompassed 52% of the visits. The top five were fever (11%), extremity pain/injury, vomiting, cough, and trauma (unspecified). Complaint clusters were associated with actual resource utilization: for routine care, the AUC was 0.73 (0.74 in the validation set), and for admission, the AUC was 0.77 (0.74 in the validation set). Both resource utilization and triage classification increased with increased expert severity ranking (test for trend, p < 0.001). Conclusions: The proposed Pediatric Emergency Reason for Visit Cluster (PERC) system is a comprehensive yet parsimonious, clinically sensible means of categorizing pediatric ED complaints. The PERC system's association with measures of acuity and resource utilization makes it a potentially useful tool in epidemiologic and health services research.