Novel symptom clusters predict disease impact and healthcare utilisation in inflammatory bowel disease: Prospective longitudinal follow-up study.

Abstract

Predicting adverse disease outcomes and high-volume users of healthcare amongst patients with inflammatory bowel disease (IBD) is difficult. The aim of this study is to use latent class analysis to create novel clusters of patients and to assess whether these predict outcomes during 6.5 years of longitudinal follow-up. Baseline demographic features, disease activity indices, anxiety, depression, and somatoform symptom-reporting scores were recorded for 692 adults. Faecal calprotectin (FC) was analysed at baseline in 348 (50.3%) patients (<250 mcg/g defined biochemical remission). Using baseline gastrointestinal and psychological symptoms, latent class analysis identified specific patient clusters. Rates of glucocorticosteroid prescription or flare, escalation, hospitalisation, or intestinal resection were compared between clusters using multivariate Cox regression. A three-cluster model was the optimum solution; 132 (19.1%) patients had below-average gastrointestinal and psychological symptoms (cluster 1), 352 (50.9%) had average levels of gastrointestinal and psychological symptoms (cluster 2), and 208 (30.1%) had the highest levels of both gastrointestinal and psychological symptoms (cluster 3). Compared with cluster 1, cluster 3 had significantly increased risk of flare or glucocorticosteroid prescription (hazard ratio (HR): 2.13; 95% confidence interval (CI): 1.46-3.10), escalation (HR: 1.92; 95% CI: 1.34-2.76), a composite of escalation, hospitalisation, or intestinal resection (HR: 2.05; 95% CI: 1.45-2.88), or any of the endpoints of interest (HR: 2.06; 95% CI: 1.45-2.93). Healthcare utilisation was highest in cluster 3. Novel model-based clusters identify patients with IBD at higher risk of adverse disease outcomes who are high-volume users of healthcare.