Symptom-based clusters in patients with advanced chronic organ failure identify different trajectories of symptom variations.

ABSTRACT

BACKGROUND: Healthcare needs are complex and heterogeneous in advanced chronic organ failure. However, based on symptom clusters, groups of patients with similar quality of life, care dependency and life-sustaining treatment preferences can be identified. AIMS: To evaluate the stability of symptom-based clusters over time, and whether and to what extent the clusters are able to predict patients' 2-year survival and hospitalization rates. METHODS: This is a secondary analysis of a longitudinal observational study including 95 outpatients with chronic obstructive pulmonary disease (COPD) GOLD stage III-IV, 80 outpatients with chronic heart failure (CHF) NYHA stage III-IV and 80 outpatients with chronic renal failure (CRF) requiring dialysis. Patients were clustered into three groups applying K-means algorithm on baseline symptoms' severity and were then longitudinally evaluated. 2-year survival and hospital admissions during 1 year were estimated using Kaplan-Meier curves and Cox models. 1-year tendencies in symptom variation, using mixed linear models, and clusters comparison over time were performed. RESULTS: The three clusters were unable to predict patients' survival and hospital admissions. Noteworthy, they show different trajectories of symptom variation, with Cluster 1 patients experiencing a worsening of symptoms, associated with an increased care dependency, and Cluster 2 and Cluster 3 patients being stable or having a relief in some symptoms. Although Cluster 1 is becoming more similar to Cluster 2, the three clusters preserve the overall characteristics and differences. DISCUSSION: Symptom-based clusters might help to identify patients with different trajectories of symptom variations. CONCLUSION: Symptom clusters do not predict survival and hospital admissions and are stable over time.