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.
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