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08-12-2017 | Uitgave 2/2018

Quality of Life Research 2/2018

Clustering based on unsupervised binary trees to define subgroups of cancer patients according to symptom severity in cancer

Tijdschrift:
Quality of Life Research > Uitgave 2/2018
Auteurs:
Pierre Michel, Zeinab Hamidou, Karine Baumstarck, Badih Ghattas, Noémie Resseguier, Olivier Chinot, Fabrice Barlesi, Sébastien Salas, Laurent Boyer, Pascal Auquier

Abstract

Background

Studies have suggested that clinicians do not feel comfortable with the interpretation of symptom severity, functional status, and quality of life (QoL). Implementation strategies of these types of measurements in clinical practice imply that consensual norms and guidelines regarding data interpretation are available. The aim of this study was to define subgroups of patients according to the levels of symptom severity using a method of interpretable clustering that uses unsupervised binary trees.

Methods

The patients were classified using a top-down hierarchical method: Clustering using Unsupervised Binary Trees (CUBT). We considered a three-group structure: “high”, “moderate”, and “low” level of symptom severity. The clustering tree was based on three stages using the 9-symptom scale scores of the EORTC QLQ-C30: a maximal tree was first developed by applying a recursive partitioning algorithm; the tree was then pruned using a criterion of minimal dissimilarity; finally, the most similar clusters were joined together. Inter-cluster comparisons were performed to test the sample partition and QoL data.

Results

Two hundred thirty-five patients with different types of cancer were included. The three-cluster structure classified 143 patients with “low”, 46 with “moderate”, and 46 with “high” levels of symptom severity. This partition was explained by cut-off values on Fatigue and Appetite Loss scores. The three clusters consistently differentiated patients based on the clinical characteristics and QoL outcomes.

Conclusion

Our study suggests that CUBT is relevant to define the levels of symptom severity in cancer. This finding may have important implications for helping clinicians to interpret symptom profiles in clinical practice, to identify individuals at risk for poorer outcomes and implement targeted interventions.

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